tf.contrib.legacy_seq2seq.embedding_rnn_decoder( decoder_inputs, initial_state, cell, num_symbols, embedding_size, output_projection=None, feed_previous=False, update_embedding_for_previous=True, scope=None )
RNN decoder with embedding and a pure-decoding option.
decoder_inputs: A list of 1D batch-sized int32 Tensors (decoder inputs).
initial_state: 2D Tensor [batch_size x cell.state_size].
cell: tf.nn.rnn_cell.RNNCell defining the cell function.
num_symbols: Integer, how many symbols come into the embedding.
embedding_size: Integer, the length of the embedding vector for each symbol.
output_projection: None or a pair (W, B) of output projection weights and biases; W has shape [output_size x num_symbols] and B has shape [num_symbols]; if provided and feed_previous=True, each fed previous output will first be multiplied by W and added B.
feed_previous: Boolean; if True, only the first of decoder_inputs will be used (the "GO" symbol), and all other decoder inputs will be generated by: next = embedding_lookup(embedding, argmax(previous_output)), In effect, this implements a greedy decoder. It can also be used during training to emulate http://arxiv.org/abs/1506.03099. If False, decoder_inputs are used as given (the standard decoder case).
update_embedding_for_previous: Boolean; if False and feed_previous=True, only the embedding for the first symbol of decoder_inputs (the "GO" symbol) will be updated by back propagation. Embeddings for the symbols generated from the decoder itself remain unchanged. This parameter has no effect if feed_previous=False.
scope: VariableScope for the created subgraph; defaults to "embedding_rnn_decoder".
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors. The
output is of shape [batch_size x cell.output_size] when
output_projection is not None (and represents the dense representation
of predicted tokens). It is of shape [batch_size x num_decoder_symbols]
when output_projection is None.
state: The state of each decoder cell in each time-step. This is a list
with length len(decoder_inputs) -- one item for each time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
ValueError: When output_projection has the wrong shape.