tf.contrib.cudnn_rnn.CudnnRNNTanh

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Cudnn implementation of the RNN-tanh layer.

tf.contrib.cudnn_rnn.CudnnRNNTanh(
    num_layers, num_units, input_mode=CUDNN_INPUT_LINEAR_MODE,
    direction=CUDNN_RNN_UNIDIRECTION, dropout=0.0, seed=None,
    dtype=tf.dtypes.float32, kernel_initializer=None, bias_initializer=None,
    name=None
)

num_layers the number of layers for the RNN model.
num_units the number of units within the RNN model.
input_mode indicate whether there is a linear projection between the input and the actual computation before the first layer. It can be 'linear_input', 'skip_input' or 'auto_select'. 'linear_input' (default) always applies a linear projection of input onto RNN hidden state. (standard RNN behavior). 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'.
direction the direction model that the model operates. Can be either 'unidirectional' or 'bidirectional'
dropout dropout rate, a number between [0, 1]. Dropout is applied between each layer (no dropout is applied for a model with a single layer). When set to 0, dropout is disabled.
seed the op seed used for initializing dropout. See tf.compat.v1.set_random_seed for behavior.
dtype tf.float16, tf.float32 or tf.float64
kernel_initializer starting value to initialize the weight.
bias_initializer starting value to initialize the bias (default is all zeros).
name VariableScope for the created subgraph; defaults to class name. This only serves the default scope if later no scope is specified when invoking call().

ValueError if direction is invalid. Or dtype is not supported.

canonical_bias_shapes Shapes of Cudnn canonical bias tensors.
canonical_weight_shapes Shapes of Cudnn canonical weight tensors.
direction Returns unidirectional or bidirectional.
graph DEPRECATED FUNCTION
input_mode Input mode of first layer.

Indicates whether there is a linear projection between the input and the actual computation before the first layer. It can be

  • 'linear_input': (default) always applies a linear projection of input onto RNN hidden state. (standard RNN behavior)
  • 'skip_input': 'skip_input' is only allowed when input_size == num_units.
  • 'auto_select'. implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'.
input_size

num_dirs

num_layers

num_units

rnn_mode Type of RNN cell used.
saveable

scope_name

Methods

state_shape

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state_shape(
    batch_size
)

Shape of the state of Cudnn RNN cells w/o.

input_c.

Shape is a 1-element tuple, [num_layers * num_dirs, batch_size, num_units] Args: batch_size: an int

Returns
a tuple of python arrays.