Python tensorflow.to_int64方法代碼示例

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def argmax_with_score(logits, axis=None):
  """Argmax along with the value."""
  axis = axis or len(logits.get_shape()) - 1
  predictions = tf.argmax(logits, axis=axis)

  logits_shape = shape_list(logits)
  prefix_shape, vocab_size = logits_shape[:-1], logits_shape[-1]
  prefix_size = 1
  for d in prefix_shape:
    prefix_size *= d

  # Flatten to extract scores
  flat_logits = tf.reshape(logits, [prefix_size, vocab_size])
  flat_predictions = tf.reshape(predictions, [prefix_size])
  flat_indices = tf.stack(
      [tf.range(tf.to_int64(prefix_size)),
       tf.to_int64(flat_predictions)],
      axis=1)
  flat_scores = tf.gather_nd(flat_logits, flat_indices)

  # Unflatten
  scores = tf.reshape(flat_scores, prefix_shape)

  return predictions, scores 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def bilstm_representation_raw(self, item, indices, re_use_lstm, name='lstm'):
        """Creates a representation graph which retrieves a text item (represented by its word embeddings) and returns
        a vector-representation

        :param item: the text item. Can be question or (good/bad) answer
        :param sequence_length: maximum length of the text item
        :param re_use_lstm: should be False for the first call, True for al subsequent ones to get the same lstm
        variables
        :return: representation tensor
        """
        tensor_non_zero_token = non_zero_tokens(tf.to_float(indices))
        sequence_length = tf.to_int64(tf.reduce_sum(tensor_non_zero_token, 1))

        with tf.variable_scope(name, reuse=re_use_lstm):
            output, _last = tf.nn.bidirectional_dynamic_rnn(
                self.lstm_cell_forward,
                self.lstm_cell_backward,
                item,
                dtype=tf.float32,
                sequence_length=sequence_length
            )

            return tf.concat(2, output) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def _build_training_graph(self, logits, labels, learning_rate):
        """
        Build the training graph.
        Args:
            logits: Logits tensor, float - [batch_size, class_count].
            labels: Labels tensor, int32 - [batch_size], with values in the range
                [0, class_count).
            learning_rate: The learning rate for the optimization.
        Returns:
            train_op: The Op for training.
            loss: The Op for calculating loss.
        """
        # Create an operation that calculates loss.
        labels = tf.to_int64(labels)
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
            logits=logits, labels=labels, name='xentropy')
        loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
        train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)

        correct_predict = tf.nn.in_top_k(logits, labels, 1)
        accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))

        return train_op, loss, accuracy 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def listMLE(self, output, target_indexs, target_rels, name=None):
		loss = None
		with ops.name_scope(name, "listMLE",[output] + target_indexs + target_rels):
			output = tf.nn.l2_normalize(output, 1)
			loss = -1.0 * math_ops.reduce_sum(output,1)
			print(loss.get_shape())
			exp_output = tf.exp(output)
			exp_output_table = tf.reshape(exp_output,[-1])
			print(exp_output.get_shape())
			print(exp_output_table.get_shape())
			sum_exp_output = math_ops.reduce_sum(exp_output,1)
			loss = tf.add(loss, tf.log(sum_exp_output))
			#compute MLE
			for i in xrange(self.rank_list_size-1):
				idx = target_indexs[i] + tf.to_int64(self.batch_index_bias)
				y_i = embedding_ops.embedding_lookup(exp_output_table, idx)
				#y_i = tf.gather_nd(exp_output, idx)
				sum_exp_output = tf.subtract(sum_exp_output, y_i)
				loss = tf.add(loss, tf.log(sum_exp_output))
		batch_size = tf.shape(target_rels[0])[0]
		return math_ops.reduce_sum(loss) / math_ops.cast(batch_size, dtypes.float32) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def horizontal_cell(images, num_filters_out, cell_fw, cell_bw, keep_prob=1.0, scope=None):
  """Run an LSTM bidirectionally over all the rows of each image.

  Args:
    images: (num_images, height, width, depth) tensor
    num_filters_out: output depth
    scope: optional scope name

  Returns:
    (num_images, height, width, num_filters_out) tensor, where
  """
  with tf.variable_scope(scope, "HorizontalGru", [images]):
    sequence = images_to_sequence(images)

    shapeT = tf.shape(sequence)
    sequence_length = shapeT[0]
    batch_sizeRNN = shapeT[1]
    sequence_lengths = tf.to_int64(
      tf.fill([batch_sizeRNN], sequence_length))
    forward_drop1 = DropoutWrapper(cell_fw, output_keep_prob=keep_prob)
    backward_drop1 = DropoutWrapper(cell_bw, output_keep_prob=keep_prob)
    rnn_out1, _ = tf.nn.bidirectional_dynamic_rnn(forward_drop1, backward_drop1, sequence, dtype=tf.float32,
                                                  sequence_length=sequence_lengths, time_major=True,
                                                  swap_memory=True, scope=scope)
    rnn_out1 = tf.concat(rnn_out1, 2)
    rnn_out1 = tf.reshape(rnn_out1, shape=[-1, batch_sizeRNN, 2, num_filters_out])
    output_sequence = tf.reduce_sum(rnn_out1, axis=2)
    batch_size=tf.shape(images)[0]
    output = sequence_to_images(output_sequence, batch_size)
    return output 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def get_text(self, ids):
    """Returns a string corresponding to a sequence of character ids.

        Args:
          ids: a tensor with shape [batch_size, max_sequence_length]
        """
    return tf.reduce_join(
        self.table.lookup(tf.to_int64(ids)), reduction_indices=1) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def tensors_to_item(self, keys_to_tensors):
    return tf.to_int64(
        self._num_of_views * keys_to_tensors[self._original_width_key] /
        keys_to_tensors[self._width_key]) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def index_last_dim_with_indices(x, indices):
  """Use indices to index into the last axis of x.

  This can be useful for recovering the actual probabilities of a sample from a
  probability distribution.

  Args:
    x: Tensor, n-d.
    indices: Tensor, (n-1)-d, where the dimension sizes match the first (n-1)
      dimensions of x. The values of indices will be used to index into the last
      axis of x.

  Returns:
    Tensor, (n-1)-d.
  """
  assert len(x.shape) == len(indices.shape) + 1

  x_shape = shape_list(x)
  vocab_size = x_shape[-1]

  flat_x = tf.reshape(x, [list_product(x_shape[:-1]), vocab_size])
  flat_indices = tf.reshape(indices, [list_product(x_shape[:-1])])

  idx = tf.stack(
      [
          tf.range(tf.to_int64(shape_list(flat_indices)[0])),
          tf.to_int64(flat_indices)
      ],
      axis=1)
  flat_x_idx = tf.gather_nd(flat_x, idx)

  x_idx = tf.reshape(flat_x_idx, x_shape[:-1])

  return x_idx 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def preprocess_example(self, example, mode, _):
    # Resize from usual size ~1350x60 to 90x4 in this test.
    img = example["inputs"]
    img = tf.to_int64(
        tf.image.resize_images(img, [90, 4], tf.image.ResizeMethod.AREA))
    img = tf.image.per_image_standardization(img)
    example["inputs"] = img
    return example 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def resize_video_frames(images, size):
  resized_images = []
  for image in images:
    resized_images.append(
        tf.to_int64(tf.image.resize_images(
            image, [size, size], tf.image.ResizeMethod.BILINEAR)))
  return resized_images 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def preprocess_example(self, example, mode, unused_hparams):
    example["inputs"].set_shape([_CIFAR10_IMAGE_SIZE, _CIFAR10_IMAGE_SIZE, 3])
    example["inputs"] = tf.to_int64(example["inputs"])
    return example 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def resize_by_area(img, size):
  """image resize function used by quite a few image problems."""
  return tf.to_int64(
      tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA)) 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def make_multiscale(image, resolutions,
                    resize_method=tf.image.ResizeMethod.BICUBIC,
                    num_channels=3):
  """Returns list of scaled images, one for each resolution.

  Args:
    image: Tensor of shape [height, height, num_channels].
    resolutions: List of heights that image's height is resized to.
    resize_method: tf.image.ResizeMethod.
    num_channels: Number of channels in image.

  Returns:
    List of Tensors, one for each resolution with shape given by
    [resolutions[i], resolutions[i], num_channels].
  """
  scaled_images = []
  for height in resolutions:
    scaled_image = tf.image.resize_images(
        image,
        size=[height, height],  # assuming that height = width
        method=resize_method)
    scaled_image = tf.to_int64(scaled_image)
    scaled_image.set_shape([height, height, num_channels])
    scaled_images.append(scaled_image)

  return scaled_images 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def make_multiscale_dilated(image, resolutions, num_channels=3):
  """Returns list of scaled images, one for each resolution.

  Resizes by skipping every nth pixel.

  Args:
    image: Tensor of shape [height, height, num_channels].
    resolutions: List of heights that image's height is resized to. The function
      assumes VALID padding, so the original image's height must be divisible
      by each resolution's height to return the exact resolution size.
    num_channels: Number of channels in image.

  Returns:
    List of Tensors, one for each resolution with shape given by
    [resolutions[i], resolutions[i], num_channels] if resolutions properly
    divide the original image's height; otherwise shape height and width is up
    to valid skips.
  """
  image_height = common_layers.shape_list(image)[0]
  scaled_images = []
  for height in resolutions:
    dilation_rate = image_height // height  # assuming height = width
    scaled_image = image[::dilation_rate, ::dilation_rate]
    scaled_image = tf.to_int64(scaled_image)
    scaled_image.set_shape([None, None, num_channels])
    scaled_images.append(scaled_image)
  return scaled_images 

# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import to_int64 [as 別名]
def preprocess_example(self, example, mode, _):
    # Just resize with area.
    if self._was_reversed:
      example["inputs"] = tf.to_int64(
          tf.image.resize_images(example["inputs"], self.rescale_size,
                                 tf.image.ResizeMethod.AREA))
    else:
      example = imagenet_preprocess_example(example, mode)
      example["inputs"] = tf.to_int64(
          tf.image.resize_images(example["inputs"], self.rescale_size))
    return example