Python gen_array_ops.reverse_sequence方法代码示例

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import reverse_sequence [as 别名]
def reverse_sequence(input,
                     seq_lengths,
                     seq_axis=None,
                     batch_axis=None,
                     name=None,
                     seq_dim=None,
                     batch_dim=None):
  seq_axis = deprecation.deprecated_argument_lookup("seq_axis", seq_axis,
                                                    "seq_dim", seq_dim)
  batch_axis = deprecation.deprecated_argument_lookup("batch_axis", batch_axis,
                                                      "batch_dim", batch_dim)
  return gen_array_ops.reverse_sequence(
      input=input,
      seq_lengths=seq_lengths,
      seq_dim=seq_axis,
      batch_dim=batch_axis,
      name=name)
# pylint: enable=redefined-builtin 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import reverse_sequence [as 别名]
def reverse_sequence(input,
                     seq_lengths,
                     seq_axis=None,
                     batch_axis=None,
                     name=None,
                     seq_dim=None,
                     batch_dim=None):
  seq_axis = deprecation.deprecated_argument_lookup("seq_axis", seq_axis,
                                                    "seq_dim", seq_dim)
  batch_axis = deprecation.deprecated_argument_lookup("batch_axis", batch_axis,
                                                      "batch_dim", batch_dim)
  return gen_array_ops.reverse_sequence(
      input=input,
      seq_lengths=seq_lengths,
      seq_dim=seq_axis,
      batch_dim=batch_axis,
      name=name)


# pylint: enable=redefined-builtin 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import reverse_sequence [as 别名]
def crf_decode(potentials, transition_params, sequence_length):
  """Decode the highest scoring sequence of tags in TensorFlow.

  This is a function for tensor.

  Args:
    potentials: A [batch_size, max_seq_len, num_tags] tensor, matrix of
              unary potentials.
    transition_params: A [num_tags, num_tags] tensor, matrix of
              binary potentials.
    sequence_length: A [batch_size] tensor, containing sequence lengths.

  Returns:
    decode_tags: A [batch_size, max_seq_len] tensor, with dtype tf.int32.
                Contains the highest scoring tag indicies.
    best_score: A [batch_size] tensor, containing the score of decode_tags.
  """
  # For simplicity, in shape comments, denote:
  # 'batch_size' by 'B', 'max_seq_len' by 'T' , 'num_tags' by 'O' (output).
  num_tags = potentials.get_shape()[2].value

  # Computes forward decoding. Get last score and backpointers.
  crf_fwd_cell = CrfDecodeForwardRnnCell(transition_params)
  initial_state = array_ops.slice(potentials, [0, 0, 0], [-1, 1, -1])
  initial_state = array_ops.squeeze(initial_state, axis=[1])      # [B, O]
  inputs = array_ops.slice(potentials, [0, 1, 0], [-1, -1, -1])   # [B, T-1, O]
  backpointers, last_score = rnn.dynamic_rnn(
      crf_fwd_cell,
      inputs=inputs,
      sequence_length=sequence_length - 1,
      initial_state=initial_state,
      time_major=False,
      dtype=dtypes.int32)             # [B, T - 1, O], [B, O]
  backpointers = gen_array_ops.reverse_sequence(
      backpointers, sequence_length - 1, seq_dim=1)               # [B, T-1, O]

  # Computes backward decoding. Extract tag indices from backpointers.
  crf_bwd_cell = CrfDecodeBackwardRnnCell(num_tags)
  initial_state = math_ops.cast(math_ops.argmax(last_score, axis=1),
                                dtype=dtypes.int32)               # [B]
  initial_state = array_ops.expand_dims(initial_state, axis=-1)   # [B, 1]
  decode_tags, _ = rnn.dynamic_rnn(
      crf_bwd_cell,
      inputs=backpointers,
      sequence_length=sequence_length - 1,
      initial_state=initial_state,
      time_major=False,
      dtype=dtypes.int32)           # [B, T - 1, 1]
  decode_tags = array_ops.squeeze(decode_tags, axis=[2])           # [B, T - 1]
  decode_tags = array_ops.concat([initial_state, decode_tags], axis=1)  # [B, T]
  decode_tags = gen_array_ops.reverse_sequence(
      decode_tags, sequence_length, seq_dim=1)                     # [B, T]

  best_score = math_ops.reduce_max(last_score, axis=1)             # [B]
  return decode_tags, best_score 

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import reverse_sequence [as 别名]
def crf_decode(potentials, transition_params, sequence_length):
        """Decode the highest scoring sequence of tags in TensorFlow.
        This is a function for tensor.
        Args:
        potentials: A [batch_size, max_seq_len, num_tags] tensor, matrix of
                  unary potentials.
        transition_params: A [num_tags, num_tags] tensor, matrix of
                  binary potentials.
        sequence_length: A [batch_size] tensor, containing sequence lengths.
        Returns:
        decode_tags: A [batch_size, max_seq_len] tensor, with dtype tf.int32.
                    Contains the highest scoring tag indicies.
        best_score: A [batch_size] tensor, containing the score of decode_tags.
        """
        # For simplicity, in shape comments, denote:
        # 'batch_size' by 'B', 'max_seq_len' by 'T' , 'num_tags' by 'O' (output).
        num_tags = potentials.get_shape()[2].value

        # Computes forward decoding. Get last score and backpointers.
        crf_fwd_cell = CrfDecodeForwardRnnCell(transition_params)
        initial_state = array_ops.slice(potentials, [0, 0, 0], [-1, 1, -1])
        initial_state = array_ops.squeeze(initial_state, axis=[1])  # [B, O]
        inputs = array_ops.slice(potentials, [0, 1, 0], [-1, -1, -1])  # [B, T-1, O]
        backpointers, last_score = rnn.dynamic_rnn(
            crf_fwd_cell,
            inputs=inputs,
            sequence_length=sequence_length - 1,
            initial_state=initial_state,
            time_major=False,
            dtype=dtypes.int32)  # [B, T - 1, O], [B, O]
        backpointers = gen_array_ops.reverse_sequence(backpointers, sequence_length - 1, seq_dim=1)  # [B, T-1, O]

        # Computes backward decoding. Extract tag indices from backpointers.
        crf_bwd_cell = CrfDecodeBackwardRnnCell(num_tags)
        initial_state = math_ops.cast(math_ops.argmax(last_score, axis=1), dtype=dtypes.int32)  # [B]
        initial_state = array_ops.expand_dims(initial_state, axis=-1)  # [B, 1]
        decode_tags, _ = rnn.dynamic_rnn(
            crf_bwd_cell,
            inputs=backpointers,
            sequence_length=sequence_length - 1,
            initial_state=initial_state,
            time_major=False,
            dtype=dtypes.int32)  # [B, T - 1, 1]
        decode_tags = array_ops.squeeze(decode_tags, axis=[2])  # [B, T - 1]
        decode_tags = array_ops.concat([initial_state, decode_tags], axis=1)  # [B, T]
        decode_tags = gen_array_ops.reverse_sequence(decode_tags, sequence_length, seq_dim=1)  # [B, T]

        best_score = math_ops.reduce_max(last_score, axis=1)  # [B]
        return decode_tags, best_score