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

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

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.ObservedStochasticTensor.loss(final_loss, name=None)

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.StochasticTensor.graph

tf.SparseTensorValue.indices Alias for field number 0

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