tf.nn.rnn_cell.EmbeddingWrapper.state_size

tf.nn.rnn_cell.EmbeddingWrapper.output_size

class tf.nn.rnn_cell.EmbeddingWrapper Operator adding input embedding to the given cell. Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your inputs in time, do the embedding on this batch-concatenated sequence, then split it and feed into your RNN.

tf.nn.rnn_cell.DropoutWrapper.__init__(cell, input_keep_prob=1.0, output_keep_prob=1.0, seed=None) Create a cell with added input and/or output dropout. Dropout is never used on the state. Args: cell: an RNNCell, a projection to output_size is added to it. input_keep_prob: unit Tensor or float between 0 and 1, input keep probability; if it is float and 1, no input dropout will be added. output_keep_prob: unit Tensor or float between 0 and 1, output keep probability; if it is float and 1, no

tf.nn.rnn_cell.DropoutWrapper.__call__(inputs, state, scope=None) Run the cell with the declared dropouts.

tf.nn.rnn_cell.DropoutWrapper.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the sha

tf.nn.rnn_cell.DropoutWrapper.state_size

tf.nn.rnn_cell.DropoutWrapper.output_size

class tf.nn.rnn_cell.DropoutWrapper Operator adding dropout to inputs and outputs of the given cell.

tf.nn.rnn_cell.BasicRNNCell.__init__(num_units, input_size=None, activation=tanh)