Python math_ops.complex方法代码示例

本文整理汇总了Python中tensorflow.python.ops.math_ops.complex方法的典型用法代码示例。如果您正苦于以下问题：Python math_ops.complex方法的具体用法？Python math_ops.complex怎么用？Python math_ops.complex使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.math_ops的用法示例。

在下文中一共展示了math_ops.complex方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: _PowGrad

# 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 complex [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

开发者ID:ryfeus，项目名称:lambda-packs，代码行数:25，代码来源:math_grad.py

示例2: test_log_abs_det

# 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 complex [as 别名]
def test_log_abs_det(self):
    self._skip_if_tests_to_skip_contains("log_abs_det")
    for use_placeholder in False, True:
      for shape in self._shapes_to_test:
        for dtype in self._dtypes_to_test:
          if dtype.is_complex:
            self.skipTest(
                "tf.matrix_determinant does not work with complex, so this "
                "test is being skipped.")
          with self.test_session(graph=ops.Graph()) as sess:
            sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
            operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
                shape, dtype, use_placeholder=use_placeholder)
            op_log_abs_det = operator.log_abs_determinant()
            mat_log_abs_det = math_ops.log(
                math_ops.abs(linalg_ops.matrix_determinant(mat)))
            if not use_placeholder:
              self.assertAllEqual(shape[:-2], op_log_abs_det.get_shape())
            op_log_abs_det_v, mat_log_abs_det_v = sess.run(
                [op_log_abs_det, mat_log_abs_det],
                feed_dict=feed_dict)
            self.assertAC(op_log_abs_det_v, mat_log_abs_det_v)

开发者ID:ryfeus，项目名称:lambda-packs，代码行数:24，代码来源:linear_operator_test_util.py

示例3: _PowGrad

# 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 complex [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 = math_ops.select(
        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 = math_ops.select(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

开发者ID:tobegit3hub，项目名称:deep_image_model，代码行数:26，代码来源:math_grad.py

示例4: call

# 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 complex [as 别名]
def __call__(self, inputs, state, scope=None):
        with vs.variable_scope(scope or "eunn_cell"):

            state = _eunn_loop(state, self._capacity, self.diag_vec, self.off_vec, self.diag, self._fft)

            input_matrix_init = init_ops.random_uniform_initializer(-0.01, 0.01)
            if self._comp:
                input_matrix_re = vs.get_variable("U_re", [inputs.get_shape()[-1], self._hidden_size],
                                                  initializer=input_matrix_init)
                input_matrix_im = vs.get_variable("U_im", [inputs.get_shape()[-1], self._hidden_size],
                                                  initializer=input_matrix_init)
                inputs_re = math_ops.matmul(inputs, input_matrix_re)
                inputs_im = math_ops.matmul(inputs, input_matrix_im)
                inputs = math_ops.complex(inputs_re, inputs_im)
            else:
                input_matrix = vs.get_variable("U", [inputs.get_shape()[-1], self._hidden_size],
                                               initializer=input_matrix_init)
                inputs = math_ops.matmul(inputs, input_matrix)

            bias = vs.get_variable("modReLUBias", [self._hidden_size], initializer=init_ops.constant_initializer())
            output = self._activation((inputs + state), bias, self._comp)

        return output, output

开发者ID:IsaacChanghau，项目名称:AmusingPythonCodes，代码行数:25，代码来源:EUNN.py