tf.nn.rnn_cell.InputProjectionWrapper.state_size

class tf.nn.rnn_cell.GRUCell Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).

tf.nn.rnn_cell.GRUCell.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 shapes [ba

tf.nn.rnn_cell.GRUCell.state_size

tf.nn.rnn_cell.GRUCell.output_size

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

tf.nn.rnn_cell.GRUCell.__call__(inputs, state, scope=None) Gated recurrent unit (GRU) with nunits cells.

tf.nn.rnn_cell.EmbeddingWrapper.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 s

tf.nn.rnn_cell.EmbeddingWrapper.__init__(cell, embedding_classes, embedding_size, initializer=None) Create a cell with an added input embedding. Args: cell: an RNNCell, an embedding will be put before its inputs. embedding_classes: integer, how many symbols will be embedded. embedding_size: integer, the size of the vectors we embed into. initializer: an initializer to use when creating the embedding; if None, the initializer from variable scope or a default one is used. Raises: TypeErro

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.