Python math_ops.scalar_mul方法代码示例

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import scalar_mul [as 别名]
def _SquaredDifferenceGrad(op, grad):
  """Returns the gradient for (x-y)^2."""
  x = op.inputs[0]
  y = op.inputs[1]
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  # pylint: disable=protected-access
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  # pylint: enable=protected-access
  # .op works with Tensors or IndexedSlices
  with ops.control_dependencies([grad.op]):
    # The parens ensure that if grad is IndexedSlices, it'll get multiplied by
    # Tensor (not a number like 2.0) which causes it to convert to Tensor.
    x_grad = math_ops.scalar_mul(2.0, grad) * (x - y)
  return (array_ops.reshape(math_ops.reduce_sum(x_grad, rx), sx),
          -array_ops.reshape(math_ops.reduce_sum(x_grad, ry), sy))


# Logical operations have no gradients. 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import scalar_mul [as 别名]
def _SquaredDifferenceGrad(op, grad):
  """Returns the gradient for (x-y)^2."""
  x = op.inputs[0]
  y = op.inputs[1]
  sx = array_ops.shape(x)
  sy = array_ops.shape(y)
  # pylint: disable=protected-access
  rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
  # pylint: enable=protected-access
  with ops.control_dependencies([grad]):
    # The parens ensure that if grad is IndexedSlices, it'll get multiplied by
    # Tensor (not a number like 2.0) which causes it to convert to Tensor.
    x_grad = math_ops.scalar_mul(2.0, grad) * (x - y)
  return (array_ops.reshape(math_ops.reduce_sum(x_grad, rx), sx),
          -array_ops.reshape(math_ops.reduce_sum(x_grad, ry), sy))


# Logical operations have no gradients. 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import scalar_mul [as 别名]
def testAcceptsRefs(self):
    var = variables.Variable(10)
    result = math_ops.scalar_mul(3, var)
    init = variables.global_variables_initializer()
    with self.test_session(use_gpu=True) as sess:
      sess.run(init)
      self.assertEqual(30, result.eval()) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import scalar_mul [as 别名]
def testAcceptsConstant(self):
    const = constant_op.constant(10)
    result = math_ops.scalar_mul(3, const)
    with self.test_session(use_gpu=True):
      self.assertEqual(30, result.eval()) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import scalar_mul [as 别名]
def testAcceptsTensor(self):
    tensor = array_ops.ones([10, 10])
    result = math_ops.scalar_mul(3, tensor)
    expected = array_ops.ones([10, 10]) * 3

    with self.test_session(use_gpu=True):
      self.assertAllEqual(expected.eval(), result.eval()) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import scalar_mul [as 别名]
def testAcceptsIndexedSlices(self):
    values = constant_op.constant([2, 3, 5, 7, 0, -1], shape=[3, 2])
    indices = constant_op.constant([0, 2, 5])
    x = math_ops.scalar_mul(-3, ops.IndexedSlices(values, indices))
    with self.test_session(use_gpu=True):
      self.assertAllEqual(x.values.eval(), [[-6, -9], [-15, -21], [0, 3]])
      self.assertAllEqual(x.indices.eval(), [0, 2, 5]) 

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import scalar_mul [as 别名]
def clip_norm(g, c, n):
  """Clip a tensor by norm.

  Arguments:
    g: gradient tensor to clip.
    c: clipping threshold.
    n: norm of gradient tensor.

  Returns:
    Clipped gradient tensor.
  """
  if c > 0:
    condition = n >= c
    then_expression = lambda: math_ops.scalar_mul(c / n, g)
    else_expression = lambda: g

    # saving the shape to avoid converting sparse tensor to dense
    if isinstance(g, ops.Tensor):
      g_shape = copy.copy(g.get_shape())
    elif isinstance(g, ops.IndexedSlices):
      g_shape = copy.copy(g.dense_shape)
    if condition.dtype != dtypes_module.bool:
      condition = math_ops.cast(condition, 'bool')
    g = control_flow_ops.cond(condition, then_expression, else_expression)
    if isinstance(g, ops.Tensor):
      g.set_shape(g_shape)
    elif isinstance(g, ops.IndexedSlices):
      g._dense_shape = g_shape  # pylint: disable=protected-access
  return g