Python array_ops.stack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 stack [as 别名]
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) 

# 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 stack [as 别名]
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.stack([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([before_pad, after_pad], 1)
  return array_ops.pad(grad, paddings), None, None 

# 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 stack [as 别名]
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.stack([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] 

# 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 stack [as 别名]
def _PadGrad(op, grad):
  """Gradient for Pad."""
  # Pad introduces values around the original tensor, so the gradient function
  # slices the original shape out of the gradient."""
  x = op.inputs[0]
  a = op.inputs[1]  # [Rank(x), 2]
  # Takes a slice of a. The 1st column. [Rank(x), 1].
  pad_before = array_ops.slice(a, [0, 0],
                               array_ops.stack([array_ops.rank(x), 1]))
  # Make it a 1-D tensor.
  begin = array_ops.reshape(pad_before, [-1])
  sizes = array_ops.shape(x)
  return array_ops.slice(grad, begin, sizes), None


# ReverseSequence is just a permutation.  The gradient permutes back. 

# 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 stack [as 别名]
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.stack(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 

# 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 stack [as 别名]
def repeat(x, n):
  """Repeats a 2D tensor.

  if `x` has shape (samples, dim) and `n` is `2`,
  the output will have shape `(samples, 2, dim)`.

  Arguments:
      x: Tensor or variable.
      n: Python integer, number of times to repeat.

  Returns:
      A tensor.
  """
  assert ndim(x) == 2
  x = array_ops.expand_dims(x, 1)
  pattern = array_ops.stack([1, n, 1])
  return array_ops.tile(x, pattern) 

# 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 stack [as 别名]
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 

# 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 stack [as 别名]
def from_list(index, queues):
    """Create a queue using the queue reference from `queues[index]`.

    Args:
      index: An integer scalar tensor that determines the input that gets
        selected.
      queues: A list of `QueueBase` objects.

    Returns:
      A `QueueBase` object.

    Raises:
      TypeError: When `queues` is not a list of `QueueBase` objects,
        or when the data types of `queues` are not all the same.
    """
    if ((not queues) or
        (not isinstance(queues, list)) or
        (not all(isinstance(x, QueueBase) for x in queues))):
      raise TypeError("A list of queues expected")

    dtypes = queues[0].dtypes
    if not all([dtypes == q.dtypes for q in queues[1:]]):
      raise TypeError("Queues do not have matching component dtypes.")

    names = queues[0].names
    if not all([names == q.names for q in queues[1:]]):
      raise TypeError("Queues do not have matching component names.")

    queue_shapes = [q.shapes for q in queues]
    reduced_shapes = [
        six.moves.reduce(_shape_common, s) for s in zip(*queue_shapes)]

    queue_refs = array_ops.stack([x.queue_ref for x in queues])
    selected_queue = array_ops.gather(queue_refs, index)
    return QueueBase(dtypes=dtypes, shapes=reduced_shapes, names=names,
                     queue_ref=selected_queue) 

# 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 stack [as 别名]
def report_uninitialized_resources(resource_list=None,
                                   name="report_uninitialized_resources"):
  """Returns the names of all uninitialized resources in resource_list.

  If the returned tensor is empty then all resources have been initialized.

  Args:
   resource_list: resources to check. If None, will use shared_resources() +
    local_resources().
   name: name for the resource-checking op.

  Returns:
   Tensor containing names of the handles of all resources which have not
   yet been initialized.

  """
  if resource_list is None:
    resource_list = shared_resources() + local_resources()
  with ops.name_scope(name):
    if not resource_list:
      # Return an empty tensor so we only need to check for returned tensor
      # size being 0 as an indication of model ready.
      return array_ops.constant([], dtype=dtypes.string)
    # Get a 1-D boolean tensor listing whether each resource is initialized.
    variables_mask = math_ops.logical_not(
        array_ops.stack([r.is_initialized for r in resource_list]))
    # Get a 1-D string tensor containing all the resource names.
    variable_names_tensor = array_ops.constant(
        [s.handle.name for s in resource_list])
    # Return a 1-D tensor containing all the names of uninitialized resources.
    return array_ops.boolean_mask(variable_names_tensor, variables_mask) 

# 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 stack [as 别名]
def report_uninitialized_variables(var_list=None,
                                   name="report_uninitialized_variables"):
  """Adds ops to list the names of uninitialized variables.

  When run, it returns a 1-D tensor containing the names of uninitialized
  variables if there are any, or an empty array if there are none.

  Args:
    var_list: List of `Variable` objects to check. Defaults to the
      value of `global_variables() + local_variables()`
    name: Optional name of the `Operation`.

  Returns:
    A 1-D tensor containing names of the uninitialized variables, or an empty
    1-D tensor if there are no variables or no uninitialized variables.
  """
  if var_list is None:
    var_list = global_variables() + local_variables()
    # Backwards compatibility for old-style variables. TODO(touts): remove.
    if not var_list:
      var_list = []
      for op in ops.get_default_graph().get_operations():
        if op.type in ["Variable", "VariableV2", "AutoReloadVariable"]:
          var_list.append(op.outputs[0])
  with ops.name_scope(name):
    if not var_list:
      # Return an empty tensor so we only need to check for returned tensor
      # size being 0 as an indication of model ready.
      return array_ops.constant([], dtype=dtypes.string)
    else:
      # Get a 1-D boolean tensor listing whether each variable is initialized.
      variables_mask = math_ops.logical_not(
          array_ops.stack(
              [state_ops.is_variable_initialized(v) for v in var_list]))
      # Get a 1-D string tensor containing all the variable names.
      variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
      # Return a 1-D tensor containing all the names of uninitialized variables.
      return array_ops.boolean_mask(variable_names_tensor, variables_mask)

# pylint: disable=protected-access 

# 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 stack [as 别名]
def _UnpackGrad(op, *grads):
  """Gradient for unpack op."""
  return array_ops.stack(grads, axis=op.get_attr("axis")) 

# 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 stack [as 别名]
def forward_sync(self):
    """A control trigger node for synchronization in the forward loop.

    One main use is to keep the push ops of a stack executed in the
    iteration order.
    """
    if self._forward_sync is None:
      with ops.control_dependencies(None):
        self._forward_sync = control_trigger(name="f_sync")
      self._forward_sync._set_control_flow_context(self._forward_context)
      self._forward_index.op._add_control_input(self._forward_sync)
    return self._forward_sync 

# 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 stack [as 别名]
def grad_sync(self):
    """A control trigger node for synchronization in the grad loop.

    One main use is to keep the pop ops of a stack executed in the
    iteration order.
    """
    if self._grad_sync is None:
      with ops.control_dependencies(None):
        self._grad_sync = control_trigger(name="b_sync")
      self._grad_sync._set_control_flow_context(self._grad_context)
      self._grad_index.op._add_control_input(self._grad_sync)
    return self._grad_sync 

# 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 stack [as 别名]
def _TopKGrad(op, grad, _):
  """Return the gradients for TopK.

  Args:
    op: The TopKOp for which we need to generate gradients.
    grad: Tensor. The gradients passed to the TopKOp.

  Returns:
    A list of two tensors, the first being the gradient w.r.t to the input and
    TopK, and the second being the gradient w.r.t. to the indices (all zero).
  """
  in_shape = array_ops.shape(op.inputs[0])
  ind_shape = array_ops.shape(op.outputs[1])

  ind_lastdim = array_ops.gather(ind_shape, array_ops.size(ind_shape) - 1)
  # Flatten indices to 2D.
  ind_2d = array_ops.reshape(op.outputs[1], array_ops.stack([-1, ind_lastdim]))

  in_lastdim = array_ops.gather(in_shape, array_ops.size(in_shape) - 1)
  outerdim = array_ops.shape(ind_2d)[0]
  # Compute linear indices (flattened to 1D).
  ind = array_ops.reshape(ind_2d + array_ops.expand_dims(
      math_ops.range(0, outerdim * in_lastdim, in_lastdim), -1), [-1])

  # Substitute grad to appropriate locations and fill the rest with zeros,
  # finally reshaping it to the original input shape.
  return [array_ops.reshape(
      sparse_ops.sparse_to_dense(ind,
                                 array_ops.reshape(
                                     math_ops.reduce_prod(in_shape), [1]),
                                 array_ops.reshape(grad, [-1]),
                                 validate_indices=False),
      in_shape), array_ops.zeros(
          [], dtype=dtypes.int32)] 

# 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 stack [as 别名]
def _sum_rows(x):
  """Returns a vector summing up each row of the matrix x."""
  # _sum_rows(x) is equivalent to math_ops.reduce_sum(x, 1) when x is
  # a matrix.  The gradient of _sum_rows(x) is more efficient than
  # reduce_sum(x, 1)'s gradient in today's implementation. Therefore,
  # we use _sum_rows(x) in the nce_loss() computation since the loss
  # is mostly used for training.
  cols = array_ops.shape(x)[1]
  ones_shape = array_ops.stack([cols, 1])
  ones = array_ops.ones(ones_shape, x.dtype)
  return array_ops.reshape(math_ops.matmul(x, ones), [-1])