tf.contrib.cudnn_rnn.CudnnRNNTanh

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
)

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

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.