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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq.md new file mode 100644 index 0000000000..2eafffe765 --- /dev/null +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq.md @@ -0,0 +1,46 @@ +### `tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(encoder_inputs, decoder_inputs, cell, num_encoder_symbols, num_decoder_symbols, embedding_size, output_projection=None, feed_previous=False, dtype=None, scope=None)` {#embedding_rnn_seq2seq} + +Embedding RNN sequence-to-sequence model. + +This model first embeds encoder_inputs by a newly created embedding (of shape +[num_encoder_symbols x input_size]). Then it runs an RNN to encode +embedded encoder_inputs into a state vector. Next, it embeds decoder_inputs +by another newly created embedding (of shape [num_decoder_symbols x +input_size]). Then it runs RNN decoder, initialized with the last +encoder state, on embedded decoder_inputs. + +##### Args: + + +* <b>`encoder_inputs`</b>: A list of 1D int32 Tensors of shape [batch_size]. +* <b>`decoder_inputs`</b>: A list of 1D int32 Tensors of shape [batch_size]. +* <b>`cell`</b>: rnn_cell.RNNCell defining the cell function and size. +* <b>`num_encoder_symbols`</b>: Integer; number of symbols on the encoder side. +* <b>`num_decoder_symbols`</b>: Integer; number of symbols on the decoder side. +* <b>`embedding_size`</b>: Integer, the length of the embedding vector for each symbol. +* <b>`output_projection`</b>: None or a pair (W, B) of output projection weights and + biases; W has shape [output_size x num_decoder_symbols] and B has + shape [num_decoder_symbols]; if provided and feed_previous=True, each + fed previous output will first be multiplied by W and added B. +* <b>`feed_previous`</b>: Boolean or scalar Boolean Tensor; if True, only the first + of decoder_inputs will be used (the "GO" symbol), and all other decoder + inputs will be taken from previous outputs (as in embedding_rnn_decoder). + If False, decoder_inputs are used as given (the standard decoder case). +* <b>`dtype`</b>: The dtype of the initial state for both the encoder and encoder + rnn cells (default: tf.float32). +* <b>`scope`</b>: VariableScope for the created subgraph; defaults to + "embedding_rnn_seq2seq" + +##### Returns: + + A tuple of the form (outputs, state), where: + +* <b>`outputs`</b>: 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. +* <b>`state`</b>: 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]. + |