tf.contrib.legacy_seq2seq.basic_rnn_seq2seq(encoder_inputs, decoder_inputs, cell, dtype=tf.float32, scope=None)

tf.contrib.legacy_seq2seq.basic_rnn_seq2seq(encoder_inputs, decoder_inputs, cell, dtype=tf.float32, scope=None)

Basic RNN sequence-to-sequence model.

This model first runs an RNN to encode encoder_inputs into a state vector, then runs decoder, initialized with the last encoder state, on decoder_inputs. Encoder and decoder use the same RNN cell type, but don't share parameters.

Args:

• encoder_inputs: A list of 2D Tensors [batch_size x input_size].
• decoder_inputs: A list of 2D Tensors [batch_size x input_size].
• cell: core_rnn_cell.RNNCell defining the cell function and size.
• dtype: The dtype of the initial state of the RNN cell (default: tf.float32).
• scope: VariableScope for the created subgraph; default: "basic_rnn_seq2seq".

Returns:

A tuple of the form (outputs, state), where: outputs: A list of the same length as decoder_inputs of 2D Tensors with shape [batch_size x output_size] containing the generated outputs. state: The state of each decoder cell in the final time-step. It is a 2D Tensor of shape [batch_size x cell.state_size].

Defined in tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py.