tf.keras.layers.Dense

Help on class Dense in module keras.layers.core.dense:

class Dense(keras.engine.base_layer.Layer)
 |  Dense(*args, **kwargs)
 |  
 |  Just your regular densely-connected NN layer.
 |  
 |  `Dense` implements the operation:
 |  `output = activation(dot(input, kernel) + bias)`
 |  where `activation` is the element-wise activation function
 |  passed as the `activation` argument, `kernel` is a weights matrix
 |  created by the layer, and `bias` is a bias vector created by the layer
 |  (only applicable if `use_bias` is `True`). These are all attributes of
 |  `Dense`.
 |  
 |  Note: If the input to the layer has a rank greater than 2, then `Dense`
 |  computes the dot product between the `inputs` and the `kernel` along the
 |  last axis of the `inputs` and axis 0 of the `kernel` (using `tf.tensordot`).
 |  For example, if input has dimensions `(batch_size, d0, d1)`, then we create
 |  a `kernel` with shape `(d1, units)`, and the `kernel` operates along axis 2
 |  of the `input`, on every sub-tensor of shape `(1, 1, d1)` (there are
 |  `batch_size * d0` such sub-tensors).  The output in this case will have
 |  shape `(batch_size, d0, units)`.
 |  
 |  Besides, layer attributes cannot be modified after the layer has been called
 |  once (except the `trainable` attribute).
 |  When a popular kwarg `input_shape` is passed, then keras will create
 |  an input layer to insert before the current layer. This can be treated
 |  equivalent to explicitly defining an `InputLayer`.
 |  
 |  Example:
 |  
 |  >>> # Create a `Sequential` model and add a Dense layer as the first layer.
 |  >>> model = tf.keras.models.Sequential()
 |  >>> model.add(tf.keras.Input(shape=(16,)))
 |  >>> model.add(tf.keras.layers.Dense(32, activation='relu'))
 |  >>> # Now the model will take as input arrays of shape (None, 16)
 |  >>> # and output arrays of shape (None, 32).
 |  >>> # Note that after the first layer, you don't need to specify
 |  >>> # the size of the input anymore:
 |  >>> model.add(tf.keras.layers.Dense(32))
 |  >>> model.output_shape
 |  (None, 32)
 |  
 |  Args:
 |    units: Positive integer, dimensionality of the output space.
 |    activation: Activation function to use.
 |      If you don't specify anything, no activation is applied
 |      (ie. "linear" activation: `a(x) = x`).
 |    use_bias: Boolean, whether the layer uses a bias vector.
 |    kernel_initializer: Initializer for the `kernel` weights matrix.
 |    bias_initializer: Initializer for the bias vector.
 |    kernel_regularizer: Regularizer function applied to
 |      the `kernel` weights matrix.
 |    bias_regularizer: Regularizer function applied to the bias vector.
 |    activity_regularizer: Regularizer function applied to
 |      the output of the layer (its "activation").
 |    kernel_constraint: Constraint function applied to
 |      the `kernel` weights matrix.
 |    bias_constraint: Constraint function applied to the bias vector.
 |  
 |  Input shape:
 |    N-D tensor with shape: `(batch_size, ..., input_dim)`.
 |    The most common situation would be
 |    a 2D input with shape `(batch_size, input_dim)`.
 |  
 |  Output shape:
 |    N-D tensor with shape: `(batch_size, ..., units)`.
 |    For instance, for a 2D input with shape `(batch_size, input_dim)`,
 |    the output would have shape `(batch_size, units)`.
 |  
 |  Method resolution order:
 |      Dense
 |      keras.engine.base_layer.Layer
 |      tensorflow.python.module.module.Module
 |      tensorflow.python.trackable.autotrackable.AutoTrackable
 |      tensorflow.python.trackable.base.Trackable
 |      keras.utils.version_utils.LayerVersionSelector
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs)
 |  
 |  build(self, 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. 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).
 |  
 |  call(self, inputs)
 |      This is where the layer's logic lives.
 |      
 |      The `call()` method may not create state (except in its first
 |      invocation, wrapping the creation of variables or other resources in
 |      `tf.init_scope()`).  It is recommended to create state in `__init__()`,
 |      or the `build()` method that is called automatically before `call()`
 |      executes the first time.
 |      
 |      Args:
 |        inputs: Input tensor, or dict/list/tuple of input tensors.
 |          The first positional `inputs` argument is subject to special rules:
 |          - `inputs` must be explicitly passed. A layer cannot have zero
 |            arguments, and `inputs` cannot be provided via the default value
 |            of a keyword argument.
 |          - NumPy array or Python scalar values in `inputs` get cast as
 |            tensors.
 |          - Keras mask metadata is only collected from `inputs`.
 |          - Layers are built (`build(input_shape)` method)
 |            using shape info from `inputs` only.
 |          - `input_spec` compatibility is only checked against `inputs`.
 |          - Mixed precision input casting is only applied to `inputs`.
 |            If a layer has tensor arguments in `*args` or `**kwargs`, their
 |            casting behavior in mixed precision should be handled manually.
 |          - The SavedModel input specification is generated using `inputs`
 |            only.
 |          - Integration with various ecosystem packages like TFMOT, TFLite,
 |            TF.js, etc is only supported for `inputs` and not for tensors in
 |            positional and keyword arguments.
 |        *args: Additional positional arguments. May contain tensors, although
 |          this is not recommended, for the reasons above.
 |        **kwargs: Additional keyword arguments. May contain tensors, although
 |          this is not recommended, for the reasons above.
 |          The following optional keyword arguments are reserved:
 |          - `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, 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).
 |      
 |      Returns:
 |        A tensor or list/tuple of tensors.
 |  
 |  compute_output_shape(self, input_shape)
 |      Computes the output shape of the layer.
 |      
 |      This method will cause the layer's state to be built, if that has not
 |      happened before. This requires that the layer will later be used with
 |      inputs that match the input shape provided here.
 |      
 |      Args:
 |          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.
 |  
 |  get_config(self)
 |      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.
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from keras.engine.base_layer.Layer:
 |  
 |  __call__(self, *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.
 |  
 |  __delattr__(self, name)
 |      Implement delattr(self, name).
 |  
 |  __getstate__(self)
 |  
 |  __setattr__(self, name, value)
 |      Support self.foo = trackable syntax.
 |  
 |  __setstate__(self, state)
 |  
 |  add_loss(self, 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
 |      ```
 |      
 |      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,))
 |      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(self, 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(self, 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(self, *args, **kwargs)
 |      Deprecated, do NOT use! Alias for `add_weight`.
 |  
 |  add_weight(self, name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregationV2.NONE: 0>, **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`.
 |  
 |  compute_mask(self, 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(self, 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(self)
 |      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).
 |  
 |  finalize_state(self)
 |      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.
 |  
 |  get_input_at(self, 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(self, 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(self, 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(self, 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(self, 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(self, 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(self)
 |      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.
 |  
 |  set_weights(self, 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.
 |  
 |  ----------------------------------------------------------------------
 |  Class methods inherited from keras.engine.base_layer.Layer:
 |  
 |  from_config(config) from builtins.type
 |      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.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from keras.engine.base_layer.Layer:
 |  
 |  activity_regularizer
 |      Optional regularizer function for the output of this layer.
 |  
 |  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.
 |  
 |  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.
 |  
 |  dtype_policy
 |      The dtype policy associated with this layer.
 |      
 |      This is an instance of a `tf.keras.mixed_precision.Policy`.
 |  
 |  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.
 |      
 |      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.
 |  
 |  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.
 |  
 |  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.
 |  
 |  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.
 |  
 |  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.
 |  
 |  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.
 |  
 |  name
 |      Name of the layer (string), set in the constructor.
 |  
 |  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).
 |  
 |  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
 |      Return Functional API nodes downstream of this layer.
 |  
 |  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.
 |  
 |  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.
 |  
 |  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.
 |  
 |  stateful
 |  
 |  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.
 |      
 |      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.
 |  
 |  updates
 |  
 |  variable_dtype
 |      Alias of `Layer.dtype`, the dtype of the weights.
 |  
 |  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.
 |  
 |  weights
 |      Returns the list of all layer variables/weights.
 |      
 |      Returns:
 |        A list of variables.
 |  
 |  ----------------------------------------------------------------------
 |  Class methods inherited from tensorflow.python.module.module.Module:
 |  
 |  with_name_scope(method) from builtins.type
 |      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.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from tensorflow.python.module.module.Module:
 |  
 |  name_scope
 |      Returns a `tf.name_scope` instance for this class.
 |  
 |  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.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from tensorflow.python.trackable.base.Trackable:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  ----------------------------------------------------------------------
 |  Static methods inherited from keras.utils.version_utils.LayerVersionSelector:
 |  
 |  __new__(cls, *args, **kwargs)
 |      Create and return a new object.  See help(type) for accurate signature.