Python tensorflow.python.ops.array_ops.pack() Examples

def _reverse_seq(input_seq, lengths):
    """Reverse a list of Tensors up to specified lengths.
    Args:
        input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
        lengths:   A tensor of dimension batch_size, containing lengths for each
                   sequence in the batch. If "None" is specified, simply reverses
                   the list.
    Returns:
        time-reversed sequence
    """
    for input_ in input_seq:
        input_.set_shape(input_.get_shape().with_rank(2))

    # Join into (time, batch_size, depth)
    s_joined = array_ops_.pack(input_seq)

    # Reverse along dimension 0
    s_reversed = array_ops_.reverse_sequence(s_joined, lengths, 0, 1)
    # Split again into list
    result = array_ops_.unpack(s_reversed)
    return result 

def _SliceGrad(op, grad):
  """Gradient for Slice op."""
  # Create an Nx2 padding where the first column represents how many
  # zeros are to be prepended for each dimension, and the second
  # column indicates how many zeros are appended.
  #
  # The number of zeros to append is the shape of the input
  # elementwise-subtracted by both the begin vector and sizes vector.
  #
  # Some more reshaping is needed to assemble this tensor with the
  # right dimensions.
  input_vec = op.inputs[0]
  begin_vec = op.inputs[1]
  input_rank = array_ops.rank(input_vec)
  slice_size = array_ops.shape(op.outputs[0])

  shape = array_ops.pack([input_rank, 1])
  before_pad = array_ops.reshape(begin_vec, shape)
  after_pad = array_ops.reshape(
      array_ops.shape(input_vec) - slice_size - begin_vec, shape)
  paddings = array_ops.concat(1, [before_pad, after_pad])
  return array_ops.pad(grad, paddings), None, None 

def _TileGrad(op, grad):
  """Sum reduces grad along the tiled dimensions."""
  assert isinstance(grad, ops.Tensor)
  input_shape = array_ops.shape(op.inputs[0])
  # We interleave multiples and input_shape to get split_shape,
  # reshape grad to split_shape, and reduce along all even
  # dimensions (the tiled dimensions) to get the result
  # with shape input_shape.  For example
  #   input_shape = [20, 30, 40]
  #   multiples = [2, 3, 4]
  #   split_shape = [2, 20, 3, 30, 4, 40]
  #   axes = [0, 2, 4]
  split_shape = array_ops.reshape(array_ops.transpose(
      array_ops.pack([op.inputs[1], input_shape])), [-1])
  axes = math_ops.range(0, array_ops.size(split_shape), 2)
  input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes)
  # Fix shape inference
  input_grad.set_shape(op.inputs[0].get_shape())
  return [input_grad, None] 

def testBatch(self):
    # Build an arbitrary RGB image
    np.random.seed(7)
    batch_size = 5
    shape = (batch_size, 2, 7, 3)

    for nptype in [np.float32, np.float64]:
      inp = np.random.rand(*shape).astype(nptype)

      # Convert to HSV and back, as a batch and individually
      with self.test_session(use_gpu=True) as sess:
        batch0 = constant_op.constant(inp)
        batch1 = image_ops.rgb_to_hsv(batch0)
        batch2 = image_ops.hsv_to_rgb(batch1)
        split0 = array_ops.unpack(batch0)
        split1 = list(map(image_ops.rgb_to_hsv, split0))
        split2 = list(map(image_ops.hsv_to_rgb, split1))
        join1 = array_ops.pack(split1)
        join2 = array_ops.pack(split2)
        batch1, batch2, join1, join2 = sess.run([batch1, batch2, join1, join2])

      # Verify that processing batch elements together is the same as separate
      self.assertAllClose(batch1, join1)
      self.assertAllClose(batch2, join2)
      self.assertAllClose(batch2, inp) 

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) 

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) 

def _sample_n(self, n, seed=None):
    # Recall _assert_valid_mu ensures mu and self._cov have same batch shape.
    shape = array_ops.concat(0, [self._cov.vector_shape(), [n]])
    white_samples = random_ops.random_normal(shape=shape,
                                             mean=0.,
                                             stddev=1.,
                                             dtype=self.dtype,
                                             seed=seed)

    correlated_samples = self._cov.sqrt_matmul(white_samples)

    # Move the last dimension to the front
    perm = array_ops.concat(0, (
        array_ops.pack([array_ops.rank(correlated_samples) - 1]),
        math_ops.range(0, array_ops.rank(correlated_samples) - 1)))

    # TODO(ebrevdo): Once we get a proper tensor contraction op,
    # perform the inner product using that instead of batch_matmul
    # and this slow transpose can go away!
    correlated_samples = array_ops.transpose(correlated_samples, perm)
    samples = correlated_samples + self.mu
    return samples 

def zero_fast_weights(self, batch_size, dtype):
    """Return zero-filled fast_weights tensor(s).

    Args:
      batch_size: int, float, or unit Tensor representing the batch size.
      dtype: the data type to use for the state.

    Returns:
      A zero filled fast_weights of shape [batch_size, state_size, state_size]
    """
    state_size = self.state_size

    zeros = array_ops.zeros(
        array_ops.pack([batch_size, state_size, state_size]), dtype=dtype)
    zeros.set_shape([None, state_size, state_size])

    return zeros 

def inference_graph(self, input_data, data_spec=None):
    """Constructs a TF graph for evaluating a random forest.

    Args:
      input_data: A tensor or SparseTensor or placeholder for input data.
      data_spec: A list of tf.dtype values specifying the original types of
        each column.

    Returns:
      The last op in the random forest inference graph.
    """
    data_spec = [constants.DATA_FLOAT] if data_spec is None else data_spec
    probabilities = []
    for i in range(self.params.num_trees):
      with ops.device(self.device_assigner.get_device(i)):
        tree_data = input_data
        if self.params.bagged_features:
          tree_data = self._bag_features(i, input_data)
        probabilities.append(self.trees[i].inference_graph(tree_data,
                                                           data_spec))
    with ops.device(self.device_assigner.get_device(0)):
      all_predict = array_ops.pack(probabilities)
      return math_ops.div(
          math_ops.reduce_sum(all_predict, 0), self.params.num_trees,
          name='probabilities') 

def tensors_to_item(self, keys_to_tensors):
    indices = keys_to_tensors[self._indices_key]
    values = keys_to_tensors[self._values_key]
    if self._shape_key:
      shape = keys_to_tensors[self._shape_key]
      if isinstance(shape, sparse_tensor.SparseTensor):
        shape = sparse_ops.sparse_tensor_to_dense(shape)
    elif self._shape:
      shape = self._shape
    else:
      shape = indices.shape
    indices_shape = array_ops.shape(indices.indices)
    rank = indices_shape[1]
    ids = math_ops.to_int64(indices.values)
    indices_columns_to_preserve = array_ops.slice(
        indices.indices, [0, 0], array_ops.pack([-1, rank - 1]))
    new_indices = array_ops.concat(1, [indices_columns_to_preserve,
                                       array_ops.reshape(ids, [-1, 1])])

    tensor = sparse_tensor.SparseTensor(new_indices, values.values, shape)
    if self._densify:
      tensor = sparse_ops.sparse_tensor_to_dense(tensor, self._default_value)
    return tensor 

def tensors_to_item(self, keys_to_tensors):
    tensor = keys_to_tensors[self._tensor_key]
    shape = self._shape
    if self._shape_keys:
      shape_dims = []
      for k in self._shape_keys:
        shape_dim = keys_to_tensors[k]
        if isinstance(shape_dim, sparse_tensor.SparseTensor):
          shape_dim = sparse_ops.sparse_tensor_to_dense(shape_dim)
        shape_dims.append(shape_dim)
      shape = array_ops.reshape(array_ops.pack(shape_dims), [-1])
    if isinstance(tensor, sparse_tensor.SparseTensor):
      if shape is not None:
        tensor = sparse_ops.sparse_reshape(tensor, shape)
      tensor = sparse_ops.sparse_tensor_to_dense(tensor, self._default_value)
    else:
      if shape is not None:
        tensor = array_ops.reshape(tensor, shape)
    return tensor 

def _do_layer_inference(self, layer, data):

    # If this is a collection of layers, return the mean of their inference
    # results.
    if isinstance(layer, collections.Iterable):
      return math_ops.reduce_mean(
          array_ops.pack([l.inference_graph(data) for l in layer]), 0)
    # If this is a single layer, return its inference result.
    else:
      return layer.inference_graph(data) 

def testIndexedSlicesToTensorList(self):
    with self.test_session():
      numpy_list = []
      dense_list = []
      sparse_list = []
      for _ in range(3):
        np_val = np.random.rand(4, 4, 4, 4).astype(np.float32)
        c = constant_op.constant(np_val)
        c_sparse = math_ops._as_indexed_slices(c)
        numpy_list.append(np_val)
        dense_list.append(c)
        sparse_list.append(c_sparse)
      packed_dense = array_ops.pack(dense_list)
      packed_sparse = array_ops.pack(sparse_list)
      self.assertAllClose(packed_dense.eval(), packed_sparse.eval()) 

def inference_graph(self, input_data, data_spec=None, **inference_args):
    """Constructs a TF graph for evaluating a random forest.

    Args:
      input_data: A tensor or SparseTensor or placeholder for input data.
      data_spec: A list of tf.dtype values specifying the original types of
        each column.
      **inference_args: Keyword arguments to pass through to each tree.

    Returns:
      The last op in the random forest inference graph.
    """
    data_spec = [constants.DATA_FLOAT] if data_spec is None else data_spec
    probabilities = []
    for i in range(self.params.num_trees):
      with ops.device(self.device_assigner.get_device(i)):
        tree_data = input_data
        if self.params.bagged_features:
          tree_data = self._bag_features(i, input_data)
        probabilities.append(self.trees[i].inference_graph(
            tree_data, data_spec, **inference_args))
    with ops.device(self.device_assigner.get_device(0)):
      all_predict = array_ops.pack(probabilities)
      return math_ops.div(
          math_ops.reduce_sum(all_predict, 0), self.params.num_trees,
          name='probabilities') 

def average_impurity(self):
    """Constructs a TF graph for evaluating the leaf impurity of a forest.

    Returns:
      The last op in the graph.
    """
    impurities = []
    for i in range(self.params.num_trees):
      with ops.device(self.device_assigner.get_device(i)):
        impurities.append(self.trees[i].average_impurity())
    return math_ops.reduce_mean(array_ops.pack(impurities)) 

def __call__(self,
               inputs,
               initial_state=None,
               dtype=None,
               sequence_length=None,
               scope=None):
    is_list = isinstance(inputs, list)
    if self._use_dynamic_rnn:
      if is_list:
        inputs = array_ops.pack(inputs)
      outputs, state = rnn.dynamic_rnn(
          self._cell,
          inputs,
          sequence_length=sequence_length,
          initial_state=initial_state,
          dtype=dtype,
          time_major=True,
          scope=scope)
      if is_list:
        # Convert outputs back to list
        outputs = array_ops.unpack(outputs)
    else:  # non-dynamic rnn
      if not is_list:
        inputs = array_ops.unpack(inputs)
      outputs, state = rnn.rnn(self._cell,
                               inputs,
                               initial_state=initial_state,
                               dtype=dtype,
                               sequence_length=sequence_length,
                               scope=scope)
      if not is_list:
        # Convert outputs back to tensor
        outputs = array_ops.pack(outputs)

    return outputs, state 

def testOpsBetweenCut(self):
    with ops.Graph().as_default() as g:
      t1 = constant(1.0)
      t2 = constant(2.0)
      t3 = array_ops.pack([t1, t2])
      t4 = constant([1.0])
      t5 = array_ops.concat(0, [t4, t3])
      t6 = constant([2.0])
      t7 = array_ops.concat(0, [t5, t6])
    self._assertOpListEqual([t7.op, t5.op, t4.op],
                            _OpsBetween(g, [t7.op], [t4.op])) 

def _reverse_seq(input_seq, lengths):
  """Reverse a list of Tensors up to specified lengths.

  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
    lengths:   A tensor of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply
               reverses the list.

  Returns:
    time-reversed sequence
  """
  if lengths is None:
    return list(reversed(input_seq))

  for input_ in input_seq:
    input_.set_shape(input_.get_shape().with_rank(2))

  # Join into (time, batch_size, depth)
  s_joined = array_ops_.pack(input_seq)

  # Reverse along dimension 0
  s_reversed = array_ops_.reverse_sequence(s_joined, lengths, 0, 1)
  # Split again into list
  result = array_ops_.unpack(s_reversed)
  return result 

def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
  """Returns predictions and loss for sequence of predictions.

  Args:
    decoding: List of Tensors with predictions.
    labels: List of Tensors with labels.
    sampling_decoding: Optional, List of Tensor with predictions to be used
      in sampling. E.g. they shouldn't have dependncy on outputs.
      If not provided, decoding is used.
    name: Operation name.

  Returns:
    Predictions and losses tensors.
  """
  with ops.name_scope(name, "sequence_classifier", [decoding, labels]):
    predictions, xent_list = [], []
    for i, pred in enumerate(decoding):
      xent_list.append(nn.softmax_cross_entropy_with_logits(
          pred, labels[i],
          name="sequence_loss/xent_raw{0}".format(i)))
      if sampling_decoding:
        predictions.append(nn.softmax(sampling_decoding[i]))
      else:
        predictions.append(nn.softmax(pred))
    xent = math_ops.add_n(xent_list, name="sequence_loss/xent")
    loss = math_ops.reduce_sum(xent, name="sequence_loss")
    return array_ops_.pack(predictions, axis=1), loss 

def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None):
  """Processes inputs for Sequence to Sequence models.

  Args:
    x: Input Tensor [batch_size, input_length, embed_dim].
    y: Output Tensor [batch_size, output_length, embed_dim].
    input_length: length of input x.
    output_length: length of output y.
    sentinel: optional first input to decoder and final output expected.
      If sentinel is not provided, zeros are used. Due to fact that y is not
      available in sampling time, shape of sentinel will be inferred from x.
    name: Operation name.

  Returns:
    Encoder input from x, and decoder inputs and outputs from y.
  """
  with ops.name_scope(name, "seq2seq_inputs", [x, y]):
    in_x = array_ops_.unpack(x, axis=1)
    y = array_ops_.unpack(y, axis=1)
    if not sentinel:
      # Set to zeros of shape of y[0], using x for batch size.
      sentinel_shape = array_ops_.pack(
          [array_ops_.shape(x)[0], y[0].get_shape()[1]])
      sentinel = array_ops_.zeros(sentinel_shape)
      sentinel.set_shape(y[0].get_shape())
    in_y = [sentinel] + y
    out_y = y + [sentinel]
    return in_x, in_y, out_y 

def _log_prob(self, x):
    with ops.control_dependencies(self._assertions):
      x = ops.convert_to_tensor(x, name="x")
      distribution_log_probs = [d.log_prob(x) for d in self.components]
      cat_log_probs = self._cat_probs(log_probs=True)
      final_log_probs = [
          cat_lp + d_lp
          for (cat_lp, d_lp) in zip(cat_log_probs, distribution_log_probs)
      ]
      concat_log_probs = array_ops.pack(final_log_probs, 0)
      log_sum_exp = math_ops.reduce_logsumexp(concat_log_probs, [0])
      return log_sum_exp 

def _event_shape(self):
    return array_ops.pack([self._cov.vector_space_dimension()]) 

def __call__(self, inputs, state, scope=None):
    """Run this multi-layer cell on inputs, starting from state."""
    with vs.variable_scope(scope or type(self).__name__):  # "MultiRNNCell"
      cur_state_pos = 0
      cur_inp = inputs
      new_states = []
      for i, cell in enumerate(self._cells):
        with vs.variable_scope("Cell%d" % i):
          if self._state_is_tuple:
            if not nest.is_sequence(state):
              raise ValueError(
                  "Expected state to be a tuple of length %d, but received: %s"
                  % (len(self.state_size), state))
            cur_state = state[i]
          else:
            # print("STATE",state)
            """
            cur_state = array_ops.slice(
                state, [0, cur_state_pos], [-1, cell.state_size])
            """
            cur_state = array_ops.unpack(state)[i]
            # cur_state_pos += cell.state_size
          cur_inp, new_state = cell(cur_inp, cur_state)
          new_states.append(new_state)
    """
    new_states = (tuple(new_states) if self._state_is_tuple
                  else array_ops.concat(1, new_states))
    """
    new_states = array_ops.pack(new_states)
    return cur_inp, new_states 

def zero_state(self, batch_size, dtype):
        """Return zero-filled state tensor(s).

        Args:
            batch_size: int, float, or unit Tensor representing the batch size.
            dtype: the data type to use for the state.

        Returns:
            If `state_size` is an int or TensorShape, then the return value is a
            `N-D` tensor of shape `[batch_size x state_size]` filled with zeros.

            If `state_size` is a nested list or tuple, then the return value is
            a nested list or tuple (of the same structure) of `2-D` tensors with
        the shapes `[batch_size x s]` for each s in `state_size`.
        """
        state_size = self.state_size
        if nest.is_sequence(state_size):
            state_size_flat = nest.flatten(state_size)
            zeros_flat = [
                    array_ops.zeros(
                            array_ops.pack(_state_size_with_prefix(s, prefix=[batch_size])),
                            dtype=dtype)
                    for s in state_size_flat]
            for s, z in zip(state_size_flat, zeros_flat):
                z.set_shape(_state_size_with_prefix(s, prefix=[None]))
            zeros = nest.pack_sequence_as(structure=state_size,
                                                                        flat_sequence=zeros_flat)
        else:
            zeros_size = _state_size_with_prefix(state_size, prefix=[batch_size])
            zeros = array_ops.zeros(array_ops.pack(zeros_size), dtype=dtype)
            zeros.set_shape(_state_size_with_prefix(state_size, prefix=[None]))

        return zeros 

def average_size(self):
    """Constructs a TF graph for evaluating the average size of a forest.

    Returns:
      The average number of nodes over the trees.
    """
    sizes = []
    for i in range(self.params.num_trees):
      with ops.device(self.device_assigner.get_device(i)):
        sizes.append(self.trees[i].size())
    return math_ops.reduce_mean(array_ops.pack(sizes))

  # pylint: disable=unused-argument 

def average_impurity(self):
    """Constructs a TF graph for evaluating the leaf impurity of a forest.

    Returns:
      The last op in the graph.
    """
    impurities = []
    for i in range(self.params.num_trees):
      with ops.device(self.device_assigner.get_device(i)):
        impurities.append(self.trees[i].average_impurity())
    return math_ops.reduce_mean(array_ops.pack(impurities)) 

def _test_pack(data, axis):
    """ One iteration of pack """

    assert len(data) >= 1

    with tf.Graph().as_default():
        in_data = [
            array_ops.placeholder(shape=tensor.shape, dtype=tensor.dtype, name="in_{}".format(idx))
            for idx, tensor in enumerate(data)]
        out = array_ops.pack(in_data, axis=axis)
        name = ["in_{}:0".format(idx) for idx in range(len(data))]

        compare_tflite_with_tvm(data, name, in_data, [out]) 

def _reverse_seq(input_seq, lengths):
  """Reverse a list of Tensors up to specified lengths.

  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
    lengths:   A tensor of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply
               reverses the list.

  Returns:
    time-reversed sequence
  """
  if lengths is None:
    return list(reversed(input_seq))

  for input_ in input_seq:
    input_.set_shape(input_.get_shape().with_rank(2))

  # Join into (time, batch_size, depth)
  s_joined = array_ops_.pack(input_seq)

  # Reverse along dimension 0
  s_reversed = array_ops_.reverse_sequence(s_joined, lengths, 0, 1)
  # Split again into list
  result = array_ops_.unpack(s_reversed)
  return result 

def crop_to_1d_bounding_box(image, offset_height, target_height,
                         dynamic_shape=False):
  """Crops an image to a specified bounding box.

  This op cuts a rectangular part out of `image`. The top-left corner of the
  returned image is at `offset_height, offset_width` in `image`, and its
  lower-right corner is at
  `offset_height + target_height, offset_width + target_width`.

  Args:
    image: 3-D tensor with shape `[height, width, channels]`
    offset_height: Vertical coordinate of the top-left corner of the result in
                   the input.
    target_height: Height of the result.
    dynamic_shape: Whether the input image has undertermined shape. If set to
      `True`, shape information will be retrieved at run time. Default to
      `False`.

  Returns:
    3-D tensor of image with shape `[target_height, target_width, channels]`

  Raises:
    ValueError: If the shape of `image` is incompatible with the `offset_*` or
    `target_*` arguments, and `dynamic_shape` is set to `False`.
  """
  image = tf.convert_to_tensor(image, name='image')
  height, _ = _ImageDimensions(image, dynamic_shape=dynamic_shape)

  cropped = array_ops.slice(image,
                            array_ops.pack([offset_height, 0]),
                            array_ops.pack([target_height, -1]))

  return cropped 

def crop_to_bounding_box(image, offset_height, offset_width, target_height,
                         target_width, dynamic_shape=False):
  """Crops an image to a specified bounding box.

  This op cuts a rectangular part out of `image`. The top-left corner of the
  returned image is at `offset_height, offset_width` in `image`, and its
  lower-right corner is at
  `offset_height + target_height, offset_width + target_width`.

  Args:
    image: 3-D tensor with shape `[height, width, channels]`
    offset_height: Vertical coordinate of the top-left corner of the result in
                   the input.
    offset_width: Horizontal coordinate of the top-left corner of the result in
                  the input.
    target_height: Height of the result.
    target_width: Width of the result.
    dynamic_shape: Whether the input image has undertermined shape. If set to
      `True`, shape information will be retrieved at run time. Default to
      `False`.

  Returns:
    3-D tensor of image with shape `[target_height, target_width, channels]`

  Raises:
    ValueError: If the shape of `image` is incompatible with the `offset_*` or
    `target_*` arguments, and `dynamic_shape` is set to `False`.
  """
  image = tf.convert_to_tensor(image, name='image')
  _Check3DImage(image, require_static=(not dynamic_shape))
  shapes = _ImageDimensions(image, dynamic_shape=dynamic_shape)

  cropped = array_ops.slice(image,
                            array_ops.pack([offset_height, 0]),
                            array_ops.pack([target_height, -1]))

  return cropped


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