Python decoder.dynamic_decode函数代码示例

  def testLuongScaledDType(self):
    # Test case for GitHub issue 18099
    for dtype in [np.float16, np.float32, np.float64]:
      num_units = 128
      encoder_outputs = array_ops.placeholder(dtype, shape=[64, None, 256])
      encoder_sequence_length = array_ops.placeholder(dtypes.int32, shape=[64])
      decoder_inputs = array_ops.placeholder(dtype, shape=[64, None, 128])
      decoder_sequence_length = array_ops.placeholder(dtypes.int32, shape=[64])
      batch_size = 64
      attention_mechanism = wrapper.LuongAttention(
          num_units=num_units,
          memory=encoder_outputs,
          memory_sequence_length=encoder_sequence_length,
          scale=True,
          dtype=dtype,
      )
      cell = rnn_cell.LSTMCell(num_units)
      cell = wrapper.AttentionWrapper(cell, attention_mechanism)

      helper = helper_py.TrainingHelper(decoder_inputs,
                                        decoder_sequence_length)
      my_decoder = basic_decoder.BasicDecoder(
          cell=cell,
          helper=helper,
          initial_state=cell.zero_state(
              dtype=dtype, batch_size=batch_size))

      final_outputs, final_state, _ = decoder.dynamic_decode(my_decoder)
      self.assertTrue(
          isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
      self.assertEqual(final_outputs.rnn_output.dtype, dtype)
      self.assertTrue(
          isinstance(final_state, wrapper.AttentionWrapperState))
      self.assertTrue(
          isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))

  def _testDynamicDecodeRNNWithTrainingHelperMatchesDynamicRNN(
      self, use_sequence_length):
    sequence_length = [3, 4, 3, 1, 0]
    batch_size = 5
    max_time = 8
    input_depth = 7
    cell_depth = 10
    max_out = max(sequence_length)

    with self.session(use_gpu=True) as sess:
      inputs = np.random.randn(batch_size, max_time,
                               input_depth).astype(np.float32)

      cell = rnn_cell.LSTMCell(cell_depth)
      zero_state = cell.zero_state(dtype=dtypes.float32, batch_size=batch_size)
      helper = helper_py.TrainingHelper(inputs, sequence_length)
      my_decoder = basic_decoder.BasicDecoder(
          cell=cell, helper=helper, initial_state=zero_state)

      # Match the variable scope of dynamic_rnn below so we end up
      # using the same variables
      with vs.variable_scope("root") as scope:
        final_decoder_outputs, final_decoder_state, _ = decoder.dynamic_decode(
            my_decoder,
            # impute_finished=True ensures outputs and final state
            # match those of dynamic_rnn called with sequence_length not None
            impute_finished=use_sequence_length,
            scope=scope)

      with vs.variable_scope(scope, reuse=True) as scope:
        final_rnn_outputs, final_rnn_state = rnn.dynamic_rnn(
            cell,
            inputs,
            sequence_length=sequence_length if use_sequence_length else None,
            initial_state=zero_state,
            scope=scope)

      sess.run(variables.global_variables_initializer())
      sess_results = sess.run({
          "final_decoder_outputs": final_decoder_outputs,
          "final_decoder_state": final_decoder_state,
          "final_rnn_outputs": final_rnn_outputs,
          "final_rnn_state": final_rnn_state
      })

      # Decoder only runs out to max_out; ensure values are identical
      # to dynamic_rnn, which also zeros out outputs and passes along state.
      self.assertAllClose(sess_results["final_decoder_outputs"].rnn_output,
                          sess_results["final_rnn_outputs"][:, 0:max_out, :])
      if use_sequence_length:
        self.assertAllClose(sess_results["final_decoder_state"],
                            sess_results["final_rnn_state"])

    def inference(self, inputs, train=True):
        config = self.config

        # extract character representations from embedding
        with tf.variable_scope('embedding', initializer=tf.contrib.layers.xavier_initializer()):
            embedding = tf.get_variable('embedding',
                    shape=(config.vocab_size, config.embed_dim), dtype=tf.float32)
            embedded_inputs = tf.nn.embedding_lookup(embedding, inputs['text'])

        # extract speaker embedding if multi-speaker
        with tf.variable_scope('speaker'):
            if config.num_speakers > 1:
                speaker_embed = tf.get_variable('speaker_embed',
                        shape=(config.num_speakers, config.speaker_embed_dim), dtype=tf.float32)
                speaker_embed = \
                        tf.nn.embedding_lookup(speaker_embed, inputs['speaker'])
            else:
                speaker_embed = None

        # process text input with CBHG module 
        with tf.variable_scope('encoder'):
            pre_out = self.pre_net(embedded_inputs, dropout=config.char_dropout_prob, train=train)
            tf.summary.histogram('pre_net_out', pre_out)

            encoded = ops.CBHG(pre_out, speaker_embed, K=16, c=[128,128,128], gru_units=128)

        # pass through attention based decoder
        with tf.variable_scope('decoder'):
            dec = self.create_decoder(encoded, inputs, speaker_embed, train)
            (seq2seq_output, _), attention_state, _ = \
                    decoder.dynamic_decode(dec, maximum_iterations=config.max_decode_iter)
            self.alignments = tf.transpose(attention_state.alignment_history.stack(), [1,0,2])
            tf.summary.histogram('seq2seq_output', seq2seq_output)

        # use second CBHG module to process mel features into linear spectogram
        with tf.variable_scope('post-process'):
            # reshape to account for r value
            post_input = tf.reshape(seq2seq_output, 
                    (tf.shape(seq2seq_output)[0], -1, config.mel_features))

            output = ops.CBHG(post_input, K=8, c=[128,256,80], gru_units=128)
            output = tf.layers.dense(output, units=config.fft_size)

            # reshape back to r frame representation
            output = tf.reshape(output, (tf.shape(output)[0], -1, config.fft_size*config.r))
            tf.summary.histogram('output', output)

        return seq2seq_output, output

  def _init_decoder(self):
    with tf.variable_scope('Decoder') as scope:

      self.fc_layer = Dense(self.vocab_size)
      
      if self.is_inference:
        self.start_tokens = tf.placeholder(tf.int32,shape=[None],name='start_tokens')
        self.end_token = tf.placeholder(tf.int32,name='end_token')
        
      
        self.helper = seq2seq.GreedyEmbeddingHelper(
            embedding=self.embedding_matrix,
            start_tokens=self.start_tokens,
            end_token=self.end_token
        )
      else:
        self.helper = seq2seq.TrainingHelper(
            inputs=self.decoder_train_inputs_embedded, 
            sequence_length=self.decoder_train_length,
            time_major=True
        )
      
      self.decoder = seq2seq.BasicDecoder(
          cell=self.decoder_cell,
          helper=self.helper,
          initial_state=self.encoder_state,
          output_layer=self.fc_layer
      )
      
      
      (self.decoder_outputs_train,
       self.decoder_state_train,
       self.decoder_context_state_train
       ) = (
           decoder.dynamic_decode(
               self.decoder, 
               output_time_major=True)
      )
      self.logits = self.decoder_outputs_train.rnn_output
      if not self.is_inference:
        self.decoder_prediction_inference = tf.argmax(self.logits, axis=-1, name='decoder_prediction_inference')
      
        self.decoder_prediction_train = tf.argmax(self.decoder_outputs_train.rnn_output, axis=-1, name='decoder_prediction_train')
        
        self._init_optimizer()
      else:
        self.prob = tf.nn.softmax(self.logits)

  def _testWithAttention(self,
                         create_attention_mechanism,
                         expected_final_output,
                         expected_final_state,
                         attention_mechanism_depth=3,
                         alignment_history=False,
                         expected_final_alignment_history=None,
                         attention_layer_size=6,
                         name=""):
    encoder_sequence_length = [3, 2, 3, 1, 0]
    decoder_sequence_length = [2, 0, 1, 2, 3]
    batch_size = 5
    encoder_max_time = 8
    decoder_max_time = 4
    input_depth = 7
    encoder_output_depth = 10
    cell_depth = 9

    if attention_layer_size is not None:
      attention_depth = attention_layer_size
    else:
      attention_depth = encoder_output_depth

    decoder_inputs = np.random.randn(batch_size, decoder_max_time,
                                     input_depth).astype(np.float32)
    encoder_outputs = np.random.randn(batch_size, encoder_max_time,
                                      encoder_output_depth).astype(np.float32)

    attention_mechanism = create_attention_mechanism(
        num_units=attention_mechanism_depth,
        memory=encoder_outputs,
        memory_sequence_length=encoder_sequence_length)

    with self.test_session(use_gpu=True) as sess:
      with vs.variable_scope(
          "root",
          initializer=init_ops.random_normal_initializer(stddev=0.01, seed=3)):
        cell = core_rnn_cell.LSTMCell(cell_depth)
        cell = wrapper.AttentionWrapper(
            cell,
            attention_mechanism,
            attention_layer_size=attention_layer_size,
            alignment_history=alignment_history)
        helper = helper_py.TrainingHelper(decoder_inputs,
                                          decoder_sequence_length)
        my_decoder = basic_decoder.BasicDecoder(
            cell=cell,
            helper=helper,
            initial_state=cell.zero_state(
                dtype=dtypes.float32, batch_size=batch_size))

        final_outputs, final_state, _ = decoder.dynamic_decode(my_decoder)

      self.assertTrue(
          isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
      self.assertTrue(
          isinstance(final_state, wrapper.AttentionWrapperState))
      self.assertTrue(
          isinstance(final_state.cell_state, core_rnn_cell.LSTMStateTuple))

      self.assertEqual((batch_size, None, attention_depth),
                       tuple(final_outputs.rnn_output.get_shape().as_list()))
      self.assertEqual((batch_size, None),
                       tuple(final_outputs.sample_id.get_shape().as_list()))

      self.assertEqual((batch_size, attention_depth),
                       tuple(final_state.attention.get_shape().as_list()))
      self.assertEqual((batch_size, cell_depth),
                       tuple(final_state.cell_state.c.get_shape().as_list()))
      self.assertEqual((batch_size, cell_depth),
                       tuple(final_state.cell_state.h.get_shape().as_list()))

      if alignment_history:
        state_alignment_history = final_state.alignment_history.stack()
        # Remove the history from final_state for purposes of the
        # remainder of the tests.
        final_state = final_state._replace(alignment_history=())  # pylint: disable=protected-access
        self.assertEqual((None, batch_size, encoder_max_time),
                         tuple(state_alignment_history.get_shape().as_list()))
      else:
        state_alignment_history = ()

      sess.run(variables.global_variables_initializer())
      sess_results = sess.run({
          "final_outputs": final_outputs,
          "final_state": final_state,
          "state_alignment_history": state_alignment_history,
      })

      print("Copy/paste (%s)\nexpected_final_output = " % name,
            sess_results["final_outputs"])
      sys.stdout.flush()
      print("Copy/paste (%s)\nexpected_final_state = " % name,
            sess_results["final_state"])
      sys.stdout.flush()
      print("Copy/paste (%s)\nexpected_final_alignment_history = " % name,
            np.asarray(sess_results["state_alignment_history"]))
      sys.stdout.flush()
      nest.map_structure(self.assertAllClose, expected_final_output,
                         sess_results["final_outputs"])
#.........这里部分代码省略.........

  def _testWithAttention(self,
                         create_attention_mechanism,
                         expected_final_outputs,
                         expected_final_state,
                         attention_mechanism_depth=3):
    encoder_sequence_length = [3, 2, 3, 1, 0]
    decoder_sequence_length = [2, 0, 1, 2, 3]
    batch_size = 5
    encoder_max_time = 8
    decoder_max_time = 4
    input_depth = 7
    encoder_output_depth = 10
    cell_depth = 9
    attention_depth = 6

    decoder_inputs = np.random.randn(batch_size, decoder_max_time,
                                     input_depth).astype(np.float32)
    encoder_outputs = np.random.randn(batch_size, encoder_max_time,
                                      encoder_output_depth).astype(np.float32)

    attention_mechanism = create_attention_mechanism(
        num_units=attention_mechanism_depth,
        memory=encoder_outputs,
        memory_sequence_length=encoder_sequence_length)

    with self.test_session() as sess:
      with vs.variable_scope(
          "root",
          initializer=init_ops.random_normal_initializer(stddev=0.01, seed=3)):
        cell = core_rnn_cell.LSTMCell(cell_depth)
        cell = wrapper.DynamicAttentionWrapper(
            cell, attention_mechanism, attention_size=attention_depth)
        helper = helper_py.TrainingHelper(decoder_inputs,
                                          decoder_sequence_length)
        my_decoder = basic_decoder.BasicDecoder(
            cell=cell,
            helper=helper,
            initial_state=cell.zero_state(
                dtype=dtypes.float32, batch_size=batch_size))

        final_outputs, final_state = decoder.dynamic_decode(my_decoder)

      self.assertTrue(
          isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
      self.assertTrue(
          isinstance(final_state, wrapper.DynamicAttentionWrapperState))
      self.assertTrue(
          isinstance(final_state.cell_state, core_rnn_cell.LSTMStateTuple))

      self.assertEqual((batch_size, None, attention_depth),
                       tuple(final_outputs.rnn_output.get_shape().as_list()))
      self.assertEqual((batch_size, None),
                       tuple(final_outputs.sample_id.get_shape().as_list()))

      self.assertEqual((batch_size, attention_depth),
                       tuple(final_state.attention.get_shape().as_list()))
      self.assertEqual((batch_size, cell_depth),
                       tuple(final_state.cell_state.c.get_shape().as_list()))
      self.assertEqual((batch_size, cell_depth),
                       tuple(final_state.cell_state.h.get_shape().as_list()))

      sess.run(variables.global_variables_initializer())
      sess_results = sess.run({
          "final_outputs": final_outputs,
          "final_state": final_state
      })

      nest.map_structure(self.assertAllClose, expected_final_outputs,
                         sess_results["final_outputs"])
      nest.map_structure(self.assertAllClose, expected_final_state,
                         sess_results["final_state"])

  def _testDynamicDecodeRNN(self, time_major, maximum_iterations=None):

    sequence_length = [3, 4, 3, 1, 0]
    batch_size = 5
    max_time = 8
    input_depth = 7
    cell_depth = 10
    max_out = max(sequence_length)

    with self.session(use_gpu=True) as sess:
      if time_major:
        inputs = np.random.randn(max_time, batch_size,
                                 input_depth).astype(np.float32)
      else:
        inputs = np.random.randn(batch_size, max_time,
                                 input_depth).astype(np.float32)
      cell = rnn_cell.LSTMCell(cell_depth)
      helper = helper_py.TrainingHelper(
          inputs, sequence_length, time_major=time_major)
      my_decoder = basic_decoder.BasicDecoder(
          cell=cell,
          helper=helper,
          initial_state=cell.zero_state(
              dtype=dtypes.float32, batch_size=batch_size))

      final_outputs, final_state, final_sequence_length = (
          decoder.dynamic_decode(my_decoder, output_time_major=time_major,
                                 maximum_iterations=maximum_iterations))

      def _t(shape):
        if time_major:
          return (shape[1], shape[0]) + shape[2:]
        return shape

      self.assertTrue(
          isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
      self.assertTrue(isinstance(final_state, rnn_cell.LSTMStateTuple))

      self.assertEqual(
          (batch_size,),
          tuple(final_sequence_length.get_shape().as_list()))
      self.assertEqual(
          _t((batch_size, None, cell_depth)),
          tuple(final_outputs.rnn_output.get_shape().as_list()))
      self.assertEqual(
          _t((batch_size, None)),
          tuple(final_outputs.sample_id.get_shape().as_list()))

      sess.run(variables.global_variables_initializer())
      sess_results = sess.run({
          "final_outputs": final_outputs,
          "final_state": final_state,
          "final_sequence_length": final_sequence_length,
      })

      # Mostly a smoke test
      time_steps = max_out
      expected_length = sequence_length
      if maximum_iterations is not None:
        time_steps = min(max_out, maximum_iterations)
        expected_length = [min(x, maximum_iterations) for x in expected_length]
      self.assertEqual(
          _t((batch_size, time_steps, cell_depth)),
          sess_results["final_outputs"].rnn_output.shape)
      self.assertEqual(
          _t((batch_size, time_steps)),
          sess_results["final_outputs"].sample_id.shape)
      self.assertItemsEqual(expected_length,
                            sess_results["final_sequence_length"])

  def _testWithMaybeMultiAttention(self,
                                   is_multi,
                                   create_attention_mechanisms,
                                   expected_final_output,
                                   expected_final_state,
                                   attention_mechanism_depths,
                                   alignment_history=False,
                                   expected_final_alignment_history=None,
                                   attention_layer_sizes=None,
                                   attention_layers=None,
                                   name=''):
    # Allow is_multi to be True with a single mechanism to enable test for
    # passing in a single mechanism in a list.
    assert len(create_attention_mechanisms) == 1 or is_multi
    encoder_sequence_length = [3, 2, 3, 1, 1]
    decoder_sequence_length = [2, 0, 1, 2, 3]
    batch_size = 5
    encoder_max_time = 8
    decoder_max_time = 4
    input_depth = 7
    encoder_output_depth = 10
    cell_depth = 9

    if attention_layer_sizes is not None:
      # Compute sum of attention_layer_sizes. Use encoder_output_depth if None.
      attention_depth = sum([attention_layer_size or encoder_output_depth
                             for attention_layer_size in attention_layer_sizes])
    elif attention_layers is not None:
      # Compute sum of attention_layers output depth.
      attention_depth = sum(
          attention_layer.compute_output_shape(
              [batch_size, cell_depth + encoder_output_depth])[-1].value
          for attention_layer in attention_layers)
    else:
      attention_depth = encoder_output_depth * len(create_attention_mechanisms)

    decoder_inputs = array_ops.placeholder_with_default(
        np.random.randn(batch_size, decoder_max_time,
                        input_depth).astype(np.float32),
        shape=(None, None, input_depth))
    encoder_outputs = array_ops.placeholder_with_default(
        np.random.randn(batch_size, encoder_max_time,
                        encoder_output_depth).astype(np.float32),
        shape=(None, None, encoder_output_depth))

    attention_mechanisms = [
        creator(num_units=depth,
                memory=encoder_outputs,
                memory_sequence_length=encoder_sequence_length)
        for creator, depth in zip(create_attention_mechanisms,
                                  attention_mechanism_depths)]

    with self.test_session(use_gpu=True) as sess:
      with vs.variable_scope(
          'root',
          initializer=init_ops.random_normal_initializer(stddev=0.01, seed=3)):
        attention_layer_size = attention_layer_sizes
        attention_layer = attention_layers
        if not is_multi:
          if attention_layer_size is not None:
            attention_layer_size = attention_layer_size[0]
          if attention_layer is not None:
            attention_layer = attention_layer[0]
        cell = rnn_cell.LSTMCell(cell_depth)
        cell = wrapper.AttentionWrapper(
            cell,
            attention_mechanisms if is_multi else attention_mechanisms[0],
            attention_layer_size=attention_layer_size,
            alignment_history=alignment_history,
            attention_layer=attention_layer)
        helper = helper_py.TrainingHelper(decoder_inputs,
                                          decoder_sequence_length)
        my_decoder = basic_decoder.BasicDecoder(
            cell=cell,
            helper=helper,
            initial_state=cell.zero_state(
                dtype=dtypes.float32, batch_size=batch_size))

        final_outputs, final_state, _ = decoder.dynamic_decode(my_decoder)

      self.assertTrue(
          isinstance(final_outputs, basic_decoder.BasicDecoderOutput))
      self.assertTrue(
          isinstance(final_state, wrapper.AttentionWrapperState))
      self.assertTrue(
          isinstance(final_state.cell_state, rnn_cell.LSTMStateTuple))

      self.assertEqual((batch_size, None, attention_depth),
                       tuple(final_outputs.rnn_output.get_shape().as_list()))
      self.assertEqual((batch_size, None),
                       tuple(final_outputs.sample_id.get_shape().as_list()))

      self.assertEqual((batch_size, attention_depth),
                       tuple(final_state.attention.get_shape().as_list()))
      self.assertEqual((batch_size, cell_depth),
                       tuple(final_state.cell_state.c.get_shape().as_list()))
      self.assertEqual((batch_size, cell_depth),
                       tuple(final_state.cell_state.h.get_shape().as_list()))

      if alignment_history:
#.........这里部分代码省略.........

  def _testDynamicDecodeRNN(self, time_major, has_attention,
                            with_alignment_history=False):
    encoder_sequence_length = np.array([3, 2, 3, 1, 1])
    decoder_sequence_length = np.array([2, 0, 1, 2, 3])
    batch_size = 5
    decoder_max_time = 4
    input_depth = 7
    cell_depth = 9
    attention_depth = 6
    vocab_size = 20
    end_token = vocab_size - 1
    start_token = 0
    embedding_dim = 50
    max_out = max(decoder_sequence_length)
    output_layer = layers_core.Dense(vocab_size, use_bias=True, activation=None)
    beam_width = 3

    with self.cached_session() as sess:
      batch_size_tensor = constant_op.constant(batch_size)
      embedding = np.random.randn(vocab_size, embedding_dim).astype(np.float32)
      cell = rnn_cell.LSTMCell(cell_depth)
      initial_state = cell.zero_state(batch_size, dtypes.float32)
      coverage_penalty_weight = 0.0
      if has_attention:
        coverage_penalty_weight = 0.2
        inputs = array_ops.placeholder_with_default(
            np.random.randn(batch_size, decoder_max_time, input_depth).astype(
                np.float32),
            shape=(None, None, input_depth))
        tiled_inputs = beam_search_decoder.tile_batch(
            inputs, multiplier=beam_width)
        tiled_sequence_length = beam_search_decoder.tile_batch(
            encoder_sequence_length, multiplier=beam_width)
        attention_mechanism = attention_wrapper.BahdanauAttention(
            num_units=attention_depth,
            memory=tiled_inputs,
            memory_sequence_length=tiled_sequence_length)
        initial_state = beam_search_decoder.tile_batch(
            initial_state, multiplier=beam_width)
        cell = attention_wrapper.AttentionWrapper(
            cell=cell,
            attention_mechanism=attention_mechanism,
            attention_layer_size=attention_depth,
            alignment_history=with_alignment_history)
      cell_state = cell.zero_state(
          dtype=dtypes.float32, batch_size=batch_size_tensor * beam_width)
      if has_attention:
        cell_state = cell_state.clone(cell_state=initial_state)
      bsd = beam_search_decoder.BeamSearchDecoder(
          cell=cell,
          embedding=embedding,
          start_tokens=array_ops.fill([batch_size_tensor], start_token),
          end_token=end_token,
          initial_state=cell_state,
          beam_width=beam_width,
          output_layer=output_layer,
          length_penalty_weight=0.0,
          coverage_penalty_weight=coverage_penalty_weight)

      final_outputs, final_state, final_sequence_lengths = (
          decoder.dynamic_decode(
              bsd, output_time_major=time_major, maximum_iterations=max_out))

      def _t(shape):
        if time_major:
          return (shape[1], shape[0]) + shape[2:]
        return shape

      self.assertTrue(
          isinstance(final_outputs,
                     beam_search_decoder.FinalBeamSearchDecoderOutput))
      self.assertTrue(
          isinstance(final_state, beam_search_decoder.BeamSearchDecoderState))

      beam_search_decoder_output = final_outputs.beam_search_decoder_output
      self.assertEqual(
          _t((batch_size, None, beam_width)),
          tuple(beam_search_decoder_output.scores.get_shape().as_list()))
      self.assertEqual(
          _t((batch_size, None, beam_width)),
          tuple(final_outputs.predicted_ids.get_shape().as_list()))

      sess.run(variables.global_variables_initializer())
      sess_results = sess.run({
          'final_outputs': final_outputs,
          'final_state': final_state,
          'final_sequence_lengths': final_sequence_lengths
      })

      max_sequence_length = np.max(sess_results['final_sequence_lengths'])

      # A smoke test
      self.assertEqual(
          _t((batch_size, max_sequence_length, beam_width)),
          sess_results['final_outputs'].beam_search_decoder_output.scores.shape)
      self.assertEqual(
          _t((batch_size, max_sequence_length, beam_width)), sess_results[
              'final_outputs'].beam_search_decoder_output.predicted_ids.shape)

  def _testDynamicDecodeRNN(self, time_major, has_attention):
    encoder_sequence_length = [3, 2, 3, 1, 0]
    decoder_sequence_length = [2, 0, 1, 2, 3]
    batch_size = 5
    decoder_max_time = 4
    input_depth = 7
    cell_depth = 9
    attention_depth = 6
    vocab_size = 20
    end_token = vocab_size - 1
    start_token = 0
    embedding_dim = 50
    max_out = max(decoder_sequence_length)
    output_layer = layers_core.Dense(vocab_size, use_bias=True, activation=None)
    beam_width = 3

    with self.test_session() as sess:
      embedding = np.random.randn(vocab_size, embedding_dim).astype(np.float32)
      cell = core_rnn_cell.LSTMCell(cell_depth)
      if has_attention:
        inputs = np.random.randn(batch_size, decoder_max_time,
                                 input_depth).astype(np.float32)
        attention_mechanism = attention_wrapper.BahdanauAttention(
            num_units=attention_depth,
            memory=inputs,
            memory_sequence_length=encoder_sequence_length)
        cell = attention_wrapper.AttentionWrapper(
            cell=cell,
            attention_mechanism=attention_mechanism,
            attention_size=attention_depth,
            alignment_history=False)
      cell_state = cell.zero_state(
          dtype=dtypes.float32, batch_size=batch_size * beam_width)
      bsd = beam_search_decoder.BeamSearchDecoder(
          cell=cell,
          embedding=embedding,
          start_tokens=batch_size * [start_token],
          end_token=end_token,
          initial_state=cell_state,
          beam_width=beam_width,
          output_layer=output_layer,
          length_penalty_weight=0.0)

      final_outputs, final_state = decoder.dynamic_decode(
          bsd, output_time_major=time_major, maximum_iterations=max_out)

      def _t(shape):
        if time_major:
          return (shape[1], shape[0]) + shape[2:]
        return shape

      self.assertTrue(
          isinstance(final_outputs,
                     beam_search_decoder.FinalBeamSearchDecoderOutput))
      self.assertTrue(
          isinstance(final_state, beam_search_decoder.BeamSearchDecoderState))

      beam_search_decoder_output = final_outputs.beam_search_decoder_output
      self.assertEqual(
          _t((batch_size, None, beam_width)),
          tuple(beam_search_decoder_output.scores.get_shape().as_list()))
      self.assertEqual(
          _t((batch_size, None, beam_width)),
          tuple(final_outputs.predicted_ids.get_shape().as_list()))

      sess.run(variables.global_variables_initializer())
      sess_results = sess.run({
          'final_outputs': final_outputs,
          'final_state': final_state
      })

      # Mostly a smoke test
      time_steps = max_out
      self.assertEqual(
          _t((batch_size, time_steps, beam_width)),
          sess_results['final_outputs'].beam_search_decoder_output.scores.shape)
      self.assertEqual(
          _t((batch_size, time_steps, beam_width)), sess_results[
              'final_outputs'].beam_search_decoder_output.predicted_ids.shape)