Python v1.int64方法代碼示例

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def encode_knowledge_bottom(self, features):
    tf.logging.info("Encoding knowledge " + str(self.triple_num))
    # Make sure this is embeddings for triples
    # <tf.float32>[batch_size, triple_num*max_triple_length, 1, emb_dim]
    fact_embedding = features["encoded_triples"]
    # [batch_size, triple_num*max_triple_length, emb_dim]
    fact_embedding = tf.squeeze(fact_embedding, 2)

    kb_shape = common_layers.shape_list(fact_embedding)
    batch_size = kb_shape[0]
    embed_dim = kb_shape[2]
    # <tf.float32>[batch_size*triple_num, max_triple_length, emb_dim]
    re_fact_embedding = tf.reshape(
        fact_embedding, [batch_size * self.triple_num, -1, embed_dim],
        name="reshape_fact_embedding")

    # <tf.int64>[batch_size, triple_num]
    input_fact_lengths = features["triple_lens"]
    # Stack the fact lengths.
    # <tf.int64>[batch_size*max_triple_num]
    re_fact_lengths = tf.reshape(
        input_fact_lengths, [batch_size * self.triple_num, 1],
        name="reshape_fact_lengths")

    return re_fact_embedding, re_fact_lengths 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def compute_last_embedding(input_embeddings, input_lengths, hparams):
  """Computes average of last K embedding.

  Args:
    input_embeddings: <tf.float32>[bs, max_seq_len, emb_dim]
    input_lengths: <tf.int64>[bs, 1]
    hparams: model hparams

  Returns:
    last_k_embedding: <tf.float32>[bs, emb_dim]
  """
  max_seq_len = tf.shape(input_embeddings)[1]
  # <tf.float32>[bs, 1, max_seq_len]
  mask = tf.sequence_mask(input_lengths, max_seq_len, dtype=tf.float32)
  del_mask = tf.sequence_mask(
      input_lengths - hparams.last_k, max_seq_len, dtype=tf.float32)
  final_mask = mask - del_mask
  # <tf.float32>[bs, 1, emb_dim]
  sum_embedding = tf.matmul(final_mask, input_embeddings)
  # <tf.float32>[bs, 1, emb_dim]
  last_k_embedding = sum_embedding / tf.to_float(
      tf.expand_dims(
          tf.ones([tf.shape(input_embeddings)[0], 1]) * hparams.last_k, 2))
  # <tf.float32>[bs, dim]
  return tf.squeeze(last_k_embedding, 1) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def compute_max_pool_embedding(input_embeddings, input_lengths):
  """Computes max pool embedding.

  Args:
    input_embeddings: <tf.float32>[bs, max_seq_len, emb_dim]
    input_lengths: <tf.int64>[bs, 1]

  Returns:
    max_pool_embedding: <tf.float32>[bs, emb_dim]
  """
  max_seq_len = tf.shape(input_embeddings)[1]
  # <tf.float32>[bs, max_seq_len]
  mask = 1.0 - tf.sequence_mask(input_lengths, max_seq_len, dtype=tf.float32)
  mask = tf.squeeze(mask * (-1e-6), 1)
  mask = tf.expand_dims(mask, 2)
  # <tf.float32>[bs, emb_dim]
  max_pool_embedding = tf.reduce_max(input_embeddings + mask, 1)
  # <tf.float32>[bs, dim]
  return max_pool_embedding 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def compute_average_embedding(input_embeddings, input_lengths):
  """Computes bag-of-words embedding.

  Args:
    input_embeddings: <tf.float32>[bs, max_seq_len, emb_dim]
    input_lengths: <tf.int64>[bs, 1]

  Returns:
    bow_embedding: <tf.float32>[bs, emb_dim]
  """
  max_seq_len = tf.shape(input_embeddings)[1]
  # <tf.float32>[bs, 1, max_seq_len]
  mask = tf.sequence_mask(input_lengths, max_seq_len, dtype=tf.float32)
  # <tf.float32>[bs, 1, emb_dim]
  sum_embedding = tf.matmul(mask, input_embeddings)
  # <tf.float32>[bs, 1, emb_dim]
  avg_embedding = sum_embedding / tf.to_float(tf.expand_dims(input_lengths, 2))
  # <tf.float32>[bs, dim]
  return tf.squeeze(avg_embedding, 1) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def testDatasetPacking(self):
    dataset = tf.data.Dataset.from_generator(
        example_generator,
        output_types={"inputs": tf.int64, "targets": tf.int64},
        output_shapes={"inputs": tf.TensorShape((None,)),
                       "targets": tf.TensorShape((None,))}
    )
    dataset = generator_utils.pack_dataset(
        dataset, length=5, keys=("inputs", "targets"), use_custom_ops=False)

    with tf.Session().as_default() as sess:
      batch = dataset.make_one_shot_iterator().get_next()
      for reference in reference_packing():
        example = sess.run(batch)
        self.assertAllEqual(set(example.keys()), set(reference.keys()))
        for k in reference:
          self.assertAllEqual(example[k], reference[k]) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def example_reading_spec(self):
    data_fields, data_items_to_decoders = (
        super(ImageVqav2Tokens10kLabels3k, self).example_reading_spec())
    data_fields["image/image_id"] = tf.FixedLenFeature((), tf.int64)
    data_fields["image/question_id"] = tf.FixedLenFeature((), tf.int64)
    data_fields["image/question"] = tf.FixedLenSequenceFeature(
        (), tf.int64, allow_missing=True)
    data_fields["image/answer"] = tf.FixedLenSequenceFeature(
        (), tf.int64, allow_missing=True)

    slim = contrib.slim()
    data_items_to_decoders["question"] = slim.tfexample_decoder.Tensor(
        "image/question")
    data_items_to_decoders["targets"] = slim.tfexample_decoder.Tensor(
        "image/answer")
    return data_fields, data_items_to_decoders 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def process_rewards(self, rewards):
    """Clips the rewards, optionally rounds them and casts to integer.

    Args:
      rewards: numpy array of raw (float) rewards.

    Returns:
      processed_rewards: numpy array of np.int64
    """

    min_reward, max_reward = self.reward_range

    # Clips at min and max reward.
    rewards = np.clip(rewards, min_reward, max_reward)

    if self._discrete_rewards:
      # Round to (nearest) int and convert to integral type.
      rewards = np.around(rewards, decimals=0).astype(np.int64)
    return rewards 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def example_reading_spec(self):
    """Return a mix of env and video data fields and decoders."""
    slim = contrib.slim()
    video_fields, video_decoders = (
        video_utils.VideoProblem.example_reading_spec(self))
    env_fields, env_decoders = (
        gym_env_problem.GymEnvProblem.example_reading_spec(self))

    # Remove raw observations field since we want to capture them as videos.
    env_fields.pop(env_problem.OBSERVATION_FIELD)
    env_decoders.pop(env_problem.OBSERVATION_FIELD)

    # Add frame number spec and decoder.
    env_fields[_FRAME_NUMBER_FIELD] = tf.FixedLenFeature((1,), tf.int64)
    env_decoders[_FRAME_NUMBER_FIELD] = slim.tfexample_decoder.Tensor(
        _FRAME_NUMBER_FIELD)

    # Add video fields and decoders
    env_fields.update(video_fields)
    env_decoders.update(video_decoders)
    return env_fields, env_decoders 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def _init_graph(self):
    """Initialize computation graph for tensorflow.
    """
    with self.graph.as_default():
      self.refiner = im.ImNet(dim=self.dim,
                              in_features=self.codelen,
                              out_features=self.out_features,
                              num_filters=self.num_filters)
      self.global_step = tf.get_variable('global_step', shape=[],
                                         dtype=tf.int64)

      self.pts_ph = tf.placeholder(tf.float32, shape=[self.point_batch, 3])
      self.lat_ph = tf.placeholder(tf.float32, shape=[self.codelen])

      lat = tf.broadcast_to(self.lat_ph[tf.newaxis],
                            [self.point_batch, self.codelen])
      code = tf.concat((self.pts_ph, lat), axis=-1)  # [pb, 3+c]

      vals = self.refiner(code, training=False)  # [pb, 1]
      self.vals = tf.squeeze(vals, axis=1)  # [pb]
      self.saver = tf.train.Saver()
      self.sess = tf.Session()
      self.saver.restore(self.sess, self.ckpt) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def testDecodeExampleWithInt64Tensor(self):
    np_array = np.random.randint(1, 10, size=(2, 3, 1))

    example = tf.train.Example(
        features=tf.train.Features(feature={
            'array': self._EncodedInt64Feature(np_array),
        }))

    serialized_example = example.SerializeToString()

    with self.cached_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'array': parsing_ops.FixedLenFeature(np_array.shape, tf.int64)
      }
      items_to_handlers = {
          'array': tfexample_decoder.Tensor('array'),
      }
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_array] = decoder.decode(serialized_example, ['array'])
      self.assertAllEqual(tf_array.eval(), np_array) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def testDecodeExampleWithVarLenTensor(self):
    np_array = np.array([[[1], [2], [3]], [[4], [5], [6]]])

    example = tf.train.Example(
        features=tf.train.Features(feature={
            'labels': self._EncodedInt64Feature(np_array),
        }))

    serialized_example = example.SerializeToString()

    with self.cached_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'labels': parsing_ops.VarLenFeature(dtype=tf.int64),
      }
      items_to_handlers = {
          'labels': tfexample_decoder.Tensor('labels'),
      }
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_labels] = decoder.decode(serialized_example, ['labels'])
      labels = tf_labels.eval()
      self.assertAllEqual(labels, np_array.flatten()) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def testDecodeExampleWithFixLenTensorWithShape(self):
    np_array = np.array([[1, 2, 3], [4, 5, 6]])

    example = tf.train.Example(
        features=tf.train.Features(feature={
            'labels': self._EncodedInt64Feature(np_array),
        }))

    serialized_example = example.SerializeToString()

    with self.cached_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'labels': parsing_ops.FixedLenFeature(np_array.shape, dtype=tf.int64),
      }
      items_to_handlers = {
          'labels': tfexample_decoder.Tensor('labels', shape=np_array.shape),
      }
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_labels] = decoder.decode(serialized_example, ['labels'])
      labels = tf_labels.eval()
      self.assertAllEqual(labels, np_array) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def testDecodeExampleWithVarLenTensorToDense(self):
    np_array = np.array([[1, 2, 3], [4, 5, 6]])
    example = tf.train.Example(
        features=tf.train.Features(feature={
            'labels': self._EncodedInt64Feature(np_array),
        }))

    serialized_example = example.SerializeToString()

    with self.cached_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'labels': parsing_ops.VarLenFeature(dtype=tf.int64),
      }
      items_to_handlers = {
          'labels': tfexample_decoder.Tensor('labels', shape=np_array.shape),
      }
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_labels] = decoder.decode(serialized_example, ['labels'])
      labels = tf_labels.eval()
      self.assertAllEqual(labels, np_array) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def serving_input_receiver_fn():
  """Creates an input function for serving."""
  seq_len = FLAGS.max_seq_length
  serialized_example = tf.placeholder(
      dtype=tf.string, shape=[None], name="serialized_example")
  features = {
      "input_ids": tf.FixedLenFeature([seq_len], dtype=tf.int64),
      "input_mask": tf.FixedLenFeature([seq_len], dtype=tf.int64),
      "segment_ids": tf.FixedLenFeature([seq_len], dtype=tf.int64),
  }
  feature_map = tf.parse_example(serialized_example, features=features)
  feature_map["is_real_example"] = tf.constant(1, dtype=tf.int32)
  feature_map["label_ids"] = tf.constant(0, dtype=tf.int32)

  # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
  # So cast all int64 to int32.
  for name in feature_map.keys():
    t = feature_map[name]
    if t.dtype == tf.int64:
      t = tf.to_int32(t)
    feature_map[name] = t

  return tf.estimator.export.ServingInputReceiver(
      features=feature_map, receiver_tensors=serialized_example) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import int64 [as 別名]
def categorical_sample(logits, dtype=tf.int32,
                       sample_shape=(), seed=None):
  """Samples from categorical distribution."""
  logits = tf.convert_to_tensor(logits, name="logits")
  event_size = tf.shape(logits)[-1]
  batch_shape_tensor = tf.shape(logits)[:-1]
  def _sample_n(n):
    """Sample vector of categoricals."""
    if logits.shape.ndims == 2:
      logits_2d = logits
    else:
      logits_2d = tf.reshape(logits, [-1, event_size])
    sample_dtype = tf.int64 if logits.dtype.size > 4 else tf.int32
    draws = tf.multinomial(
        logits_2d, n, seed=seed, output_dtype=sample_dtype)
    draws = tf.reshape(
        tf.transpose(draws),
        tf.concat([[n], batch_shape_tensor], 0))
    return tf.cast(draws, dtype)
  return _call_sampler(_sample_n, sample_shape)