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

class tf.contrib.training.SequenceQueueingStateSaver SequenceQueueingStateSaver provides access to stateful values from input. This class is meant to be used instead of, e.g., a Queue, for splitting variable-length sequence inputs into segments of sequences with fixed length and batching them into mini-batches. It maintains contexts and state for a sequence across the segments. It can be used in conjunction with a QueueRunner (see the example below). The SequenceQueueingStateSaver (SQSS) accep

tf.fft2d(input, name=None) Compute the 2-dimensional discrete Fourier Transform over the inner-most 2 dimensions of input. Args: input: A Tensor of type complex64. A complex64 tensor. name: A name for the operation (optional). Returns: A Tensor of type complex64. A complex64 tensor of the same shape as input. The inner-most 2 dimensions of input are replaced with their 2D Fourier Transform.

tf.contrib.learn.Estimator.__repr__()

tf.nn.rnn_cell.MultiRNNCell.state_size

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.graph

tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh) Initialize the parameters for an LSTM cell. Args: num_units: int, The number of units in the LSTM cell input_size: Deprecated and unused. use_peepholes: bool, set True to enable diagonal/peephole connections. cell_clip: (optional) A float value, if provi

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.loss(final_loss, name=None)

tf.SparseTensorValue.indices Alias for field number 0

virtual Status tensorflow::WritableFile::Append(const StringPiece &data)=0