Python math_ops.neg函数代码示例

def erosion2d(value, kernel, strides, rates, padding, name=None):
  """Computes the grayscale erosion of 4-D `value` and 3-D `kernel` tensors.

  The `value` tensor has shape `[batch, in_height, in_width, depth]` and the
  `kernel` tensor has shape `[kernel_height, kernel_width, depth]`, i.e.,
  each input channel is processed independently of the others with its own
  structuring function. The `output` tensor has shape
  `[batch, out_height, out_width, depth]`. The spatial dimensions of the
  output tensor depend on the `padding` algorithm. We currently only support the
  default "NHWC" `data_format`.

  In detail, the grayscale morphological 2-D erosion is given by:

      output[b, y, x, c] =
         min_{dy, dx} value[b,
                            strides[1] * y - rates[1] * dy,
                            strides[2] * x - rates[2] * dx,
                            c] -
                      kernel[dy, dx, c]

  Duality: The erosion of `value` by the `kernel` is equal to the negation of
  the dilation of `-value` by the reflected `kernel`.

  Args:
    value: A `Tensor`. 4-D with shape `[batch, in_height, in_width, depth]`.
    kernel: A `Tensor`. Must have the same type as `value`.
      3-D with shape `[kernel_height, kernel_width, depth]`.
    strides: A list of `ints` that has length `>= 4`.
      1-D of length 4. The stride of the sliding window for each dimension of
      the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
    rates: A list of `ints` that has length `>= 4`.
      1-D of length 4. The input stride for atrous morphological dilation.
      Must be: `[1, rate_height, rate_width, 1]`.
    padding: A `string` from: `"SAME", "VALID"`.
      The type of padding algorithm to use.
    name: A name for the operation (optional). If not specified "erosion2d"
      is used.

  Returns:
    A `Tensor`. Has the same type as `value`.
    4-D with shape `[batch, out_height, out_width, depth]`.

  Raises:
    ValueError: If the `value` depth does not match `kernel`' shape, or if
      padding is other than `'VALID'` or `'SAME'`.
  """
  with ops.op_scope([value, kernel], name, "erosion2d") as name:
    # Reduce erosion to dilation by duality.
    return math_ops.neg(gen_nn_ops.dilation2d(input=math_ops.neg(value),
                                              filter=array_ops.reverse(
                                                  kernel, [True, True, False]),
                                              strides=strides,
                                              rates=rates,
                                              padding=padding,
                                              name=name))

  def testInitializerFunction(self):
    value = [[-42], [133.7]]
    shape = [2, 1]
    with self.test_session():
      initializer = lambda: constant_op.constant(value)

      v1 = variables.Variable(initializer, dtype=dtypes.float32)
      self.assertEqual(shape, v1.get_shape())
      self.assertAllClose(value, v1.initial_value.eval())
      with self.assertRaises(errors_impl.FailedPreconditionError):
        v1.eval()

      v2 = variables.Variable(
          math_ops.neg(v1.initialized_value()), dtype=dtypes.float32)
      self.assertEqual(v1.get_shape(), v2.get_shape())
      self.assertAllClose(np.negative(value), v2.initial_value.eval())

      # Once v2.initial_value.eval() has been called, v1 has effectively been
      # initialized.
      self.assertAllClose(value, v1.eval())

      with self.assertRaises(errors_impl.FailedPreconditionError):
        v2.eval()
      variables.global_variables_initializer().run()
      self.assertAllClose(np.negative(value), v2.eval())

def _SparseUpdate(variable, gradients, accum, linear, base_lr,
                  lr_power, l1, l2):
  """Sparse Update "variable", "accum", "linear" based on sparse "gradients".

  See the description in _Update.

  Args:
    variable: A Variable.
    gradients: A Sparse Tensor
    accum: A Variable containing the sum of the squares of gradients.
    linear: A Variable containing approximation info.
    base_lr: A constant represents base learning rate.
    lr_power: A constant is used to adjust learning rate.
    l1: A constant represents l1 regularization strength.
    l2: A constant represents l2 regularization strength.

  Returns:
    A group op including three ScatterUpdate ops:
      1. ScatterUpdate for "accum"
      2. ScatterUpdate for "linear"
      3. ScatterUpdate for "variable"
  """
  assert isinstance(gradients, ops.IndexedSlices)
  with ops.name_scope("sparse_update_" + variable.op.name) as scope:
    dtype = variable.dtype.base_dtype
    base_lr = ops.convert_to_tensor(base_lr, dtype=dtype)
    lr_power = ops.convert_to_tensor(lr_power, dtype=dtype)
    l1 = ops.convert_to_tensor(l1, dtype=dtype)
    l2 = ops.convert_to_tensor(l2, dtype=dtype)

    # Compute the new value for the accumulator
    previous_accum = array_ops.gather(accum, gradients.indices)
    sqr_grad = gradients.values * gradients.values
    accum_updated = sqr_grad + previous_accum

    # Compute the new linear
    neg_lr_power = math_ops.neg(lr_power)
    sigma = math_ops.pow(accum_updated, neg_lr_power) - math_ops.pow(
        previous_accum, neg_lr_power)
    sigma /= base_lr
    variable_slice = array_ops.gather(variable, gradients.indices)
    proximal_adjust = sigma * variable_slice
    linear_slice = array_ops.gather(linear, gradients.indices)
    linear_updated = linear_slice + gradients.values - proximal_adjust

    # Compute the new "variable"
    variable_updated = _Compute(accum_updated, linear_updated, base_lr,
                                lr_power, l1, l2)

    with ops.control_dependencies([sigma]):
      accum_update_op = state_ops.scatter_update(accum, gradients.indices,
                                                accum_updated)
    linear_update_op = state_ops.scatter_update(linear, gradients.indices,
                                               linear_updated)
    variable_update_op = state_ops.scatter_update(variable, gradients.indices,
                                                 variable_updated)
    group_op = control_flow_ops.group(linear_update_op, accum_update_op,
                                      variable_update_op, name=scope)
    return group_op

def natural_exp_decay(learning_rate, global_step, decay_steps, decay_rate,
                      staircase=False, name=None):
  """Applies natural exponential decay to the initial learning rate.

  When training a model, it is often recommended to lower the learning rate as
  the training progresses.  This function applies an exponential decay function
  to a provided initial learning rate.  It requires an `global_step` value to
  compute the decayed learning rate.  You can just pass a TensorFlow variable
  that you increment at each training step.

  The function returns the decayed learning rate.  It is computed as:

  ```python
  decayed_learning_rate = learning_rate * exp(-decay_rate * global_step)
  ```

  Example: decay exponetially with a base of 0.96:

  ```python
  ...
  global_step = tf.Variable(0, trainable=False)
  learning_rate = 0.1
  k = 0.5
  learning_rate = tf.train.exponential_time_decay(learning_rate, global_step, k)

  # Passing global_step to minimize() will increment it at each step.
  learning_step = (
      tf.train.GradientDescentOptimizer(learning_rate)
      .minimize(...my loss..., global_step=global_step)
  )
  ```

  Args:
    learning_rate: A scalar `float32` or `float64` `Tensor` or a
      Python number.  The initial learning rate.
    global_step: A Python number.
      Global step to use for the decay computation.  Must not be negative.
    decay_rate: A Python number.  The decay rate.
    name: String.  Optional name of the operation.  Defaults to
      'ExponentialTimeDecay'

  Returns:
    A scalar `Tensor` of the same type as `learning_rate`.  The decayed
    learning rate.
  """
  with ops.name_scope(name, "NaturalExpDecay",
                      [learning_rate, global_step, decay_rate]) as name:
    learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate")
    dtype = learning_rate.dtype
    global_step = math_ops.cast(global_step, dtype)
    decay_steps = math_ops.cast(decay_steps, dtype)
    decay_rate = math_ops.cast(decay_rate, dtype)
    p = global_step / decay_steps
    if staircase:
      p = math_ops.floor(p)
    exponent = math_ops.exp(math_ops.mul(math_ops.neg(decay_rate), p))
    return math_ops.mul(learning_rate, exponent, name=name)

def _Update(variable, gradients, accum, linear, base_lr, lr_power, l1, l2):
  """Update "variable", "accum", "linear" based on "gradients".

  Some notations here: "variable" as W, "accum" as N, "linear" as Z,
                       "gradients" as G, N(t) means "accum" at t-step.
  Assuming lr_power = -0.5 which means using adagrad learning rate.
  "accum" updates as: N = N + G^2
  "linear" updates as: Z = Z + G - W * (sqrt(N(t)) - sqrt(N(t-1)))/base_lr
  REQUIRES: Dimensionality of variable, gradients, accum and linear
            must be same.

  Args:
    variable: A Variable.
    gradients: A Tensor of same shape as 'variable'.
    accum: A Variable containing the sum of the squares of gradients.
    linear: A Variable containing approximation info.
    base_lr: A constant represents base learning rate.
    lr_power: A constant is used to adjust learning rate.
    l1: A constant represents l1 regularization strength.
    l2: A constant represents l2 regularization strength.

  Returns:
    A group op including three Assign ops:
      1. Assign for "accum"
      2. Assign for "linear"
      3. Assign for "variable"
  """
  dtype = variable.dtype.base_dtype
  base_lr = ops.convert_to_tensor(base_lr, dtype=dtype)
  lr_power = ops.convert_to_tensor(lr_power, dtype=dtype)
  l1 = ops.convert_to_tensor(l1, dtype=dtype)
  l2 = ops.convert_to_tensor(l2, dtype=dtype)
  # Compute the new accumulator
  sqr_grad = math_ops.square(gradients)
  accum_updated = sqr_grad + accum
  # Compute the new linear
  neg_lr_power = math_ops.neg(lr_power)
  sigma = math_ops.pow(accum_updated, neg_lr_power) - math_ops.pow(
      accum, neg_lr_power)
  sigma /= base_lr
  proximal_adjust = sigma * variable
  linear_updated = linear + gradients - proximal_adjust
  # Compute the "variable"
  variable_updated = _Compute(accum_updated, linear_updated, base_lr,
                              lr_power, l1, l2)

  with ops.control_dependencies([sigma]):
    accum_update_op = state_ops.assign(accum, accum_updated)
  linear_update_op = state_ops.assign(linear, linear_updated)
  variable_update_op = state_ops.assign(variable, variable_updated)
  group_op = control_flow_ops.group(linear_update_op, accum_update_op,
                                    variable_update_op)
  return group_op

def _flip_gradient_grad(op, grad):
    """The gradients for `flip_gradient`.

    Args:
        op: The `flip_gradient` `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 `flip_gradient` op.

    Returns:
        Gradients with respect to the input of `flip_gradient`.
    """
    s = op.inputs[1]
    return [math_ops.neg(grad) * s, None]

  def setUp(self):
    self.a = variables.Variable(2.0, name="a")
    self.b = variables.Variable(3.0, name="b")

    self.c = math_ops.mul(self.a, self.b, name="c")  # Should be 6.0.
    self.d = math_ops.mul(self.a, self.a, name="d")  # Should be 4.0.

    self.e = math_ops.mul(self.d, self.c, name="e")  # Should be 24.0.

    self.f_y = constant_op.constant(0.30, name="f_y")
    self.f = math_ops.div(self.b, self.f_y, name="f")  # Should be 10.0.

    # The there nodes x, y and z form a graph with "cross-links" in. I.e., x
    # and y are both direct inputs to z, but x is also a direct input to y.
    self.x = variables.Variable(2.0, name="x")  # Should be 2.0
    self.y = math_ops.neg(self.x, name="y")  # Should be -2.0.

    self.z = math_ops.mul(self.x, self.y, name="z")  # Should be -4.0.

    self.sess = session.Session()
    self.sess.run(variables.global_variables_initializer())

    self.sess = session.Session()
    self.sess.run(variables.global_variables_initializer())

 def training_loss(self, features, labels):
   return math_ops.neg(self.average_size())

 def training_loss(self, features, labels, data_spec=None,
                   name='training_loss'):
   return math_ops.neg(self.average_size(), name=name)