tf.compat.v1.keras.layers.CuDNNLSTM

View source on GitHub

Fast LSTM implementation backed by cuDNN.

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.

Arguments:

  • 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

Methods

get_initial_state

View source

get_initial_state(
    inputs
)

reset_states

View source

reset_states(
    states=None
)