tf.contrib.cudnn_rnn.CudnnRNNTanh

Cudnn implementation of the RNN-tanh layer.

Args
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().
Raises
ValueError if direction is invalid. Or dtype is not supported.
Attributes
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

View source

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.

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/cudnn_rnn/CudnnRNNTanh