Layers¶

Deep learning models are often said to be made up of “layers”. Intuitively, a “layer” is a function which transforms some tensor into another tensor. DeepChem maintains an extensive collection of layers which perform various useful scientific transformations. For now, most layers are Keras only but over time we expect this support to expand to other types of models and layers.

class deepchem.models.layers.InteratomicL2Distances(N_atoms: int, M_nbrs: int, ndim: int, **kwargs)[source]

Compute (squared) L2 Distances between atoms given neighbors.

This class computes pairwise distances between its inputs.

Examples

>>> import numpy as np
>>> import deepchem as dc
>>> atoms = 5
>>> neighbors = 2
>>> coords = np.random.rand(atoms, 3)
>>> neighbor_list = np.random.randint(0, atoms, size=(atoms, neighbors))
>>> layer = InteratomicL2Distances(atoms, neighbors, 3)
>>> result = np.array(layer([coords, neighbor_list]))
>>> result.shape
(5, 2)

__init__(N_atoms: int, M_nbrs: int, ndim: int, **kwargs)[source]

Constructor for this layer.

Parameters: N_atoms (int) – Number of atoms in the system total. M_nbrs (int) – Number of neighbors to consider when computing distances. n_dim (int) – Number of descriptors for each atom.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

Invokes this layer.

Parameters: inputs (list) – Should be of form inputs=[coords, nbr_list] where coords is a tensor of shape (None, N, 3) and nbr_list is a list. Tensor of shape (N_atoms, M_nbrs) with interatomic distances.
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config() → Dict[KT, VT][source]

Returns config dictionary for this layer.

get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.GraphConv(out_channel: int, min_deg: int = 0, max_deg: int = 10, activation_fn: Callable = None, **kwargs)[source]

Graph Convolutional Layers

This layer implements the graph convolution introduced in [1]_. The graph convolution combines per-node feature vectures in a nonlinear fashion with the feature vectors for neighboring nodes. This “blends” information in local neighborhoods of a graph.

References

 [1] Duvenaud, David K., et al. “Convolutional networks on graphs for learning molecular fingerprints.” Advances in neural information processing systems. 2015. https://arxiv.org/abs/1509.09292
__init__(out_channel: int, min_deg: int = 0, max_deg: int = 10, activation_fn: Callable = None, **kwargs)[source]

Initialize a graph convolutional layer.

Parameters: out_channel (int) – The number of output channels per graph node. min_deg (int, optional (default 0)) – The minimum allowed degree for each graph node. max_deg (int, optional (default 10)) – The maximum allowed degree for each graph node. Note that this is set to 10 to handle complex molecules (some organometallic compounds have strange structures). If you’re using this for non-molecular applications, you may need to set this much higher depending on your dataset. activation_fn (function) – A nonlinear activation function to apply. If you’re not sure, tf.nn.relu is probably a good default for your application.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

This is where the layer’s logic lives.

Parameters: inputs – Input tensor, or list/tuple of input tensors. **kwargs – Additional keyword arguments. A tensor or list/tuple of tensors.
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
sum_neigh(atoms, deg_adj_lists)[source]

Store the summed atoms by degree

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.GraphPool(min_degree=0, max_degree=10, **kwargs)[source]

A GraphPool gathers data from local neighborhoods of a graph.

This layer does a max-pooling over the feature vectors of atoms in a neighborhood. You can think of this layer as analogous to a max-pooling layer for 2D convolutions but which operates on graphs instead. This technique is described in [1]_.

References

 [1] Duvenaud, David K., et al. “Convolutional networks on graphs for

learning molecular fingerprints.” Advances in neural information processing systems. 2015. https://arxiv.org/abs/1509.09292

__init__(min_degree=0, max_degree=10, **kwargs)[source]

Initialize this layer

Parameters: min_deg (int, optional (default 0)) – The minimum allowed degree for each graph node. max_deg (int, optional (default 10)) – The maximum allowed degree for each graph node. Note that this is set to 10 to handle complex molecules (some organometallic compounds have strange structures). If you’re using this for non-molecular applications, you may need to set this much higher depending on your dataset.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

This is where the layer’s logic lives.

Parameters: inputs – Input tensor, or list/tuple of input tensors. **kwargs – Additional keyword arguments. A tensor or list/tuple of tensors.
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.GraphGather(batch_size, activation_fn=None, **kwargs)[source]

A GraphGather layer pools node-level feature vectors to create a graph feature vector.

Many graph convolutional networks manipulate feature vectors per graph-node. For a molecule for example, each node might represent an atom, and the network would manipulate atomic feature vectors that summarize the local chemistry of the atom. However, at the end of the application, we will likely want to work with a molecule level feature representation. The GraphGather layer creates a graph level feature vector by combining all the node-level feature vectors.

One subtlety about this layer is that it depends on the batch_size. This is done for internal implementation reasons. The GraphConv, and GraphPool layers pool all nodes from all graphs in a batch that’s being processed. The GraphGather reassembles these jumbled node feature vectors into per-graph feature vectors.

References

 [1] Duvenaud, David K., et al. “Convolutional networks on graphs for

learning molecular fingerprints.” Advances in neural information processing systems. 2015. https://arxiv.org/abs/1509.09292

__init__(batch_size, activation_fn=None, **kwargs)[source]

Initialize this layer.

Parameters: batch_size (int) – The batch size for this layer. Note that the layer’s behavior changes depending on the batch size. activation_fn (function) – A nonlinear activation function to apply. If you’re not sure, tf.nn.relu is probably a good default for your application.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

Invoking this layer.

Parameters: inputs (list) – This list should consist of inputs = [atom_features, deg_slice, membership, deg_adj_list placeholders…]. These are all tensors that are created/process by GraphConv and GraphPool
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.LSTMStep(output_dim, input_dim, init_fn='glorot_uniform', inner_init_fn='orthogonal', activation_fn='tanh', inner_activation_fn='hard_sigmoid', **kwargs)[source]

Layer that performs a single step LSTM update.

This layer performs a single step LSTM update. Note that it is not a full LSTM recurrent network. The LSTMStep layer is useful as a primitive for designing layers such as the AttnLSTMEmbedding or the IterRefLSTMEmbedding below.

__init__(output_dim, input_dim, init_fn='glorot_uniform', inner_init_fn='orthogonal', activation_fn='tanh', inner_activation_fn='hard_sigmoid', **kwargs)[source]
Parameters: output_dim (int) – Dimensionality of output vectors. input_dim (int) – Dimensionality of input vectors. init_fn (str) – TensorFlow nitialization to use for W. inner_init_fn (str) – TensorFlow initialization to use for U. activation_fn (str) – TensorFlow activation to use for output. inner_activation_fn (str) – TensorFlow activation to use for inner steps.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Constructs learnable weights for this layer.

call(inputs)[source]

Execute this layer on input tensors.

Parameters: inputs (list) – List of three tensors (x, h_tm1, c_tm1). h_tm1 means “h, t-1”. Returns h, [h, c] list
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.AttnLSTMEmbedding(n_test, n_support, n_feat, max_depth, **kwargs)[source]

Implements AttnLSTM as in matching networks paper.

The AttnLSTM embedding adjusts two sets of vectors, the “test” and “support” sets. The “support” consists of a set of evidence vectors. Think of these as the small training set for low-data machine learning. The “test” consists of the queries we wish to answer with the small amounts of available data. The AttnLSTMEmbdding allows us to modify the embedding of the “test” set depending on the contents of the “support”. The AttnLSTMEmbedding is thus a type of learnable metric that allows a network to modify its internal notion of distance.

See references [1]_ [2] for more details.

References

 [1] Vinyals, Oriol, et al. “Matching networks for one shot learning.” Advances in neural information processing systems. 2016.
 [2] Vinyals, Oriol, Samy Bengio, and Manjunath Kudlur. “Order matters: Sequence to sequence for sets.” arXiv preprint arXiv:1511.06391 (2015).
__init__(n_test, n_support, n_feat, max_depth, **kwargs)[source]
Parameters: n_support (int) – Size of support set. n_test (int) – Size of test set. n_feat (int) – Number of features per atom max_depth (int) – Number of “processing steps” used by sequence-to-sequence for sets model.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

Execute this layer on input tensors.

Parameters: inputs (list) – List of two tensors (X, Xp). X should be of shape (n_test, n_feat) and Xp should be of shape (n_support, n_feat) where n_test is the size of the test set, n_support that of the support set, and n_feat is the number of per-atom features. Returns two tensors of same shape as input. Namely the output shape will be [(n_test, n_feat), (n_support, n_feat)] list
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.IterRefLSTMEmbedding(n_test, n_support, n_feat, max_depth, **kwargs)[source]

Implements the Iterative Refinement LSTM.

Much like AttnLSTMEmbedding, the IterRefLSTMEmbedding is another type of learnable metric which adjusts “test” and “support.” Recall that “support” is the small amount of data available in a low data machine learning problem, and that “test” is the query. The AttnLSTMEmbedding only modifies the “test” based on the contents of the support. However, the IterRefLSTM modifies both the “support” and “test” based on each other. This allows the learnable metric to be more malleable than that from AttnLSTMEmbeding.

__init__(n_test, n_support, n_feat, max_depth, **kwargs)[source]

Unlike the AttnLSTM model which only modifies the test vectors additively, this model allows for an additive update to be performed to both test and support using information from each other.

Parameters: n_support (int) – Size of support set. n_test (int) – Size of test set. n_feat (int) – Number of input atom features max_depth (int) – Number of LSTM Embedding layers.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

Execute this layer on input tensors.

Parameters: inputs (list) – List of two tensors (X, Xp). X should be of shape (n_test, n_feat) and Xp should be of shape (n_support, n_feat) where n_test is the size of the test set, n_support that of the support set, and n_feat is the number of per-atom features. Returns two tensors of same shape as input. Namely the output shape will be [(n_test, n_feat), (n_support, n_feat)]
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.SwitchedDropout(rate, **kwargs)[source]

Apply dropout based on an input.

This is required for uncertainty prediction. The standard Keras Dropout layer only performs dropout during training, but we sometimes need to do it during prediction. The second input to this layer should be a scalar equal to 0 or 1, indicating whether to perform dropout.

__init__(rate, **kwargs)[source]
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

This is where the layer’s logic lives.

Parameters: inputs – Input tensor, or list/tuple of input tensors. **kwargs – Additional keyword arguments. A tensor or list/tuple of tensors.
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.WeightedLinearCombo(std=0.3, **kwargs)[source]

Computes a weighted linear combination of input layers, with the weights defined by trainable variables.

__init__(std=0.3, **kwargs)[source]

Initialize this layer.

Parameters: std (float, optional (default 0.3)) – The standard deviation to use when randomly initializing weights.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

This is where the layer’s logic lives.

Parameters: inputs – Input tensor, or list/tuple of input tensors. **kwargs – Additional keyword arguments. A tensor or list/tuple of tensors.
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.CombineMeanStd(training_only=False, noise_epsilon=1.0, **kwargs)[source]

Generate Gaussian nose.

__init__(training_only=False, noise_epsilon=1.0, **kwargs)[source]

Create a CombineMeanStd layer.

This layer should have two inputs with the same shape, and its output also has the same shape. Each element of the output is a Gaussian distributed random number whose mean is the corresponding element of the first input, and whose standard deviation is the corresponding element of the second input.

Parameters: training_only (bool) – if True, noise is only generated during training. During prediction, the output is simply equal to the first input (that is, the mean of the distribution used during training). noise_epsilon (float) – The noise is scaled by this factor
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs, training=True)[source]

This is where the layer’s logic lives.

Parameters: inputs – Input tensor, or list/tuple of input tensors. **kwargs – Additional keyword arguments. A tensor or list/tuple of tensors.
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.Stack(axis=1, **kwargs)[source]

Stack the inputs along a new axis.

__init__(axis=1, **kwargs)[source]
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

This is where the layer’s logic lives.

Parameters: inputs – Input tensor, or list/tuple of input tensors. **kwargs – Additional keyword arguments. A tensor or list/tuple of tensors.
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.VinaFreeEnergy(N_atoms, M_nbrs, ndim, nbr_cutoff, start, stop, stddev=0.3, Nrot=1, **kwargs)[source]

Computes free-energy as defined by Autodock Vina.

TODO(rbharath): Make this layer support batching.

__init__(N_atoms, M_nbrs, ndim, nbr_cutoff, start, stop, stddev=0.3, Nrot=1, **kwargs)[source]
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]
Parameters: X (tf.Tensor of shape (N, d)) – Coordinates/features. Z (tf.Tensor of shape (N)) – Atomic numbers of neighbor atoms. layer – The free energy of each complex in batch tf.Tensor of shape (B)
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
gaussian_first(d)[source]

Computes Autodock Vina’s first Gaussian interaction term.

gaussian_second(d)[source]

Computes Autodock Vina’s second Gaussian interaction term.

get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
hydrogen_bond(d)[source]

Computes Autodock Vina’s hydrogen bond interaction term.

hydrophobic(d)[source]

Computes Autodock Vina’s hydrophobic interaction term.

inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
nonlinearity(c, w)[source]

Computes non-linearity used in Vina.

outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
repulsion(d)[source]

Computes Autodock Vina’s repulsion interaction term.

set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.NeighborList(N_atoms, M_nbrs, ndim, nbr_cutoff, start, stop, **kwargs)[source]

Computes a neighbor-list in Tensorflow.

Neighbor-lists (also called Verlet Lists) are a tool for grouping atoms which are close to each other spatially. This layer computes a Neighbor List from a provided tensor of atomic coordinates. You can think of this as a general “k-means” layer, but optimized for the case k==3.

TODO(rbharath): Make this layer support batching.

__init__(N_atoms, M_nbrs, ndim, nbr_cutoff, start, stop, **kwargs)[source]
Parameters: N_atoms (int) – Maximum number of atoms this layer will neighbor-list. M_nbrs (int) – Maximum number of spatial neighbors possible for atom. ndim (int) – Dimensionality of space atoms live in. (Typically 3D, but sometimes will want to use higher dimensional descriptors for atoms). nbr_cutoff (float) – Length in Angstroms (?) at which atom boxes are gridded.
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]

This is where the layer’s logic lives.

Parameters: inputs – Input tensor, or list/tuple of input tensors. **kwargs – Additional keyword arguments. A tensor or list/tuple of tensors.
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_nbr_list(coords)[source]

Get closest neighbors for atoms.

Needs to handle padding for atoms with no neighbors.

Parameters: coords (tf.Tensor) – Shape (N_atoms, ndim) nbr_list – Shape (N_atoms, M_nbrs) of atom indices tf.Tensor
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
get_atoms_in_nbrs(coords, cells)[source]

Get the atoms in neighboring cells for each cells.

Returns: atoms_in_nbrs = (N_atoms, n_nbr_cells, M_nbrs)
get_cells()[source]

Returns the locations of all grid points in box.

Suppose start is -10 Angstrom, stop is 10 Angstrom, nbr_cutoff is 1. Then would return a list of length 20^3 whose entries would be [(-10, -10, -10), (-10, -10, -9), …, (9, 9, 9)]

Returns: cells – (n_cells, ndim) shape. tf.Tensor
get_cells_for_atoms(coords, cells)[source]

Compute the cells each atom belongs to.

Parameters: coords (tf.Tensor) – Shape (N_atoms, ndim) cells (tf.Tensor) – (n_cells, ndim) shape. cells_for_atoms – Shape (N_atoms, 1) tf.Tensor
get_closest_atoms(coords, cells)[source]

For each cell, find M_nbrs closest atoms.

Let N_atoms be the number of atoms.

Parameters: coords (tf.Tensor) – (N_atoms, ndim) shape. cells (tf.Tensor) – (n_cells, ndim) shape. closest_inds – Of shape (n_cells, M_nbrs) tf.Tensor
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_neighbor_cells(cells)[source]

Compute neighbors of cells in grid.

# TODO(rbharath): Do we need to handle periodic boundary conditions properly here? # TODO(rbharath): This doesn’t handle boundaries well. We hard-code # looking for n_nbr_cells neighbors, which isn’t right for boundary cells in # the cube.

Parameters: cells (tf.Tensor) – (n_cells, ndim) shape. nbr_cells – (n_cells, n_nbr_cells) tf.Tensor
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns: A list of variables.
weights

Returns the list of all layer variables/weights.

Returns: A list of variables.
classmethod with_name_scope(method)[source]

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)


Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap. The original method wrapped such that it enters the module’s name scope.
class deepchem.models.layers.AtomicConvolution(atom_types=None, radial_params=[], boxsize=None, **kwargs)[source]

Implements the atomic convolutional transform introduced in

Gomes, Joseph, et al. “Atomic convolutional networks for predicting protein-ligand binding affinity.” arXiv preprint arXiv:1703.10603 (2017).

At a high level, this transform performs a graph convolution on the nearest neighbors graph in 3D space.

__init__(atom_types=None, radial_params=[], boxsize=None, **kwargs)[source]

Atomic convolution layer

N = max_num_atoms, M = max_num_neighbors, B = batch_size, d = num_features l = num_radial_filters * num_atom_types

Parameters: atom_types (list or None) – Of length a, where a is number of atom types for filtering. radial_params (list) – Of length l, where l is number of radial filters learned. boxsize (float or None) – Simulation box length [Angstrom].
activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)[source]

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

python class MyLayer(tf.keras.layers.Layer):

def call(inputs, self):

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Inputs. These losses become part of the model’s topology and are tracked in get_config.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) 

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(x.kernel)) 

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Parameters: losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. inputs – Ignored when executing eagerly. If anything other than None is passed, it signals the losses are conditional on some of the layer’s inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)[source]

Adds metric tensor to the layer.

Parameters: value – Metric tensor. aggregation – Sample-wise metric reduction function. If aggregation=None, it indicates that the metric tensor provided has been aggregated already. eg, bin_acc = BinaryAccuracy(name=’acc’) followed by model.add_metric(bin_acc(y_true, y_pred)). If aggregation=’mean’, the given metric tensor will be sample-wise reduced using mean function. eg, model.add_metric(tf.reduce_sum(outputs), name=’output_mean’, aggregation=’mean’). name – String metric name. ValueError – If aggregation is anything other than None or mean.
add_update(updates, inputs=None)[source]

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode. inputs – Deprecated, will be automatically inferred.
add_variable(*args, **kwargs)[source]

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)[source]

Adds a new variable to the layer.

Parameters: name – Variable name. shape – Variable shape. Defaults to scalar if unspecified. dtype – The type of the variable. Defaults to self.dtype or float32. initializer – Initializer instance (callable). regularizer – Regularizer instance (callable). trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ. constraint – Constraint instance (callable). partitioner – Partitioner to be passed to the Trackable API. use_resource – Whether to use ResourceVariable. synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True. aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation. **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device. The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned. RuntimeError – If called with partitioned variable regularization and eager execution is enabled. ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.
apply(inputs, *args, **kwargs)[source]

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters: inputs – Input tensor(s). *args – additional positional arguments to be passed to self.call. **kwargs – additional keyword arguments to be passed to self.call. Output tensor(s).
build(input_shape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).
call(inputs)[source]
Parameters: X (tf.Tensor of shape (B, N, d)) – Coordinates/features. Nbrs (tf.Tensor of shape (B, N, M)) – Neighbor list. Nbrs_Z (tf.Tensor of shape (B, N, M)) – Atomic numbers of neighbor atoms. layer – A new tensor representing the output of the atomic conv layer tf.Tensor of shape (B, N, l)
compute_mask(inputs, mask=None)[source]

Parameters: inputs – Tensor or list of tensors. mask – Tensor or list of tensors. None or a tensor (or list of tensors, one per output tensor of the layer).
compute_output_shape(input_shape)[source]

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. An input shape tuple.
compute_output_signature(input_signature)[source]

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters: input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer. Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input. TypeError – If input_signature contains a non-TensorSpec object.
count_params()[source]

Count the total number of scalars composing the weights.

Returns: An integer count. ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).
distance_matrix(D)[source]

Calcuates the distance matrix from the distance tensor

B = batch_size, N = max_num_atoms, M = max_num_neighbors, d = num_features

Parameters: D (tf.Tensor of shape (B, N, M, d)) – Distance tensor. R – Distance matrix. tf.Tensor of shape (B, N, M)
distance_tensor(X, Nbrs, boxsize, B, N, M, d)[source]

Calculates distance tensor for batch of molecules.

B = batch_size, N = max_num_atoms, M = max_num_neighbors, d = num_features

Parameters: X (tf.Tensor of shape (B, N, d)) – Coordinates/features tensor. Nbrs (tf.Tensor of shape (B, N, M)) – Neighbor list tensor. boxsize (float or None) – Simulation box length [Angstrom]. D – Coordinates/features distance tensor. tf.Tensor of shape (B, N, M, d)
dtype

Dtype used by the weights of the layer, set in the constructor.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)[source]

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config. A layer instance.
gaussian_distance_matrix(R, rs, e)[source]

Calculates gaussian distance matrix.

B = batch_size, N = max_num_atoms, M = max_num_neighbors

Parameters: [B, N, M] (R) – Distance matrix. rs (tf.Variable) – Gaussian distance matrix mean. e (tf.Variable) – Gaussian distance matrix width (e = .5/std**2). retval [B, N, M] – Gaussian distance matrix. tf.Tensor
get_config()[source]

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns: Python dictionary.
get_input_at(node_index)[source]

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_input_mask_at(node_index)[source]

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)[source]

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple inputs). RuntimeError – If called in Eager mode.
get_losses_for(inputs)[source]

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of loss tensors of the layer that depend on inputs.
get_output_at(node_index)[source]

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A tensor (or list of tensors if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_output_mask_at(node_index)[source]

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)[source]

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called. A shape tuple (or list of shape tuples if the layer has multiple outputs). RuntimeError – If called in Eager mode.
get_updates_for(inputs)[source]

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors. List of update ops of the layer that depend on inputs.
get_weights()[source]

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.
inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns: Input tensor or list of input tensors. RuntimeError – If called in Eager mode. AttributeError – If no inbound nodes are found.
input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Input mask tensor (potentially None) or list of input mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor). AttributeError – if the layer has no defined input_shape. RuntimeError – if called in Eager mode.
input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

python self.input_spec = tf.keras.layers.InputSpec(ndim=4) 

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

 ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] 

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.
losses

Losses which are associated with this Layer.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Returns: A list of tensors.
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns: A list of non-trainable variables.
outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns: Output tensor or list of output tensors. AttributeError – if the layer is connected to more than one incoming layers. RuntimeError – if called in Eager mode.
output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns: Output mask tensor (potentially None) or list of output mask tensors. AttributeError – if the layer is connected to more than one incoming layers.
output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor). AttributeError – if the layer has no defined output shape. RuntimeError – if called in Eager mode.
radial_cutoff(R, rc)[source]

B = batch_size, N = max_num_atoms, M = max_num_neighbors

Parameters: [B, N, M] (R) – Distance matrix. rc (tf.Variable) – Interaction cutoff [Angstrom]. FC [B, N, M] – Radial cutoff matrix. tf.Tensor
radial_symmetry_function(R, rc, rs, e)[source]

B = batch_size, N = max_num_atoms, M = max_num_neighbors, d = num_filters

Parameters: R (tf.Tensor of shape (B, N, M)) – Distance matrix. rc (float) – Interaction cutoff [Angstrom]. rs (float) – Gaussian distance matrix mean. e (float) – Gaussian distance matrix width. retval – Radial symmetry function (before summation) tf.Tensor of shape (B, N, M)
set_weights(weights)[source]

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights). ValueError – If the provided weights list does not match the layer’s specifications.
submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.
trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.
variables

Returns the list of all layer variables/weights.

Alias of self.weights.