tf.contrib.legacy_seq2seq.embedding_rnn_decoder

RNN decoder with embedding and a pure-decoding option.

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
)

Args
decoder_inputs	A list of 1D batch-sized int32 Tensors (decoder inputs).
initial_state	2D Tensor [batch_size x cell.state_size].
cell	tf.compat.v1.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".

Returns

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].

Raises
ValueError	When output_projection has the wrong shape.