chemicalchecker.tool.adanet.dnn_extend_generator.NanMaskingLayer

class NanMaskingLayer(*args, **kwargs)

Bases: Layer

Methods

add_loss	Add loss tensor(s), potentially dependent on layer inputs.
add_metric	Adds metric tensor to the layer.
add_update	Add update op(s), potentially dependent on layer inputs.
add_variable	Deprecated, do NOT use! Alias for add_weight.
add_weight	Adds a new variable to the layer, or gets an existing one; returns it
apply
build	Creates the variables of the layer (optional, for subclass implementers).
call	This is where the layer's logic lives.
compute_mask	Computes an output mask tensor.
compute_output_shape	Computes the output shape of the layer.
compute_output_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	Creates a layer from its config.
get_config	Returns the config of the layer.
get_input_at	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at	Retrieves the input shape(s) of a layer at a given node.
get_losses_for	Retrieves losses relevant to a specific set of inputs.
get_output_at	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at	Retrieves the output shape(s) of a layer at a given node.
get_updates_for	Retrieves updates relevant to a specific set of inputs.
get_weights	Returns the current weights of the layer.
set_weights	Sets the weights of the layer, from Numpy arrays.
with_name_scope	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.
graph
inbound_nodes	Deprecated, do NOT use! Only for external Keras compatibility .
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	Losses which are associated with this Layer.
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	Deprecated, do NOT use! Only for external Keras compatibility .
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.
scope_name
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.

__call__(inputs, *args, **kwargs)

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

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. Note: kwarg scope is reserved for use by the layer.

Returns:

Output tensor(s).

Note

• If the layer’s call method takes a scope keyword argument, this argument will be automatically set to the current variable scope.

• 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.

Raises:

ValueError – if the layer’s call method returns None (an invalid value).

property activity_regularizer

Optional regularizer function for the output of this layer.

add_loss(losses, inputs=None)

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):

self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True) return inputs

```

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 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,)) 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)) `

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)

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.

Raises:

ValueError – If aggregation is anything other than None or 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.

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.

add_variable(*args, **kwargs)

Deprecated, do NOT use! Alias for add_weight.

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

Adds a new variable to the layer, or gets an existing one; returns it

Parameters:

• name – variable name.

• shape – variable shape.

• dtype – The type of the variable. Defaults to self.dtype or float32.

• initializer – initializer instance (callable).

• regularizer – regularizer instance (callable).

• trainable – whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean, stddev). Note, if the current variable scope is marked as non-trainable then this parameter is ignored and any added variables are also marked as non-trainable. trainable defaults to True unless synchronization is set to ON_READ.

• constraint – constraint instance (callable).

• 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.

• partitioner – (optional) partitioner instance (callable). If provided, when the requested variable is created it will be split into multiple partitions according to partitioner. In this case, an instance of PartitionedVariable is returned. Available partitioners include tf.compat.v1.fixed_size_partitioner and tf.compat.v1.variable_axis_size_partitioner. For more details, see the documentation of tf.compat.v1.get_variable and the “Variable Partitioners and Sharding” section of the API guide.

• **kwargs – Additional keyword arguments.

Returns:

The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned.

Raises:

• RuntimeError – If called with partitioned variable regularization and eager execution is enabled.

• ValueError – When trainable has been set to True with synchronization set as ON_READ.

build(input_shape)

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(input)

This is where the layer’s logic lives.

Parameters:

• inputs – Input tensor, or list/tuple of input tensors.

• **kwargs – Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

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.

Parameters:

• 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_shape(input_shape)

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.

Returns:

An input shape tuple.

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.

Parameters:

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).

Parameters:

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

Returns:

A layer instance.

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).

Returns:

Python dictionary.

get_input_at(node_index)

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 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.

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.

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.

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.

Returns:

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

Raises:

RuntimeError – If called in Eager mode.

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Parameters:

inputs – Input tensor or list/tuple of input tensors.

Returns:

List of loss tensors of the layer that depend on inputs.

get_output_at(node_index)

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 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.

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.

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.

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.

Returns:

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

Raises:

RuntimeError – If called in Eager mode.

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Parameters:

inputs – Input tensor or list/tuple of input tensors.

Returns:

List of update ops of the layer that depend on inputs.

get_weights()

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.

property inbound_nodes

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

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.

property 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.

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

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

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.

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– 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).

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.

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)>

Parameters:

method – The method to wrap.

Returns:

The original method wrapped such that it enters the module’s name scope.