contrib.keras.layers.SimpleRNN
tf.contrib.keras.layers.SimpleRNN
class tf.contrib.keras.layers.SimpleRNN
Defined in tensorflow/contrib/keras/python/keras/layers/recurrent.py
.
Fully-connected RNN where the output is to be fed back to input.
Arguments:
units: Positive integer, dimensionality of the output space. activation: Activation function to use. If you don't specify anything, no activation is applied If you pass None, 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, used for the linear transformation of the inputs.. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state.. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_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. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
References: - A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Properties
constraints
graph
input
Retrieves the input 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 tensor or list of input tensors.
Raises:
AttributeError: if the layer is connected to more than one incoming layers.
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 inbound node, i.e. if it is connected to one incoming layer.
Returns:
Input shape, as `TensorShape` (or list of `TensorShape`, one tuple per input tensor).
Raises:
AttributeError: if the layer is connected to more than one incoming layers.
losses
non_trainable_variables
non_trainable_weights
output
Retrieves the output 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 tensor or list of output tensors.
Raises:
AttributeError: if the layer is connected to more than one incoming layers.
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 inbound node, or if all inbound nodes have the same output shape.
Returns:
Output shape, as `TensorShape` (or list of `TensorShape`, one tuple per output tensor).
Raises:
AttributeError: if the layer is connected to more than one incoming layers.
scope_name
trainable_variables
trainable_weights
updates
variables
Returns the list of all layer variables/weights.
Returns:
A list of variables.
weights
Returns the list of all layer variables/weights.
Returns:
A list of variables.
Methods
__init__
__init__( units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, **kwargs )
__call__
__call__( inputs, initial_state=None, **kwargs )
__deepcopy__
__deepcopy__(memo)
add_loss
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 a 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.
The get_losses_for
method allows to retrieve the losses relevant to a specific set of inputs.
Arguments:
-
losses
: Loss tensor, or list/tuple of tensors. -
inputs
: Optional input tensor(s) that the loss(es) depend on. Must match theinputs
argument passed to the__call__
method at the time the losses are created. IfNone
is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_update
add_update( updates, inputs=None )
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 a 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.
Arguments:
-
updates
: Update op, or list/tuple of update ops. -
inputs
: Optional input tensor(s) that the update(s) depend on. Must match theinputs
argument passed to the__call__
method at the time the updates are created. IfNone
is passed, the updates are assumed to be unconditional, and will apply across all dataflows of the layer.
add_variable
add_variable( name, shape, dtype=None, initializer=None, regularizer=None, trainable=True )
Adds a new variable to the layer, or gets an existing one; returns it.
Arguments:
-
name
: variable name. -
shape
: variable shape. -
dtype
: The type of the variable. Defaults toself.dtype
. -
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).
Returns:
The created variable.
add_weight
add_weight( name, shape, dtype=None, initializer=None, regularizer=None, trainable=True, constraint=None )
Adds a weight variable to the layer.
Arguments:
name: String, the name for the weight variable. shape: The shape tuple of the weight. dtype: The dtype of the weight. initializer: An Initializer instance (callable). regularizer: An optional Regularizer instance. trainable: A boolean, whether the weight should be trained via backprop or not (assuming that the layer itself is also trainable). constraint: An optional Constraint instance.
Returns:
The created weight variable.
apply
apply( inputs, *args, **kwargs )
Apply the layer on a input.
This simply wraps self.__call__
.
Arguments:
-
inputs
: Input tensor(s). args: additional positional arguments to be passed toself.call
.
*kwargs: additional keyword arguments to be passed toself.call
.
Returns:
Output tensor(s).
build
build(input_shape)
call
call( inputs, mask=None, initial_state=None, training=None )
compute_mask
compute_mask( inputs, mask )
count_params
count_params()
Count the total number of scalars composing the weights.
Returns:
An integer count.
Raises:
RuntimeError: if the layer isn't yet built (in which case its weights aren't yet defined).
from_config
from_config( cls, 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 Container), nor weights (handled by set_weights
).
Arguments:
config: A Python dictionary, typically the output of get_config.
Returns:
A layer instance.
get_config
get_config()
get_constants
get_constants( inputs, training=None )
get_initial_states
get_initial_states(inputs)
get_input_at
get_input_at(node_index)
Retrieves the input tensor(s) of a layer at a given node.
Arguments:
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 tensor (or list of tensors if the layer has multiple inputs).
get_input_mask_at
get_input_mask_at(node_index)
Retrieves the input mask tensor(s) of a layer at a given node.
Arguments:
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
get_input_shape_at(node_index)
Retrieves the input shape(s) of a layer at a given node.
Arguments:
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).
get_losses_for
get_losses_for(inputs)
Retrieves losses relevant to a specific set of inputs.
Arguments:
-
inputs
: Input tensor or list/tuple of input tensors. Must match theinputs
argument passed to the__call__
method at the time the losses were created. If you passinputs=None
, unconditional losses are returned, such as weight regularization losses.
Returns:
List of loss tensors of the layer that depend on inputs
.
get_output_at
get_output_at(node_index)
Retrieves the output tensor(s) of a layer at a given node.
Arguments:
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 tensor (or list of tensors if the layer has multiple outputs).
get_output_mask_at
get_output_mask_at(node_index)
Retrieves the output mask tensor(s) of a layer at a given node.
Arguments:
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
get_output_shape_at(node_index)
Retrieves the output shape(s) of a layer at a given node.
Arguments:
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).
get_updates_for
get_updates_for(inputs)
Retrieves updates relevant to a specific set of inputs.
Arguments:
-
inputs
: Input tensor or list/tuple of input tensors. Must match theinputs
argument passed to the__call__
method at the time the updates were created. If you passinputs=None
, unconditional updates are returned.
Returns:
List of update ops of the layer that depend on inputs
.
get_weights
get_weights()
Returns the current weights of the layer.
Returns:
Weights values as a list of numpy arrays.
preprocess_input
preprocess_input( inputs, training=None )
reset_states
reset_states(states_value=None)
set_weights
set_weights(weights)
Sets the weights of the layer, from Numpy arrays.
Arguments:
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.
step
step( inputs, states )
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/keras/layers/SimpleRNN