tf.compat.v1.keras.layers.CuDNNLSTM

Fast LSTM implementation backed by cuDNN.

Inherits From: RNN, Layer, Module

tf.compat.v1.keras.layers.CuDNNLSTM(
    units,
    kernel_initializer='glorot_uniform',
    recurrent_initializer='orthogonal',
    bias_initializer='zeros',
    unit_forget_bias=True,
    kernel_regularizer=None,
    recurrent_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    recurrent_constraint=None,
    bias_constraint=None,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    **kwargs
)

More information about cuDNN can be found on the NVIDIA developer website. Can only be run on GPU.

Args
`units`	Positive integer, dimensionality of the output space.
`kernel_initializer`	Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.
`unit_forget_bias`	Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in Jozefowicz et al.
`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.
`return_sequences`	Boolean. Whether to return the last output. in the output sequence, or the full sequence.
`return_state`	Boolean. Whether to return the last state in addition to the output.
`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.

Attributes
`cell`
`states`

Attributes

cell

states

Methods

`get_losses_for`

View source

get_losses_for(
    inputs=None
)

`reset_states`

View source

reset_states(
    states=None
)

Reset the recorded states for the stateful RNN layer.

Can only be used when RNN layer is constructed with stateful = True. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size.

Raises
`AttributeError`	When the RNN layer is not stateful.
`ValueError`	When the batch size of the RNN layer is unknown.
`ValueError`	When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise.