Python array_ops.zeros_like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 zeros_like [as 别名]
def _init_attention(encoder_state):
  """Initialize attention. Handling both LSTM and GRU.

  Args:
    encoder_state: The encoded state to initialize the `dynamic_rnn_decoder`.

  Returns:
    attn: initial zero attention vector.
  """

  # Multi- vs single-layer
  # TODO(thangluong): is this the best way to check?
  if isinstance(encoder_state, tuple):
    top_state = encoder_state[-1]
  else:
    top_state = encoder_state

  # LSTM vs GRU
  if isinstance(top_state, rnn_cell_impl.LSTMStateTuple):
    attn = array_ops.zeros_like(top_state.h)
  else:
    attn = array_ops.zeros_like(top_state)

  return attn 

# 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 zeros_like [as 别名]
def ZerosLikeOutsideLoop(op, index):
  """Create zeros_like for the specified output of an op."""
  val = op.outputs[index]
  if not IsSwitch(op):
    return array_ops.zeros_like(val, optimize=False)
  else:
    op_ctxt = op._get_control_flow_context()
    if op_ctxt:
      # We are in a cond context. Use a switch to create zeros only when needed.
      pred = op_ctxt.pred
      branch = op_ctxt.branch
      switch_val = switch(op.inputs[0], pred)[1 - branch]
      zeros_shape = array_ops.shape_internal(switch_val, optimize=False)
      return array_ops.zeros(zeros_shape, dtype=val.dtype)
    else:
      return array_ops.zeros_like(val, optimize=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 zeros_like [as 别名]
def _PowGrad(op, grad):
  """Returns grad * (y*x^(y-1), z*log(x))."""
  x = op.inputs[0]
  y = op.inputs[1]
  z = op.outputs[0]
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  x = math_ops.conj(x)
  y = math_ops.conj(y)
  z = math_ops.conj(z)
  gx = array_ops.reshape(
      math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
  # Avoid false singularity at x = 0
  if x.dtype.is_complex:
    # real(x) < 0 is fine for the complex case
    log_x = array_ops.where(
        math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
  else:
    # There's no sensible real value to return if x < 0, so return 0
    log_x = array_ops.where(x > 0, math_ops.log(x), array_ops.zeros_like(x))
  gy = array_ops.reshape(math_ops.reduce_sum(grad * z * log_x, ry), sy)
  return gx, gy 

# 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 zeros_like [as 别名]
def _safe_div(numerator, denominator, name="value"):
  """Computes a safe divide which returns 0 if the denominator is zero.

  Note that the function contains an additional conditional check that is
  necessary for avoiding situations where the loss is zero causing NaNs to
  creep into the gradient computation.

  Args:
    numerator: An arbitrary `Tensor`.
    denominator: `Tensor` whose shape matches `numerator` and whose values are
      assumed to be non-negative.
    name: An optional name for the returned op.

  Returns:
    The element-wise value of the numerator divided by the denominator.
  """
  return array_ops.where(
      math_ops.greater(denominator, 0),
      math_ops.div(numerator, array_ops.where(
          math_ops.equal(denominator, 0),
          array_ops.ones_like(denominator), denominator)),
      array_ops.zeros_like(numerator),
      name=name) 

# 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 zeros_like [as 别名]
def _safe_div(numerator, denominator, name="value"):
  """Computes a safe divide which returns 0 if the denominator is zero.

  Note that the function contains an additional conditional check that is
  necessary for avoiding situations where the loss is zero causing NaNs to
  creep into the gradient computation.

  Args:
    numerator: An arbitrary `Tensor`.
    denominator: A `Tensor` whose shape matches `numerator` and whose values are
      assumed to be non-negative.
    name: An optional name for the returned op.

  Returns:
    The element-wise value of the numerator divided by the denominator.
  """
  return array_ops.where(
      math_ops.greater(denominator, 0),
      math_ops.div(numerator, array_ops.where(
          math_ops.equal(denominator, 0),
          array_ops.ones_like(denominator), denominator)),
      array_ops.zeros_like(numerator),
      name=name) 

# 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 zeros_like [as 别名]
def testMultipleGradientsWithVariables(self):
    gradient = constant_op.constant(self._grad_vec, dtype=dtypes.float32)
    variable = variables_lib.Variable(array_ops.zeros_like(gradient))
    grad_to_var = (gradient, variable)
    gradient_multipliers = {variable: self._multiplier}

    [grad_to_var] = learning.multiply_gradients([grad_to_var],
                                                gradient_multipliers)

    # Ensure the variable passed through.
    self.assertEqual(grad_to_var[1], variable)

    with self.test_session() as sess:
      actual_gradient = sess.run(grad_to_var[0])
    np_testing.assert_almost_equal(actual_gradient, self._multiplied_grad_vec,
                                   5) 

# 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 zeros_like [as 别名]
def _init_attention(encoder_state):
  """Initialize attention. Handling both LSTM and GRU.

  Args:
    encoder_state: The encoded state to initialize the `dynamic_rnn_decoder`.

  Returns:
    attn: initial zero attention vector.
  """

  # Multi- vs single-layer
  # TODO(thangluong): is this the best way to check?
  if isinstance(encoder_state, tuple):
    top_state = encoder_state[-1]
  else:
    top_state = encoder_state

  # LSTM vs GRU
  if isinstance(top_state, core_rnn_cell_impl.LSTMStateTuple):
    attn = array_ops.zeros_like(top_state.h)
  else:
    attn = array_ops.zeros_like(top_state)

  return attn 

# 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 zeros_like [as 别名]
def _zero_out_grad(op, grad):
  """The gradients for `zero_out`.

  Args:
    op: The `zero_out` `Operation` that we are differentiating, which we can use
      to find the inputs and outputs of the original op.
    grad: Gradient with respect to the output of the `zero_out` op.

  Returns:
    Gradients with respect to the input of `zero_out`.
  """
  to_zero = op.inputs[0]
  shape = array_ops.shape(to_zero)
  index = array_ops.zeros_like(shape)
  first_grad = array_ops.reshape(grad, [-1])[0]
  to_zero_grad = sparse_ops.sparse_to_dense(index, shape, first_grad, 0)
  return [to_zero_grad]  # List of one Tensor, since we have one input 

# 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 zeros_like [as 别名]
def _safe_div(numerator, denominator, name):
  """Divides two tensors element-wise, returning 0 if the denominator is <= 0.

  Args:
    numerator: A real `Tensor`.
    denominator: A real `Tensor`, with dtype matching `numerator`.
    name: Name for the returned op.

  Returns:
    0 if `denominator` <= 0, else `numerator` / `denominator`
  """
  t = math_ops.truediv(numerator, denominator)
  zero = array_ops.zeros_like(t, dtype=denominator.dtype)
  condition = math_ops.greater(denominator, zero)
  zero = math_ops.cast(zero, t.dtype)
  return array_ops.where(condition, t, zero, name=name) 

# 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 zeros_like [as 别名]
def __init__(self, inputs, sequence_length, time_major=False, name=None):
        """Initializer.

        Args:
          inputs: A (structure of) input tensors.
          sequence_length: An int32 vector tensor.
          time_major: Python bool.  Whether the tensors in `inputs` are time major.
            If `False` (default), they are assumed to be batch major.
          name: Name scope for any created operations.

        Raises:
          ValueError: if `sequence_length` is not a 1D tensor.
        """
        with ops.name_scope(name, "TrainingHelper", [inputs, sequence_length]):
            inputs = ops.convert_to_tensor(inputs, name="inputs")
            self._inputs = inputs
            if not time_major:
                inputs = nest.map_structure(_transpose_batch_time, inputs)

            self._input_tas = nest.map_structure(_unstack_ta, inputs)
            self._sequence_length = ops.convert_to_tensor(
                sequence_length, name="sequence_length")
            if self._sequence_length.get_shape().ndims != 1:
                raise ValueError(
                    "Expected sequence_length to be a vector, but received shape: %s" %
                    self._sequence_length.get_shape())

            self._zero_inputs = nest.map_structure(
                lambda inp: array_ops.zeros_like(inp[0, :]), inputs)

            self._batch_size = shape_list(sequence_length)[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 zeros_like [as 别名]
def _SparseDenseCwiseMulOrDivGrad(op, grad, is_mul):
  """Common code for SparseDenseCwise{Mul,Div} gradients."""
  x_indices = op.inputs[0]
  x_shape = op.inputs[2]
  y = op.inputs[3]

  y_shape = math_ops.to_int64(array_ops.shape(y))
  num_added_dims = array_ops.expand_dims(
      array_ops.size(x_shape) - array_ops.size(y_shape), 0)
  augmented_y_shape = array_ops.concat(
      [array_ops.ones(num_added_dims, ops.dtypes.int64), y_shape], 0)

  scaling = x_shape // augmented_y_shape
  scaled_indices = x_indices // scaling
  scaled_indices = array_ops.slice(scaled_indices,
                                   array_ops.concat([[0], num_added_dims], 0),
                                   [-1, -1])
  dense_vals = array_ops.gather_nd(y, scaled_indices)

  if is_mul:
    dx = grad * dense_vals
    dy_val = grad * op.inputs[1]
  else:
    dx = grad / dense_vals
    dy_val = grad * (-op.inputs[1] / math_ops.square(dense_vals))
  # indices can repeat after scaling, so we can't use sparse_to_dense().
  dy = sparse_ops.sparse_add(
      array_ops.zeros_like(y),
      sparse_tensor.SparseTensor(scaled_indices, dy_val, y_shape))

  # (sp_indices, sp_vals, sp_shape, dense)
  return (None, dx, None, dy) 

# 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 zeros_like [as 别名]
def _entropy(self):
    # Use broadcasting rules to calculate the full broadcast scale.
    scale = self.scale + array_ops.zeros_like(self.loc)
    return math.log(2.) + 1. + math_ops.log(scale) 

# 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 zeros_like [as 别名]
def _mean(self):
    return self.loc + array_ops.zeros_like(self.scale) 

# 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 zeros_like [as 别名]
def _stddev(self):
    return math.sqrt(2.) * self.scale + array_ops.zeros_like(self.loc) 

# 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 zeros_like [as 别名]
def _SelectGrad(op, grad):
  c = op.inputs[0]
  x = op.inputs[1]
  zeros = array_ops.zeros_like(x)
  return (None, array_ops.where(c, grad, zeros),
          array_ops.where(c, zeros, grad))