我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用tensorflow.python.ops.rnn_cell.OutputProjectionWrapper()。
def embedding_rnn_seq2seq(encoder_inputs, decoder_inputs, cell, num_encoder_symbols, num_decoder_symbols, embedding_size, output_projection=None, feed_previous=False, dtype=dtypes.float32, scope=None): with variable_scope.variable_scope(scope or "embedding_rnn_seq2seq"): # Encoder. encoder_cell = rnn_cell.EmbeddingWrapper( cell, embedding_classes=num_encoder_symbols, embedding_size=embedding_size) _, encoder_state = rnn.rnn(encoder_cell, encoder_inputs, dtype=dtype) # Decoder. if output_projection is None: cell = rnn_cell.OutputProjectionWrapper(cell, num_decoder_symbols) if isinstance(feed_previous, bool): return embedding_rnn_decoder( decoder_inputs, encoder_state, cell, num_decoder_symbols, embedding_size, output_projection=output_projection, feed_previous=feed_previous) # If feed_previous is a Tensor, we construct 2 graphs and use cond. def decoder(feed_previous_bool): reuse = None if feed_previous_bool else True with variable_scope.variable_scope(variable_scope.get_variable_scope(), reuse=reuse): outputs, state = embedding_rnn_decoder( decoder_inputs, encoder_state, cell, num_decoder_symbols, embedding_size, output_projection=output_projection, feed_previous=feed_previous_bool, update_embedding_for_previous=False) return outputs + [state] outputs_and_state = control_flow_ops.cond(feed_previous, lambda: decoder(True), lambda: decoder(False)) return outputs_and_state[:-1], outputs_and_state[-1]
def embedding_rnn_seq2seq(encoder_inputs, decoder_inputs, cell, num_encoder_symbols, num_decoder_symbols, embedding_size, output_projection=None, feed_previous=False, dtype=dtypes.float32, scope=None): """Embedding RNN sequence-to-sequence model. """ with variable_scope.variable_scope(scope or "embedding_rnn_seq2seq"): # Encoder. encoder_cell = rnn_cell.EmbeddingWrapper( cell, embedding_classes=num_encoder_symbols, embedding_size=embedding_size) _, encoder_state = rnn.rnn(encoder_cell, encoder_inputs, dtype=dtype) # Decoder. if output_projection is None: cell = rnn_cell.OutputProjectionWrapper(cell, num_decoder_symbols) if isinstance(feed_previous, bool): return embedding_rnn_decoder( decoder_inputs, encoder_state, cell, num_decoder_symbols, embedding_size, output_projection=output_projection, feed_previous=feed_previous) # If feed_previous is a Tensor, we construct 2 graphs and use cond. def decoder(feed_previous_bool): reuse = None if feed_previous_bool else True with variable_scope.variable_scope(variable_scope.get_variable_scope(), reuse=reuse): outputs, state = embedding_rnn_decoder( decoder_inputs, encoder_state, cell, num_decoder_symbols, embedding_size, output_projection=output_projection, feed_previous=feed_previous_bool, update_embedding_for_previous=False) return outputs + [state] outputs_and_state = control_flow_ops.cond(feed_previous, lambda: decoder(True), lambda: decoder(False)) return outputs_and_state[:-1], outputs_and_state[-1]
def 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, beam_search=True, beam_size=10): with variable_scope.variable_scope(scope or "embedding_rnn_seq2seq") as scope: if dtype is not None: scope.set_dtype(dtype) else: dtype = scope.dtype # Encoder. encoder_cell = rnn_cell.EmbeddingWrapper( cell, embedding_classes=num_encoder_symbols, embedding_size=embedding_size) _, encoder_state = rnn.rnn(encoder_cell, encoder_inputs, dtype=dtype) # Decoder. if output_projection is None: cell = rnn_cell.OutputProjectionWrapper(cell, num_decoder_symbols) if isinstance(feed_previous, bool): return embedding_rnn_decoder(decoder_inputs, encoder_state, cell, num_decoder_symbols, embedding_size, output_projection=output_projection, feed_previous=feed_previous, scope=scope, beam_search=beam_search, beam_size=beam_size) # If feed_previous is a Tensor, we construct 2 graphs and use cond. def decoder(feed_previous_bool): reuse = None if feed_previous_bool else True with variable_scope.variable_scope( variable_scope.get_variable_scope(), reuse=reuse) as scope: outputs, state = embedding_rnn_decoder( decoder_inputs, encoder_state, cell, num_decoder_symbols, embedding_size, output_projection=output_projection, feed_previous=feed_previous_bool, update_embedding_for_previous=False, beam_search=beam_search, beam_size=beam_size) state_list = [state] if nest.is_sequence(state): state_list = nest.flatten(state) return outputs + state_list outputs_and_state = control_flow_ops.cond(feed_previous, lambda: decoder(True), lambda: decoder(False)) outputs_len = len(decoder_inputs) # Outputs length same as decoder inputs. state_list = outputs_and_state[outputs_len:] state = state_list[0] if nest.is_sequence(encoder_state): state = nest.pack_sequence_as(structure=encoder_state, flat_sequence=state_list) return outputs_and_state[:outputs_len], state
