phygnn.layers.custom_layers.FlexiblePadding

class FlexiblePadding(*args, **kwargs)[source]

Bases: Layer

Class to perform padding on tensors

Parameters:

paddings (int array) – Integer array with shape [n,2] where n is the rank of the tensor and elements give the number of leading and trailing pads
mode (str) – tf.pad() / np.pad() padding mode. Can be REFLECT, CONSTANT, or SYMMETRIC
option (str) – Option for TensorFlow padding (“tf”) or numpy (“np”). Default is tf for tensorflow training. We have observed silent failures of tf.pad() with larger array sizes, so “np” might be preferable at inference time on large chunks, but it is much slower when it has to convert tensors to numpy arrays.

Methods

`add_loss`(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
`add_metric`(value[, name])	Adds metric tensor to the layer.
`add_update`(updates)	Add update op(s), potentially dependent on layer inputs.
`add_variable`(args, *kwargs)	Deprecated, do NOT use! Alias for add_weight.
`add_weight`([name, shape, dtype, ...])	Adds a new variable to the layer.
`build`(input_shape)	Creates the variables of the layer (for subclass implementers).
`build_from_config`(config)	Builds the layer's states with the supplied config dict.
`call`(x)	Calls the padding routine
`compute_mask`(inputs[, mask])	Computes an output mask tensor.
`compute_output_shape`(input_shape)	Computes output shape after padding
`compute_output_signature`(input_signature)	Compute the output tensor signature of the layer based on the inputs.
`count_params`()	Count the total number of scalars composing the weights.
`finalize_state`()	Finalizes the layers state after updating layer weights.
`from_config`(config)	Creates a layer from its config.
`get_build_config`()	Returns a dictionary with the layer's input shape.
`get_config`()	Returns the config of the layer.
`get_input_at`(node_index)	Retrieves the input tensor(s) of a layer at a given node.
`get_input_mask_at`(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
`get_input_shape_at`(node_index)	Retrieves the input shape(s) of a layer at a given node.
`get_output_at`(node_index)	Retrieves the output tensor(s) of a layer at a given node.
`get_output_mask_at`(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
`get_output_shape_at`(node_index)	Retrieves the output shape(s) of a layer at a given node.
`get_weights`()	Returns the current weights of the layer, as NumPy arrays.
`load_own_variables`(store)	Loads the state of the layer.
`save_own_variables`(store)	Saves the state of the layer.
`set_weights`(weights)	Sets the weights of the layer, from NumPy arrays.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Attributes

`activity_regularizer`	Optional regularizer function for the output of this layer.
`compute_dtype`	The dtype of the layer's computations.
`dtype`	The dtype of the layer weights.
`dtype_policy`	The dtype policy associated with this layer.
`dynamic`	Whether the layer is dynamic (eager-only); set in the constructor.
`inbound_nodes`	Return Functional API nodes upstream of this layer.
`input`	Retrieves the input tensor(s) of a layer.
`input_mask`	Retrieves the input mask tensor(s) of a layer.
`input_shape`	Retrieves the input shape(s) of a layer.
`input_spec`	InputSpec instance(s) describing the input format for this layer.
`losses`	List of losses added using the add_loss() API.
`metrics`	List of metrics added using the add_metric() API.
`name`	Name of the layer (string), set in the constructor.
`name_scope`	Returns a tf.name_scope instance for this class.
`non_trainable_variables`	Sequence of non-trainable variables owned by this module and its submodules.
`non_trainable_weights`	List of all non-trainable weights tracked by this layer.
`outbound_nodes`	Return Functional API nodes downstream of this layer.
`output`	Retrieves the output tensor(s) of a layer.
`output_mask`	Retrieves the output mask tensor(s) of a layer.
`output_shape`	Retrieves the output shape(s) of a layer.
`stateful`
`submodules`	Sequence of all sub-modules.
`supports_masking`	Whether this layer supports computing a mask using compute_mask.
`trainable`
`trainable_variables`	Sequence of trainable variables owned by this module and its submodules.
`trainable_weights`	List of all trainable weights tracked by this layer.
`updates`
`variable_dtype`	Alias of Layer.dtype, the dtype of the weights.
`variables`	Returns the list of all layer variables/weights.
`weights`	Returns the list of all layer variables/weights.

compute_output_shape(input_shape)[source]

Computes output shape after padding

Parameters:: input_shape (tuple) – shape of input tensor
Returns:: output_shape (tf.TensorShape) – shape of padded tensor

call(x)[source]

Calls the padding routine

Parameters:: x (tf.Tensor) – tensor on which to perform padding
Returns:: x (tf.Tensor) – padded tensor with shape given by compute_output_shape

__call__(*args, **kwargs)

Wraps call, applying pre- and post-processing steps.

Args:

*args: Positional arguments to be passed to self.call. **kwargs: Keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

Note:

The following optional keyword arguments are reserved for specific uses: * training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.
- mask: Boolean input mask.
If the layer’s call method takes a mask argument (as some Keras layers do), its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.
If the layer is not built, the method will call build.

Raises:

ValueError: if the layer’s call method returns None (an invalid: value).
RuntimeError: if super().__init__() was not called in the: constructor.

property activity_regularizer: Optional regularizer function for the output of this layer.

add_loss(losses, **kwargs)

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(self, inputs):
self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs

```

The same code works in distributed training: the input to add_loss() is treated like a regularization loss and averaged across replicas by the training loop (both built-in Model.fit() and compliant custom training loops).

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

Args:

losses: Loss tensor, or list/tuple of tensors. Rather than tensors,: losses may also be zero-argument callables which create a loss tensor.

**kwargs: Used for backwards compatibility only.

add_metric(value, name=None, **kwargs)

Adds metric tensor to the layer.

This method can be used inside the call() method of a subclassed layer or model.

```python class MyMetricLayer(tf.keras.layers.Layer):

def __init__(self):
super(MyMetricLayer, self).__init__(name=’my_metric_layer’) self.mean = tf.keras.metrics.Mean(name=’metric_1’)

def call(self, inputs):
self.add_metric(self.mean(inputs)) self.add_metric(tf.reduce_sum(inputs), name=’metric_2’) return inputs

```

This method can also be called directly on a Functional Model during construction. In this case, any tensor passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These metrics become part of the model’s topology and are tracked when you save the model via `save().

`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) model.add_metric(math_ops.reduce_sum(x), name='metric_1') `

Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model’s inputs.

`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) model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1') `

Args:: value: Metric tensor. name: String metric name. **kwargs: Additional keyword arguments for backward compatibility.

Accepted values: aggregation - When the value tensor provided is not the result of calling a keras.Metric instance, it will be aggregated by default using a keras.Metric.Mean.

add_update(updates)

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

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.

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).

Args:

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.

add_variable(*args, **kwargs): Deprecated, do NOT use! Alias for add_weight.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, use_resource=None, synchronization=VariableSynchronization.AUTO, aggregation=VariableAggregationV2.NONE, **kwargs)

Adds a new variable to the layer.

Args:

name: Variable name. shape: Variable shape. Defaults to scalar if unspecified. dtype: The type of the variable. Defaults to self.dtype. 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). use_resource: Whether to use a ResourceVariable or not.

See [this guide]( https://www.tensorflow.org/guide/migrate/tf1_vs_tf2#resourcevariables_instead_of_referencevariables)

for more information.

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.

Returns:

The variable created.

Raises:

ValueError: When giving unsupported dtype and no initializer or when: trainable has been set to True with synchronization set as ON_READ.

build(input_shape)

Creates the variables of the layer (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. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of: TensorShape if the layer expects a list of inputs (one instance per input).

build_from_config(config)

Builds the layer’s states with the supplied config dict.

By default, this method calls the build(config[“input_shape”]) method, which creates weights based on the layer’s input shape in the supplied config. If your config contains other information needed to load the layer’s state, you should override this method.

Args:: config: Dict containing the input shape associated with this layer.

property compute_dtype

The dtype of the layer’s computations.

This is equivalent to Layer.dtype_policy.compute_dtype. Unless mixed precision is used, this is the same as Layer.dtype, the dtype of the weights.

Layers automatically cast their inputs to the compute dtype, which causes computations and the output to be in the compute dtype as well. This is done by the base Layer class in Layer.__call__, so you do not have to insert these casts if implementing your own layer.

Layers often perform certain internal computations in higher precision when compute_dtype is float16 or bfloat16 for numeric stability. The output will still typically be float16 or bfloat16 in such cases.

Returns:: The layer’s compute dtype.

compute_mask(inputs, mask=None)

Computes an output mask tensor.

Args:

inputs: Tensor or list of tensors. mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors,: one per output tensor of the layer).

compute_output_signature(input_signature)

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.

Args:

input_signature: Single TensorSpec or nested structure of TensorSpec: objects, describing a candidate input for the layer.

Returns:

Single TensorSpec or nested structure of TensorSpec objects,: describing how the layer would transform the provided input.

Raises:

TypeError: If input_signature contains a non-TensorSpec object.

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

ValueError: if the layer isn’t yet built: (in which case its weights aren’t yet defined).

property dtype

The dtype of the layer weights.

This is equivalent to Layer.dtype_policy.variable_dtype. Unless mixed precision is used, this is the same as Layer.compute_dtype, the dtype of the layer’s computations.

property dtype_policy

The dtype policy associated with this layer.

This is an instance of a tf.keras.mixed_precision.Policy.

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

finalize_state()

Finalizes the layers state after updating layer weights.

This function can be subclassed in a layer and will be called after updating a layer weights. It can be overridden to finalize any additional layer state after a weight update.

This function will be called after weights of a layer have been restored from a loaded model.

classmethod from_config(config)

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).

Args:

config: A Python dictionary, typically the: output of get_config.

Returns:

A layer instance.

get_build_config()

Returns a dictionary with the layer’s input shape.

This method returns a config dict that can be used by build_from_config(config) to create all states (e.g. Variables and Lookup tables) needed by the layer.

By default, the config only contains the input shape that the layer was built with. If you’re writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.

Returns:: A dict containing the input shape associated with the layer.

get_config()

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).

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

Returns:: Python dictionary.

get_input_at(node_index)

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

Args:

node_index: Integer, index of the node: from which to retrieve the attribute. E.g. node_index=0 will correspond to the first input node of the layer.

Returns:

A tensor (or list of tensors if the layer has multiple inputs).

Raises:

RuntimeError: If called in Eager mode.

get_input_mask_at(node_index)

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

Args:

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.

Returns:

A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at(node_index)

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

Args:

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.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple inputs).

Raises:

RuntimeError: If called in Eager mode.

get_output_at(node_index)

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

Args:

node_index: Integer, index of the node: from which to retrieve the attribute. E.g. node_index=0 will correspond to the first output node of the layer.

Returns:

A tensor (or list of tensors if the layer has multiple outputs).

Raises:

RuntimeError: If called in Eager mode.

get_output_mask_at(node_index)

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

Args:

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.

Returns:

A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at(node_index)

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

Args:

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.

Returns:

A shape tuple (or list of shape tuples if the layer has multiple outputs).

Raises:

RuntimeError: If called in Eager mode.

get_weights()

Returns the current weights of the layer, as NumPy arrays.

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: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

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

Returns:: Weights values as a list of NumPy arrays.

property inbound_nodes: Return Functional API nodes upstream of this layer.

property 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.
Raises:: RuntimeError: If called in Eager mode. AttributeError: If no inbound nodes are found.

property 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.
Raises:: AttributeError: if the layer is connected to more than one incoming layers.

property 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).
Raises:: AttributeError: if the layer has no defined input_shape. RuntimeError: if called in Eager mode.

property 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

For more information, see tf.keras.layers.InputSpec.

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

load_own_variables(store)

Loads the state of the layer.

You can override this method to take full control of how the state of the layer is loaded upon calling keras.models.load_model().

Args:: store: Dict from which the state of the model will be loaded.

property losses

List of losses added using the add_loss() API.

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.

Examples:

>>> class MyLayer(tf.keras.layers.Layer):
...   def call(self, inputs):
...     self.add_loss(tf.abs(tf.reduce_mean(inputs)))
...     return inputs
>>> l = MyLayer()
>>> l(np.ones((10, 1)))
>>> l.losses
[1.0]

>>> 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.
>>> len(model.losses)
0
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
>>> len(model.losses)
1

>>> inputs = tf.keras.Input(shape=(10,))
>>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
>>> x = d(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Weight regularization.
>>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
>>> model.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]

Returns:: A list of tensors.

property metrics

List of metrics added using the add_metric() API.

Example:

>>> input = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2)
>>> output = d(input)
>>> d.add_metric(tf.reduce_max(output), name='max')
>>> d.add_metric(tf.reduce_min(output), name='min')
>>> [m.name for m in d.metrics]
['max', 'min']

Returns:: A list of Metric objects.

property name: Name of the layer (string), set in the constructor.

property name_scope: Returns a tf.name_scope instance for this class.

property non_trainable_variables

Sequence of non-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).

property 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.

property outbound_nodes: Return Functional API nodes downstream of this layer.

property 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.

Raises:

AttributeError: if the layer is connected to more than one incoming: layers.

RuntimeError: if called in Eager mode.

property 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.
Raises:: AttributeError: if the layer is connected to more than one incoming layers.

property 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).
Raises:: AttributeError: if the layer has no defined output shape. RuntimeError: if called in Eager mode.

save_own_variables(store)

Saves the state of the layer.

You can override this method to take full control of how the state of the layer is saved upon calling model.save().

Args:: store: Dict where the state of the model will be saved.

set_weights(weights)

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: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

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

Args:

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).

Raises:

ValueError: If the provided weights list does not match the: layer’s specifications.

property 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.

property supports_masking: Whether this layer supports computing a mask using compute_mask.

property 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).

property 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.

property variable_dtype: Alias of Layer.dtype, the dtype of the weights.

property variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns:: A list of variables.

property weights

Returns the list of all layer variables/weights.

Returns:: A list of variables.

classmethod with_name_scope(method)

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.Variable`s and `tf.Tensor`s 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)>

Args:: method: The method to wrap.
Returns:: The original method wrapped such that it enters the module’s name scope.