Python tensorflow.python.ops.array_ops 模块，unstack() 实例源码

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 _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 _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 testUnpack(self):
    self._testUnary(
        array_ops.unstack,
        np.array([[1., 2.], [3., 4.], [5., 6.]], dtype=np.float32),
        expected=[
            np.array([1., 2.], dtype=np.float32),
            np.array([3., 4.], dtype=np.float32),
            np.array([5., 6.], dtype=np.float32),
        ],
        equality_test=self.ListsAreClose)

    self._testUnary(lambda x: array_ops.unstack(x, axis=1),
                    np.array([[1., 2.], [3., 4.], [5., 6.]], dtype=np.float32),
                    expected=[
                        np.array([1., 3., 5.], dtype=np.float32),
                        np.array([2., 4., 6.], dtype=np.float32),
                    ],
                    equality_test=self.ListsAreClose)

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 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 _prepare_inputs_for_rnn(sequence_features, context_features, num_unroll):
  """Prepares features batched by the SQSS for input to a state-saving RNN.

  Args:
    sequence_features: A dict of sequence feature name to `Tensor`, with
      tensors of shape `[batch_size, num_unroll, ...]` and type float32.
    context_features: A dict of context feature name to `Tensor`, with
      tensors of shape `[batch_size, 1, ...]` and type float32.
    num_unroll: Python integer, how many time steps to unroll at a time.
      The input sequences of length `k` are then split into `k / num_unroll`
      many segments.

  Returns:
    features_by_time: A list of length `num_unroll` with `Tensor` entries of
      shape `[batch_size, len(sequence_features) + len(context_features)]` of
      type float32. Features are stored in lexicographic order by their
      corresponding feature dict keys, first in the `sequence_features` and
      then in the `context_features` dicts. Context features are copied into
      each time step.
  """

  def _tile(feature):
    return array_ops.squeeze(
        array_ops.tile(array_ops.expand_dims(feature, 1), [1, num_unroll, 1]),
        axis=2)

  sequence_features = [sequence_features[k] for k in sorted(sequence_features)]
  if not context_features:
    return array_ops.unstack(array_ops.stack(sequence_features, 2), axis=1)
  context_features = [
      _tile(context_features[k]) for k in sorted(context_features)
  ]
  return array_ops.unstack(
      array_ops.stack(sequence_features + context_features, 2), axis=1)

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 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 _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 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 = core_rnn_cell_impl.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 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 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 dense_to_sparse_tensor(dense_tensor, ignore_value=None):
  """Converts a dense Tensor to a SparseTensor, dropping ignore_value cells.

  Args:
    dense_tensor: A `Tensor`.
    ignore_value: Entries in `dense_tensor` equal to this value will be
      absent from the return `SparseTensor`. If `None`, default value of
      dense_tensor's dtype will be used (e.g. '' for `str`, 0 for `int`).

  Returns:
    A `SparseTensor` with the same shape as `dense_tensor`.

  Raises:
    ValueError: when `dense_tensor`'s rank is `None`.
  """
  with ops.name_scope("DenseToSparseTensor"):
    dense_t = ops.convert_to_tensor(dense_tensor)
    if dense_t.get_shape().ndims is None:
      # TODO(b/32318825): Implement dense_to_sparse_tensor for undefined rank.
      raise ValueError("dense_tensor.get_shape() should be defined, got None.")
    if ignore_value is None:
      if dense_t.dtype == dtypes.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ""
      else:
        ignore_value = dense_t.dtype.as_numpy_dtype()
    dense_shape = math_ops.cast(array_ops.shape(dense_t), dtypes.int64)
    indices = array_ops.where(
        math_ops.not_equal(dense_t, math_ops.cast(ignore_value, dense_t.dtype)))
    index_dims = len(dense_t.get_shape())
    # Flattens the tensor and indices for use with gather.
    flat_tensor = array_ops.reshape(dense_t, [-1])
    flat_indices = indices[:, index_dims - 1]
    # Computes the correct flattened indices for 2d (or higher) tensors.
    if index_dims > 1:
      higher_dims = indices[:, :index_dims - 1]
      shape_multipliers = array_ops.stack(
          _multiplier_helper(array_ops.unstack(dense_shape)[1:]))
      offsets = math_ops.reduce_sum(
          math_ops.multiply(higher_dims, shape_multipliers),
          reduction_indices=[1])
      flat_indices = math_ops.add(flat_indices, offsets)
    values = array_ops.gather(flat_tensor, flat_indices)
    return sparse_tensor.SparseTensor(indices, values, dense_shape)