Python array_ops.reverse方法代码示例

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def flip_left_right(image):
  """Flip an image horizontally (left to right).

  Outputs the contents of `image` flipped along the second dimension, which is
  `width`.

  See also `reverse()`.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  image = control_flow_ops.with_dependencies(
      _Check3DImage(image, require_static=False), image)
  return fix_image_flip_shape(image, array_ops.reverse(image, [1])) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def flip_up_down(image):
  """Flip an image horizontally (upside down).

  Outputs the contents of `image` flipped along the first dimension, which is
  `height`.

  See also `reverse()`.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  image = control_flow_ops.with_dependencies(
      _Check3DImage(image, require_static=False), image)
  return fix_image_flip_shape(image, array_ops.reverse(image, [0])) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def reverse(x, axes):
  """Reverse a tensor along the specified axes.

  Arguments:
      x: Tensor to reverse.
      axes: Integer or iterable of integers.
          Axes to reverse.

  Returns:
      A tensor.
  """
  if isinstance(axes, int):
    axes = [axes]
  return array_ops.reverse(x, axes)


# VALUE MANIPULATION 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def testUnknownDims(self):
    data_t = tf.placeholder(tf.float32)
    dims_known_t = tf.placeholder(tf.bool, shape=[3])
    reverse_known_t = tf.reverse(data_t, dims_known_t)
    self.assertEqual(3, reverse_known_t.get_shape().ndims)

    dims_unknown_t = tf.placeholder(tf.bool)
    reverse_unknown_t = tf.reverse(data_t, dims_unknown_t)
    self.assertIs(None, reverse_unknown_t.get_shape().ndims)

    data_2d_t = tf.placeholder(tf.float32, shape=[None, None])
    dims_2d_t = tf.placeholder(tf.bool, shape=[2])
    reverse_2d_t = tf.reverse(data_2d_t, dims_2d_t)
    self.assertEqual(2, reverse_2d_t.get_shape().ndims)

    dims_3d_t = tf.placeholder(tf.bool, shape=[3])
    with self.assertRaisesRegexp(ValueError, "must be rank 3"):
      tf.reverse(data_2d_t, dims_3d_t) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def _reverse2DimAuto(self, np_dtype):
    x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np_dtype)

    for use_gpu in [False, True]:
      with self.test_session(use_gpu=use_gpu):
        x_tf_1 = array_ops.reverse_v2(x_np, [0]).eval()
        x_tf_2 = array_ops.reverse_v2(x_np, [-2]).eval()
        x_tf_3 = array_ops.reverse_v2(x_np, [1]).eval()
        x_tf_4 = array_ops.reverse_v2(x_np, [-1]).eval()
        x_tf_5 = array_ops.reverse_v2(x_np, [1, 0]).eval()
        self.assertAllEqual(x_tf_1, np.asarray(x_np)[::-1, :])
        self.assertAllEqual(x_tf_2, np.asarray(x_np)[::-1, :])
        self.assertAllEqual(x_tf_3, np.asarray(x_np)[:, ::-1])
        self.assertAllEqual(x_tf_4, np.asarray(x_np)[:, ::-1])
        self.assertAllEqual(x_tf_5, np.asarray(x_np)[::-1, ::-1])

  # This is the version of reverse that uses axis indices rather than
  # bool tensors
  # TODO(b/32254538): Change this test to use array_ops.reverse 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def random_flip_up_down(image, seed=None):
  """Randomly flips an image vertically (upside down).

  With a 1 in 2 chance, outputs the contents of `image` flipped along the first
  dimension, which is `height`.  Otherwise output the image as-is.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`
    seed: A Python integer. Used to create a random seed. See
      [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
      for behavior.

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=False)
  uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
  mirror = math_ops.less(array_ops.pack([uniform_random, 1.0, 1.0]), 0.5)
  return array_ops.reverse(image, mirror) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def random_flip_left_right(image, seed=None):
  """Randomly flip an image horizontally (left to right).

  With a 1 in 2 chance, outputs the contents of `image` flipped along the
  second dimension, which is `width`.  Otherwise output the image as-is.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`
    seed: A Python integer. Used to create a random seed. See
      [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
      for behavior.

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=False)
  uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
  mirror = math_ops.less(array_ops.pack([1.0, uniform_random, 1.0]), 0.5)
  return array_ops.reverse(image, mirror) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def flip_left_right(image):
  """Flip an image horizontally (left to right).

  Outputs the contents of `image` flipped along the second dimension, which is
  `width`.

  See also `reverse()`.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=False)
  return array_ops.reverse(image, [False, True, False]) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def flip_up_down(image):
  """Flip an image horizontally (upside down).

  Outputs the contents of `image` flipped along the first dimension, which is
  `height`.

  See also `reverse()`.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=False)
  return array_ops.reverse(image, [True, False, False]) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def _reverse(self, t, lengths):
    """Time reverse the provided tensor or list of tensors.

    Assumes the top dimension is the time dimension.

    Args:
      t: 3D tensor or list of 2D tensors to be reversed
      lengths: 1D tensor of lengths, or `None`

    Returns:
      A reversed tensor or list of tensors
    """
    if isinstance(t, list):
      return list(reversed(t))
    else:
      if lengths is None:
        return array_ops.reverse(t, [True, False, False])
      else:
        return array_ops.reverse_sequence(t, lengths, 0, 1) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def testNextSentencePredictionExtractor(self,
                                          sentences,
                                          expected_segment_a,
                                          expected_segment_b,
                                          expected_labels,
                                          random_next_sentence_threshold=0.5,
                                          test_description=""):
    sentences = ragged_factory_ops.constant(sentences)
    # Set seed and rig the shuffle function to a deterministic reverse function
    # instead. This is so that we have consistent and deterministic results.
    random_seed.set_seed(1234)
    nsp = segment_extractor_ops.NextSentencePredictionExtractor(
        shuffle_fn=functools.partial(array_ops.reverse, axis=[-1]),
        random_next_sentence_threshold=random_next_sentence_threshold,
    )
    results = nsp.get_segments(sentences)
    actual_segment_a, actual_segment_b, actual_labels = results
    self.assertAllEqual(expected_segment_a, actual_segment_a)
    self.assertAllEqual(expected_segment_b, actual_segment_b)
    self.assertAllEqual(expected_labels, actual_labels) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def flip_up_down(image):
  """Flip an image vertically (upside down).

  Outputs the contents of `image` flipped along the first dimension, which is
  `height`.

  See also `reverse()`.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  image = control_flow_ops.with_dependencies(
      _Check3DImage(image, require_static=False), image)
  return fix_image_flip_shape(image, array_ops.reverse(image, [0])) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def random_flip_up_down(image, seed=None):
  """Randomly flips an image vertically (upside down).

  With a 1 in 2 chance, outputs the contents of `image` flipped along the first
  dimension, which is `height`.  Otherwise output the image as-is.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`
    seed: A Python integer. Used to create a random seed. See
      @{tf.set_random_seed}
      for behavior.

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  image = control_flow_ops.with_dependencies(
      _Check3DImage(image, require_static=False), image)
  uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
  mirror_cond = math_ops.less(uniform_random, .5)
  result = control_flow_ops.cond(mirror_cond,
                                 lambda: array_ops.reverse(image, [0]),
                                 lambda: image)
  return fix_image_flip_shape(image, result) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def random_flip_left_right(image, seed=None):
  """Randomly flip an image horizontally (left to right).

  With a 1 in 2 chance, outputs the contents of `image` flipped along the
  second dimension, which is `width`.  Otherwise output the image as-is.

  Args:
    image: A 3-D tensor of shape `[height, width, channels].`
    seed: A Python integer. Used to create a random seed. See
      @{tf.set_random_seed}
      for behavior.

  Returns:
    A 3-D tensor of the same type and shape as `image`.

  Raises:
    ValueError: if the shape of `image` not supported.
  """
  image = ops.convert_to_tensor(image, name='image')
  image = control_flow_ops.with_dependencies(
      _Check3DImage(image, require_static=False), image)
  uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
  mirror_cond = math_ops.less(uniform_random, .5)
  result = control_flow_ops.cond(mirror_cond,
                                 lambda: array_ops.reverse(image, [1]),
                                 lambda: image)
  return fix_image_flip_shape(image, result) 

# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import reverse [as 别名]
def rnn(cls, x, cell_class, cell_kwargs, rnn_kwargs, activations, direction):
    cell_kwargs["activation"] = activations[0]

    rnn_cell = [cell_class(**cell_kwargs)]
    cell_fw = tf.compat.v1.nn.rnn_cell.MultiRNNCell(rnn_cell)

    if direction == "bidirectional":
      cell_kwargs["activation"] = activations[1]
      rnn_cell_bw = [cell_class(**cell_kwargs)]
      cell_bw = tf.compat.v1.nn.rnn_cell.MultiRNNCell(rnn_cell_bw)

    if direction == "forward":
      outputs, states = tf.compat.v1.nn.dynamic_rnn(cell_fw, x, **rnn_kwargs)
    elif direction == "bidirectional":
      outputs, states = tf.compat.v1.nn.bidirectional_dynamic_rnn(
          cell_fw, cell_bw, x, **rnn_kwargs)
    elif direction == "reverse":

      def _reverse(input_, seq_dim):
        return array_ops.reverse(input_, axis=[seq_dim])

      time_dim = 0
      inputs_reverse = _reverse(x, time_dim)
      outputs, states = tf.compat.v1.nn.dynamic_rnn(cell_fw, inputs_reverse,
                                                    **rnn_kwargs)
      outputs = _reverse(outputs, time_dim)

    return outputs, states