tf.contrib.training.SequenceQueueingStateSaver.__init__(batch_size, num_unroll, input_length, input_key, input_sequences, input_context, initial_states, capacity=None

tf.contrib.util.constant_value(tensor) Returns the constant value of the given tensor, if efficiently calculable.

tf.contrib.training.SequenceQueueingStateSaver.batch_size

tf.get_default_session() Returns the default session for the current thread. The returned Session

class tf.contrib.training.NextQueuedSequenceBatch NextQueuedSequenceBatch stores deferred SequenceQueueingStateSaver data.

tf.sparse_to_indicator(sp_input, vocab_size, name=None) Converts a SparseTensor of ids into a dense bool indicator

tf.sparse_segment_sqrt_n(data, indices, segment_ids, name=None) Computes the sum along sparse segments of a tensor divided by

tf.nn.rnn_cell.BasicLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args:

tf.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None) Most basic RNN: output = new_state = activation(W * input + U

tf.contrib.metrics.streaming_sparse_recall_at_k(*args, **kwargs) Computes recall@k of the predictions with respect to sparse labels