Python tensorflow.get_seed方法代碼示例

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import get_seed [as 別名]
def AddPretrainedEmbeddings(self, index, embeddings_path, task_context):
    """Embeddings at the given index will be set to pretrained values."""

    def _Initializer(shape, dtype=tf.float32, partition_info=None):
      """Variable initializer that loads pretrained embeddings."""
      unused_dtype = dtype
      seed1, seed2 = tf.get_seed(self._seed)
      t = gen_parser_ops.word_embedding_initializer(
          vectors=embeddings_path,
          task_context=task_context,
          embedding_init=self._embedding_init,
          seed=seed1,
          seed2=seed2)

      t.set_shape(shape)
      return t

    self._pretrained_embeddings[index] = _Initializer 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import get_seed [as 別名]
def AddPretrainedEmbeddings(self, index, embeddings_path, task_context):
    """Embeddings at the given index will be set to pretrained values."""

    def _Initializer(shape, dtype=tf.float32, partition_info=None):
      """Variable initializer that loads pretrained embeddings."""
      unused_dtype = dtype
      seed1, seed2 = tf.get_seed(self._seed)
      t = gen_parser_ops.word_embedding_initializer(
          vectors=embeddings_path,
          task_context=task_context,
          embedding_init=self._embedding_init,
          cache_vectors_locally=False,
          seed=seed1,
          seed2=seed2)

      t.set_shape(shape)
      return t

    self._pretrained_embeddings[index] = _Initializer 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import get_seed [as 別名]
def add_embeddings(channel_id, feature_spec, seed=None):
  """Adds a variable for the embedding of a given fixed feature.

  Supports pre-trained or randomly initialized embeddings In both cases, extra
  vector is reserved for out-of-vocabulary words, so the embedding matrix has
  the size of [feature_spec.vocabulary_size + 1, feature_spec.embedding_dim].

  Args:
    channel_id: Numeric id of the fixed feature channel
    feature_spec: Feature spec protobuf of type FixedFeatureChannel
    seed: used for random initializer

  Returns:
    tf.Variable object corresponding to the embedding for that feature.

  Raises:
    RuntimeError: if more the pretrained embeddings are specified in resources
        containing more than one part.
  """
  check.Gt(feature_spec.embedding_dim, 0,
           'Embeddings requested for non-embedded feature: %s' % feature_spec)
  name = fixed_embeddings_name(channel_id)
  shape = [feature_spec.vocabulary_size + 1, feature_spec.embedding_dim]
  if feature_spec.HasField('pretrained_embedding_matrix'):
    if len(feature_spec.pretrained_embedding_matrix.part) > 1:
      raise RuntimeError('pretrained_embedding_matrix resource contains '
                         'more than one part:\n%s',
                         str(feature_spec.pretrained_embedding_matrix))
    if len(feature_spec.vocab.part) > 1:
      raise RuntimeError('vocab resource contains more than one part:\n%s',
                         str(feature_spec.vocab))
    seed1, seed2 = tf.get_seed(seed)
    embeddings = dragnn_ops.dragnn_embedding_initializer(
        embedding_input=feature_spec.pretrained_embedding_matrix.part[0]
        .file_pattern,
        vocab=feature_spec.vocab.part[0].file_pattern,
        scaling_coefficient=1.0,
        seed=seed1,
        seed2=seed2)
    return tf.get_variable(name, initializer=tf.reshape(embeddings, shape))
  else:
    return tf.get_variable(
        name,
        shape,
        initializer=tf.random_normal_initializer(
            stddev=1.0 / feature_spec.embedding_dim**.5, seed=seed)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import get_seed [as 別名]
def add_embeddings(channel_id, feature_spec, seed=None):
  """Adds a variable for the embedding of a given fixed feature.

  Supports pre-trained or randomly initialized embeddings In both cases, extra
  vector is reserved for out-of-vocabulary words, so the embedding matrix has
  the size of [feature_spec.vocabulary_size + 1, feature_spec.embedding_dim].

  Args:
    channel_id: Numeric id of the fixed feature channel
    feature_spec: Feature spec protobuf of type FixedFeatureChannel
    seed: used for random initializer

  Returns:
    tf.Variable object corresponding to the embedding for that feature.

  Raises:
    RuntimeError: if more the pretrained embeddings are specified in resources
        containing more than one part.
  """
  check.Gt(feature_spec.embedding_dim, 0,
           'Embeddings requested for non-embedded feature: %s' % feature_spec)
  name = fixed_embeddings_name(channel_id)
  shape = [feature_spec.vocabulary_size + 1, feature_spec.embedding_dim]
  if feature_spec.HasField('pretrained_embedding_matrix'):
    if len(feature_spec.pretrained_embedding_matrix.part) > 1:
      raise RuntimeError('pretrained_embedding_matrix resource contains '
                         'more than one part:\n%s',
                         str(feature_spec.pretrained_embedding_matrix))
    if len(feature_spec.vocab.part) > 1:
      raise RuntimeError('vocab resource contains more than one part:\n%s',
                         str(feature_spec.vocab))
    seed1, seed2 = tf.get_seed(seed)
    embeddings = syntaxnet_ops.word_embedding_initializer(
        vectors=feature_spec.pretrained_embedding_matrix.part[0].file_pattern,
        vocabulary=feature_spec.vocab.part[0].file_pattern,

        num_special_embeddings=1,
        embedding_init=1.0,
        seed=seed1,
        seed2=seed2)
    return tf.get_variable(
        name,
        initializer=tf.reshape(embeddings, shape),
        trainable=not feature_spec.is_constant)
  else:
    return tf.get_variable(
        name,
        shape,
        initializer=tf.random_normal_initializer(
            stddev=1.0 / feature_spec.embedding_dim**.5, seed=seed),
        trainable=not feature_spec.is_constant) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import get_seed [as 別名]
def add_embeddings(channel_id, feature_spec, seed=None):
  """Adds a variable for the embedding of a given fixed feature.

  Supports pre-trained or randomly initialized embeddings In both cases, extra
  vector is reserved for out-of-vocabulary words, so the embedding matrix has
  the size of [feature_spec.vocabulary_size + 1, feature_spec.embedding_dim].

  Args:
    channel_id: Numeric id of the fixed feature channel
    feature_spec: Feature spec protobuf of type FixedFeatureChannel
    seed: used for random initializer

  Returns:
    tf.Variable object corresponding to the embedding for that feature.

  Raises:
    RuntimeError: if more the pretrained embeddings are specified in resources
        containing more than one part.
  """
  check.Gt(feature_spec.embedding_dim, 0,
           'Embeddings requested for non-embedded feature: %s' % feature_spec)
  name = fixed_embeddings_name(channel_id)
  shape = [feature_spec.vocabulary_size + 1, feature_spec.embedding_dim]
  if feature_spec.HasField('pretrained_embedding_matrix'):
    if len(feature_spec.pretrained_embedding_matrix.part) > 1:
      raise RuntimeError('pretrained_embedding_matrix resource contains '
                         'more than one part:\n%s',
                         str(feature_spec.pretrained_embedding_matrix))
    if len(feature_spec.vocab.part) > 1:
      raise RuntimeError('vocab resource contains more than one part:\n%s',
                         str(feature_spec.vocab))
    seed1, seed2 = tf.get_seed(seed)
    embeddings = syntaxnet_ops.word_embedding_initializer(
        vectors=feature_spec.pretrained_embedding_matrix.part[0].file_pattern,
        vocabulary=feature_spec.vocab.part[0].file_pattern,
        num_special_embeddings=1,
        embedding_init=1.0,
        seed=seed1,
        seed2=seed2)
    return tf.get_variable(name, initializer=tf.reshape(embeddings, shape))
  else:
    return tf.get_variable(
        name,
        shape,
        initializer=tf.random_normal_initializer(
            stddev=1.0 / feature_spec.embedding_dim**.5, seed=seed)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import get_seed [as 別名]
def add_embeddings(channel_id, feature_spec, seed=None):
  """Adds a variable for the embedding of a given fixed feature.

  Supports pre-trained or randomly initialized embeddings In both cases, extra
  vector is reserved for out-of-vocabulary words, so the embedding matrix has
  the size of [feature_spec.vocabulary_size + 1, feature_spec.embedding_dim].

  Args:
    channel_id: Numeric id of the fixed feature channel
    feature_spec: Feature spec protobuf of type FixedFeatureChannel
    seed: used for random initializer

  Returns:
    tf.Variable object corresponding to the embedding for that feature.

  Raises:
    RuntimeError: if more the pretrained embeddings are specified in resources
        containing more than one part.
  """
  check.Gt(feature_spec.embedding_dim, 0,
           'Embeddings requested for non-embedded feature: %s' % feature_spec)
  name = fixed_embeddings_name(channel_id)
  row_num = feature_spec.vocabulary_size + 1
  shape = [row_num, feature_spec.embedding_dim]
  if feature_spec.HasField('pretrained_embedding_matrix'):
    if len(feature_spec.pretrained_embedding_matrix.part) > 1:
      raise RuntimeError('pretrained_embedding_matrix resource contains '
                         'more than one part:\n%s',
                         str(feature_spec.pretrained_embedding_matrix))
    if len(feature_spec.vocab.part) > 1:
      raise RuntimeError('vocab resource contains more than one part:\n%s',
                         str(feature_spec.vocab))
    seed1, seed2 = tf.get_seed(seed)
    embeddings = syntaxnet_ops.word_embedding_initializer(
        vectors=feature_spec.pretrained_embedding_matrix.part[0].file_pattern,
        vocabulary=feature_spec.vocab.part[0].file_pattern,
        override_num_embeddings=row_num,

        embedding_init=0.0,  # zero out rows with no pretrained values
        seed=seed1,
        seed2=seed2)
    return tf.get_variable(
        name,
        initializer=tf.reshape(embeddings, shape),
        trainable=not feature_spec.is_constant)
  else:
    return tf.get_variable(
        name,
        shape,
        initializer=tf.random_normal_initializer(
            stddev=1.0 / feature_spec.embedding_dim**.5, seed=seed),
        trainable=not feature_spec.is_constant)