tf.keras.layers.SimpleRNN

Fully-connected RNN where the output is to be fed back as the new input.

Inherits From: RNN, Layer, Operation

tf.keras.layers.SimpleRNN(
    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,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    unroll=False,
    seed=None,
    **kwargs
)

Args
`units`	Positive integer, dimensionality of the output space.
`activation`	Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`).
`use_bias`	Boolean, (default `True`), whether the layer uses a bias vector.
`kernel_initializer`	Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `"glorot_uniform"`.
`recurrent_initializer`	Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `"orthogonal"`.
`bias_initializer`	Initializer for the bias vector. Default: `"zeros"`.
`kernel_regularizer`	Regularizer function applied to the `kernel` weights matrix. Default: `None`.
`recurrent_regularizer`	Regularizer function applied to the `recurrent_kernel` weights matrix. Default: `None`.
`bias_regularizer`	Regularizer function applied to the bias vector. Default: `None`.
`activity_regularizer`	Regularizer function applied to the output of the layer (its "activation"). Default: `None`.
`kernel_constraint`	Constraint function applied to the `kernel` weights matrix. Default: `None`.
`recurrent_constraint`	Constraint function applied to the `recurrent_kernel` weights matrix. Default: `None`.
`bias_constraint`	Constraint function applied to the bias vector. Default: `None`.
`dropout`	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.
`recurrent_dropout`	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.
`return_sequences`	Boolean. Whether to return the last output in the output sequence, or the full sequence. Default: `False`.
`return_state`	Boolean. Whether to return the last state in addition to the output. Default: `False`.
`go_backwards`	Boolean (default: `False`). If `True`, process the input sequence backwards and return the reversed sequence.
`stateful`	Boolean (default: `False`). If `True`, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
`unroll`	Boolean (default: `False`). If `True`, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.

Call arguments
`sequence`	A 3D tensor, with shape `[batch, timesteps, feature]`.
`mask`	Binary tensor of shape `[batch, timesteps]` indicating whether a given timestep should be masked. An individual `True` entry indicates that the corresponding timestep should be utilized, while a `False` entry indicates that the corresponding timestep should be ignored.
`training`	Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if `dropout` or `recurrent_dropout` is used.
`initial_state`	List of initial state tensors to be passed to the first call of the cell.

Example:

inputs = np.random.random((32, 10, 8))
simple_rnn = keras.layers.SimpleRNN(4)
output = simple_rnn(inputs)  # The output has shape `(32, 4)`.
simple_rnn = keras.layers.SimpleRNN(
    4, return_sequences=True, return_state=True
)
# whole_sequence_output has shape `(32, 10, 4)`.
# final_state has shape `(32, 4)`.
whole_sequence_output, final_state = simple_rnn(inputs)

Attributes
`activation`
`bias_constraint`
`bias_initializer`
`bias_regularizer`
`dropout`
`input`	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
`kernel_constraint`
`kernel_initializer`
`kernel_regularizer`
`output`	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.
`recurrent_constraint`
`recurrent_dropout`
`recurrent_initializer`
`recurrent_regularizer`
`units`
`use_bias`

Methods

`from_config`

View source

@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_initial_state`

View source

get_initial_state(
    batch_size
)

`inner_loop`

View source

inner_loop(
    sequences, initial_state, mask, training=False
)

`reset_state`

View source

reset_state()

`reset_states`

View source

reset_states()

`symbolic_call`

View source

symbolic_call(
    *args, **kwargs
)

tf.keras.layers.SimpleRNNStay organized with collections Save and categorize content based on your preferences.

Args

Call arguments

Example:

Attributes

Methods

from_config

get_initial_state

inner_loop

reset_state

reset_states

symbolic_call

tf.keras.layers.SimpleRNN

`from_config`

`get_initial_state`

`inner_loop`

`reset_state`

`reset_states`

`symbolic_call`