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RNN decoder with attention for the sequence-to-sequence model.

In this context "attention" means that, during decoding, the RNN can look up information in the additional tensor attention_states, and it does this by focusing on a few entries from the tensor. This model has proven to yield especially good results in a number of sequence-to-sequence tasks. This implementation is based on (see below for details). It is recommended for complex sequence-to-sequence tasks.

decoder_inputs A list of 2D Tensors [batch_size x input_size].
initial_state 2D Tensor [batch_size x cell.state_size].
attention_states 3D Tensor [batch_size x attn_length x attn_size].
cell tf.compat.v1.nn.rnn_cell.RNNCell defining the cell function and size.
output_size Size of the output vectors; if None, we use cell.output_size.
num_heads Number of attention heads that read from attention_states.
loop_function If not None, this function will be applied to i-th output in order to generate i+1-th input, and decoder_inputs will be ignored, except for the first element ("GO" symbol). This can be used for decoding, but also for training to emulate Signature -- loop_function(prev, i) = next * prev is a 2D Tensor of shape [batch_size x output_size], * i is an integer, the step number (when advanced control is needed), * next is a 2D Tensor of shape [batch_size x input_size].
dtype The dtype to use for the RNN initial state (default: tf.float32).
scope VariableScope for the created subgraph; default: "attention_decoder".
initial_state_attention If False (default), initial attentions are zero. If True, initialize the attentions from the initial state and attention states -- useful when we wish to resume decoding from a previously stored decoder state and attention states.

A tuple of the form (outputs, state), where: outputs: A list of the same length as decoder_inputs of 2D Tensors of shape [batch_size x output_size]. These represent the generated outputs. Output i is computed from input i (which is either the i-th element of decoder_inputs or loop_function(output {i-1}, i)) as follows. First, we run the cell on a combination of the input and previous attention masks: cell_output, new_state = cell(linear(input, prev_attn), prev_state). Then, we calculate new attention masks: new_attn = softmax(V^T * tanh(W * attention_states + U * new_state)) and then we calculate the output: output = linear(cell_output, new_attn). state: The state of each decoder cell the final time-step. It is a 2D Tensor of shape [batch_size x cell.state_size].

ValueError when num_heads is not positive, there are no inputs, shapes of attention_states are not set, or input size cannot be inferred from the input.