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

def _ImageDimensions(image):
    """Returns the dimensions of an image tensor.
    Args:
      image: A 3-D Tensor of shape `[height, width, channels]`.
    Returns:
      A list of `[height, width, channels]` corresponding to the dimensions of the
        input image.  Dimensions that are statically known are python integers,
        otherwise they are integer scalar tensors.
    """
    if image.get_shape().is_fully_defined():
        return image.get_shape().as_list()
    else:
        static_shape = image.get_shape().with_rank(3).as_list()
        dynamic_shape = array_ops.unstack(array_ops.shape(image), 3)
        return [s if s is not None else d
                for s, d in zip(static_shape, dynamic_shape)] 

def _ImageDimensions(image):
    """Returns the dimensions of an image tensor.
    Args:
      image: A 3-D Tensor of shape `[height, width, channels]`.
    Returns:
      A list of `[height, width, channels]` corresponding to the dimensions of the
        input image.  Dimensions that are statically known are python integers,
        otherwise they are integer scalar tensors.
    """
    if image.get_shape().is_fully_defined():
        return image.get_shape().as_list()
    else:
        static_shape = image.get_shape().with_rank(3).as_list()
        dynamic_shape = array_ops.unstack(array_ops.shape(image), 3)
        return [s if s is not None else d
                for s, d in zip(static_shape, dynamic_shape)] 

def _ImageDimensions(image, rank = 3):
  """Returns the dimensions of an image tensor.

  Args:
    image: A rank-D Tensor. For 3-D  of shape: `[height, width, channels]`.
    rank: The expected rank of the image

  Returns:
    A list of corresponding to the dimensions of the
    input image.  Dimensions that are statically known are python integers,
    otherwise they are integer scalar tensors.
  """
  if image.get_shape().is_fully_defined():
    return image.get_shape().as_list()
  else:
    static_shape = image.get_shape().with_rank(rank).as_list()
    dynamic_shape = array_ops.unstack(array_ops.shape(image), rank)
    return [s if s is not None else d
            for s, d in zip(static_shape, dynamic_shape)] 

def _ImageDimensions(image, rank):
  """Returns the dimensions of an image tensor.

  Args:
    image: A rank-D Tensor. For 3-D  of shape: `[height, width, channels]`.
    rank: The expected rank of the image

  Returns:
    A list of corresponding to the dimensions of the
    input image.  Dimensions that are statically known are python integers,
    otherwise they are integer scalar tensors.
  """
  if image.get_shape().is_fully_defined():
    return image.get_shape().as_list()
  else:
    static_shape = image.get_shape().with_rank(rank).as_list()
    dynamic_shape = array_ops.unstack(array_ops.shape(image), rank)
    return [s if s is not None else d
            for s, d in zip(static_shape, dynamic_shape)] 

def call(self, inputs):
    shape = inputs.get_shape().as_list()
    input_dim = shape[-1]
    output_shape = shape[:-1] + [self.units]
    if len(output_shape) > 2:
      # Reshape the input to 2D.
      output_shape_tensors = array_ops.unstack(array_ops.shape(inputs))
      output_shape_tensors[-1] = self.units
      output_shape_tensor = array_ops.stack(output_shape_tensors)
      inputs = array_ops.reshape(inputs, [-1, input_dim])

    outputs = standard_ops.matmul(inputs, self.kernel)
    if self.use_bias:
      outputs = nn.bias_add(outputs, self.bias)

    if len(output_shape) > 2:
      # Reshape the output back to the original ndim of the input.
      outputs = array_ops.reshape(outputs, output_shape_tensor)
      outputs.set_shape(output_shape)

    if self.activation is not None:
      return self.activation(outputs)  # pylint: disable=not-callable
    return outputs 

def _ImageDimensions(image):
  """Returns the dimensions of an image tensor.

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

  Returns:
    A list of `[height, width, channels]` corresponding to the dimensions of the
    input image.  Dimensions that are statically known are python integers,
    otherwise they are integer scalar tensors.
  """
  if image.get_shape().is_fully_defined():
    return image.get_shape().as_list()
  else:
    static_shape = image.get_shape().with_rank(3).as_list()
    dynamic_shape = array_ops.unstack(array_ops.shape(image), 3)
    return [s if s is not None else d
            for s, d in zip(static_shape, dynamic_shape)] 

def _ImageDimensions(image, rank):
  """Returns the dimensions of an image tensor.

  Args:
    image: A rank-D Tensor. For 3-D  of shape: `[height, width, channels]`.
    rank: The expected rank of the image

  Returns:
    A list of corresponding to the dimensions of the
    input image.  Dimensions that are statically known are python integers,
    otherwise they are integer scalar tensors.
  """
  if image.get_shape().is_fully_defined():
    return image.get_shape().as_list()
  else:
    static_shape = image.get_shape().with_rank(rank).as_list()
    dynamic_shape = array_ops.unstack(array_ops.shape(image), rank)
    return [s if s is not None else d
            for s, d in zip(static_shape, dynamic_shape)] 

def _infer_fft_length_for_irfft(input_tensor, fft_rank):
  """Infers the `fft_length` argument for a `rank` IRFFT from `input_tensor`."""
  # A TensorShape for the inner fft_rank dimensions.
  fft_shape = input_tensor.get_shape()[-fft_rank:]

  # If any dim is unknown, fall back to tensor-based math.
  if not fft_shape.is_fully_defined():
    fft_length = _array_ops.unstack(_array_ops.shape(input_tensor)[-fft_rank:])
    fft_length[-1] = _math_ops.maximum(0, 2 * (fft_length[-1] - 1))
    return _array_ops.stack(fft_length)

  # Otherwise, return a constant.
  fft_length = fft_shape.as_list()
  if fft_length:
    fft_length[-1] = max(0, 2 * (fft_length[-1] - 1))
  return _ops.convert_to_tensor(fft_length, _dtypes.int32) 

def _ImageDimensions(image):
    """Returns the dimensions of an image tensor.
    Args:
      image: A 3-D Tensor of shape `[height, width, channels]`.
    Returns:
      A list of `[height, width, channels]` corresponding to the dimensions of the
        input image.  Dimensions that are statically known are python integers,
        otherwise they are integer scalar tensors.
    """
    if image.get_shape().is_fully_defined():
        return image.get_shape().as_list()
    else:
        static_shape = image.get_shape().with_rank(3).as_list()
        dynamic_shape = array_ops.unstack(array_ops.shape(image), 3)
        return [s if s is not None else d
                for s, d in zip(static_shape, dynamic_shape)] 

def _ImageDimensions(image):
    """Returns the dimensions of an image tensor.
    Args:
      image: A 3-D Tensor of shape `[height, width, channels]`.
    Returns:
      A list of `[height, width, channels]` corresponding to the dimensions of the
        input image.  Dimensions that are statically known are python integers,
        otherwise they are integer scalar tensors.
    """
    if image.get_shape().is_fully_defined():
        return image.get_shape().as_list()
    else:
        static_shape = image.get_shape().with_rank(3).as_list()
        dynamic_shape = array_ops.unstack(array_ops.shape(image), 3)
        return [s if s is not None else d
                for s, d in zip(static_shape, dynamic_shape)] 

def _ImageDimensions(image):
    """Returns the dimensions of an image tensor.
    Args:
      image: A 3-D Tensor of shape `[height, width, channels]`.
    Returns:
      A list of `[height, width, channels]` corresponding to the dimensions of the
        input image.  Dimensions that are statically known are python integers,
        otherwise they are integer scalar tensors.
    """
    if image.get_shape().is_fully_defined():
        return image.get_shape().as_list()
    else:
        static_shape = image.get_shape().with_rank(3).as_list()
        dynamic_shape = array_ops.unstack(array_ops.shape(image), 3)
        return [s if s is not None else d
                for s, d in zip(static_shape, dynamic_shape)] 

def call(self, inputs):
    shape = inputs.get_shape().as_list()
    input_dim = shape[-1]
    output_shape = shape[:-1] + [self.units]
    if len(output_shape) > 2:
      # Reshape the input to 2D.
      output_shape_tensors = array_ops.unstack(array_ops.shape(inputs))
      output_shape_tensors[-1] = self.units
      output_shape_tensor = array_ops.stack(output_shape_tensors)
      inputs = array_ops.reshape(inputs, [-1, input_dim])

    outputs = standard_ops.matmul(inputs, self.kernel)
    if self.use_bias:
      outputs = nn.bias_add(outputs, self.bias)

    if len(output_shape) > 2:
      # Reshape the output back to the original ndim of the input.
      outputs = array_ops.reshape(outputs, output_shape_tensor)
      outputs.set_shape(output_shape)

    if self.activation is not None:
      return self.activation(outputs)  # pylint: disable=not-callable
    return outputs 

def _ImageDimensions(image):
  """Returns the dimensions of an image tensor.

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

  Returns:
    A list of `[height, width, channels]` corresponding to the dimensions of the
    input image.  Dimensions that are statically known are python integers,
    otherwise they are integer scalar tensors.
  """
  if image.get_shape().is_fully_defined():
    return image.get_shape().as_list()
  else:
    static_shape = image.get_shape().with_rank(3).as_list()
    dynamic_shape = array_ops.unstack(array_ops.shape(image), 3)
    return [s if s is not None else d
            for s, d in zip(static_shape, dynamic_shape)] 

def _ImageDimensions(images, dynamic_shape=False):
  """Returns the dimensions of an image tensor.
  Args:
    images: 4-D Tensor of shape [batch, height, width, channels]
    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:
    list of integers [batch, height, width, channels]
  """
  # A simple abstraction to provide names for each dimension. This abstraction
  # should make it simpler to switch dimensions in the future (e.g. if we ever
  # want to switch height and width.)
  if dynamic_shape:
    return array_ops.unstack(array_ops.shape(images))
  else:
    return images.get_shape().as_list()


# In[6]: 

def _infer_fft_length_for_irfft(input_tensor, fft_rank):
  """Infers the `fft_length` argument for a `rank` IRFFT from `input_tensor`."""
  # A TensorShape for the inner fft_rank dimensions.
  fft_shape = input_tensor.get_shape()[-fft_rank:]

  # If any dim is unknown, fall back to tensor-based math.
  if not fft_shape.is_fully_defined():
    fft_length = _array_ops.unstack(_array_ops.shape(input_tensor)[-fft_rank:])
    fft_length[-1] = _math_ops.maximum(0, 2 * (fft_length[-1] - 1))
    return _array_ops.stack(fft_length)

  # Otherwise, return a constant.
  fft_length = fft_shape.as_list()
  if fft_length:
    fft_length[-1] = max(0, 2 * (fft_length[-1] - 1))
  return _ops.convert_to_tensor(fft_length, _dtypes.int32) 

def _ImageDimensions(image, rank):
  """Returns the dimensions of an image tensor.

  Args:
    image: A rank-D Tensor. For 3-D  of shape: `[height, width, channels]`.
    rank: The expected rank of the image

  Returns:
    A list of corresponding to the dimensions of the
    input image.  Dimensions that are statically known are python integers,
    otherwise they are integer scalar tensors.
  """
  if image.get_shape().is_fully_defined():
    return image.get_shape().as_list()
  else:
    static_shape = image.get_shape().with_rank(rank).as_list()
    dynamic_shape = array_ops.unstack(array_ops.shape(image), rank)
    return [s if s is not None else d
            for s, d in zip(static_shape, dynamic_shape)] 

def sequence_to_final(inputs, noutput, scope=None, name=None, reverse=False):
  """Run an LSTM across all steps and returns only the final state.

  Args:
    inputs: (length, batch_size, depth) tensor
    noutput: size of output vector
    scope: optional scope name
    name: optional name for output tensor
    reverse: run in reverse

  Returns:
    Batch of size (batch_size, noutput).
  """
  with variable_scope.variable_scope(scope, "SequenceToFinal", [inputs]):
    length, batch_size, _ = _shape(inputs)
    lstm = core_rnn_cell_impl.BasicLSTMCell(noutput, state_is_tuple=False)
    state = array_ops.zeros([batch_size, lstm.state_size])
    inputs_u = array_ops.unstack(inputs)
    if reverse:
      inputs_u = list(reversed(inputs_u))
    for i in xrange(length):
      if i > 0:
        variable_scope.get_variable_scope().reuse_variables()
      output, state = lstm(inputs_u[i], state)
    outputs = array_ops.reshape(output, [batch_size, noutput], name=name)
    return outputs 

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.stack(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.unstack(outputs)
    else:  # non-dynamic rnn
      if not is_list:
        inputs = array_ops.unstack(inputs)
      outputs, state = contrib_rnn.static_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.stack(outputs)

    return outputs, state 

def _PackGrad(op, grad):
  """Gradient for pack op."""
  return array_ops.unstack(grad, num=op.get_attr("N"), axis=op.get_attr("axis")) 

def sequence_softmax(inputs, noutput, scope=None, name=None, linear_name=None):
  """Run a softmax layer over all the time steps of an input sequence.

  Args:
    inputs: (length, batch_size, depth) tensor
    noutput: output depth
    scope: optional scope name
    name: optional name for output tensor
    linear_name: name for linear (pre-softmax) output

  Returns:
    A tensor of size (length, batch_size, noutput).

  """
  length, _, ninputs = _shape(inputs)
  inputs_u = array_ops.unstack(inputs)
  output_u = []
  with variable_scope.variable_scope(scope, "SequenceSoftmax", [inputs]):
    initial_w = random_ops.truncated_normal([0 + ninputs, noutput], stddev=0.1)
    initial_b = constant_op.constant(0.1, shape=[noutput])
    w = variables.model_variable("weights", initializer=initial_w)
    b = variables.model_variable("biases", initializer=initial_b)
    for i in xrange(length):
      with variable_scope.variable_scope(scope, "SequenceSoftmaxStep",
                                         [inputs_u[i]]):
        # TODO(tmb) consider using slim.fully_connected(...,
        # activation_fn=tf.nn.softmax)
        linear = nn_ops.xw_plus_b(inputs_u[i], w, b, name=linear_name)
        output = nn_ops.softmax(linear)
        output_u += [output]
    outputs = array_ops.stack(output_u, name=name)
  return outputs 

def _PackGrad(op, grad):
  """Gradient for pack op."""
  return array_ops.unstack(grad, num=op.get_attr("N"), axis=op.get_attr("axis")) 

def _cat_probs(self, log_probs):
    """Get a list of num_components batchwise probabilities."""
    which_softmax = nn_ops.log_softmax if log_probs else nn_ops.softmax
    cat_probs = which_softmax(self.cat.logits)
    cat_probs = array_ops.unstack(cat_probs, num=self.num_components, axis=-1)
    return cat_probs 

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.unstack(x, axis=1)
    y = array_ops.unstack(y, axis=1)
    if not sentinel:
      # Set to zeros of shape of y[0], using x for batch size.
      sentinel_shape = array_ops.stack(
          [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 test(self):
    unpack_lt = ops.unpack(self.original_lt)[0]
    golden_lt = core.LabeledTensor(
        array_ops.unstack(self.original_lt.tensor)[0],
        [self.a1, self.a2, self.a3])

    self.assertLabeledTensorsEqual(unpack_lt, golden_lt) 

def test_axis(self):
    unpack_lt = ops.unpack(self.original_lt, axis_name='z')[0]
    golden_lt = core.LabeledTensor(
        array_ops.unstack(
            self.original_lt.tensor, axis=2)[0], [self.a0, self.a1, self.a3])

    self.assertLabeledTensorsEqual(unpack_lt, golden_lt) 

def _PackGrad(op, grad):
  """Gradient for pack op."""
  return array_ops.unstack(grad, num=op.get_attr("N"), axis=op.get_attr("axis")) 

def ndlstm_base_unrolled(inputs, noutput, scope=None, reverse=False):
  """Run an LSTM, either forward or backward.

  This is a 1D LSTM implementation using unrolling and the TensorFlow
  LSTM op.

  Args:
    inputs: input sequence (length, batch_size, ninput)
    noutput: depth of output
    scope: optional scope name
    reverse: run LSTM in reverse

  Returns:
    Output sequence (length, batch_size, noutput)

  """
  with variable_scope.variable_scope(scope, "SeqLstmUnrolled", [inputs]):
    length, batch_size, _ = _shape(inputs)
    lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
    state = array_ops.zeros([batch_size, lstm_cell.state_size])
    output_u = []
    inputs_u = array_ops.unstack(inputs)
    if reverse:
      inputs_u = list(reversed(inputs_u))
    for i in xrange(length):
      if i > 0:
        variable_scope.get_variable_scope().reuse_variables()
      output, state = lstm_cell(inputs_u[i], state)
      output_u += [output]
    if reverse:
      output_u = list(reversed(output_u))
    outputs = array_ops.stack(output_u)
    return outputs 

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, n_features)
               or nested tuples of tensors.
    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))

  flat_input_seq = tuple(nest.flatten(input_) for input_ in input_seq)

  flat_results = [[] for _ in range(len(input_seq))]
  for sequence in zip(*flat_input_seq):
    input_shape = tensor_shape.unknown_shape(
        ndims=sequence[0].get_shape().ndims)
    for input_ in sequence:
      input_shape.merge_with(input_.get_shape())
      input_.set_shape(input_shape)

    # Join into (time, batch_size, depth)
    s_joined = array_ops.stack(sequence)

    # Reverse along dimension 0
    s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
    # Split again into list
    result = array_ops.unstack(s_reversed)
    for r, flat_result in zip(result, flat_results):
      r.set_shape(input_shape)
      flat_result.append(r)

  results = [nest.pack_sequence_as(structure=input_, flat_sequence=flat_result)
             for input_, flat_result in zip(input_seq, flat_results)]
  return results 

def unpack(labeled_tensor, axis_name=None, name=None):
  """Unpack the tensor.

  See tf.unpack.

  Args:
    labeled_tensor: The input tensor.
    axis_name: Optional name of axis to unpack. By default, the first axis is
      used.
    name: Optional op name.

  Returns:
    The list of unpacked LabeledTensors.

  Raises:
    ValueError: If `axis_name` is not an axis on the input.
  """
  with ops.name_scope(name, 'lt_unpack', [labeled_tensor]) as scope:
    labeled_tensor = core.convert_to_labeled_tensor(labeled_tensor)

    axis_names = list(labeled_tensor.axes.keys())
    if axis_name is None:
      axis_name = axis_names[0]

    if axis_name not in axis_names:
      raise ValueError('%s not in %s' % (axis_name, axis_names))
    axis = axis_names.index(axis_name)

    unpack_ops = array_ops.unstack(labeled_tensor.tensor, axis=axis, name=scope)
    axes = [a for i, a in enumerate(labeled_tensor.axes.values()) if i != axis]
    return [core.LabeledTensor(t, axes) for t in unpack_ops] 

def sequence_to_final(inputs, noutput, scope=None, name=None, reverse=False):
  """Run an LSTM across all steps and returns only the final state.

  Args:
    inputs: (length, batch_size, depth) tensor
    noutput: size of output vector
    scope: optional scope name
    name: optional name for output tensor
    reverse: run in reverse

  Returns:
    Batch of size (batch_size, noutput).
  """
  with variable_scope.variable_scope(scope, "SequenceToFinal", [inputs]):
    length, batch_size, _ = _shape(inputs)
    lstm = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
    state = array_ops.zeros([batch_size, lstm.state_size])
    inputs_u = array_ops.unstack(inputs)
    if reverse:
      inputs_u = list(reversed(inputs_u))
    for i in xrange(length):
      if i > 0:
        variable_scope.get_variable_scope().reuse_variables()
      output, state = lstm(inputs_u[i], state)
    outputs = array_ops.reshape(output, [batch_size, noutput], name=name)
    return outputs