Python math_ops.multiply函数代码示例

  def __call__(self, step):
    with ops.name_scope(
        self.name, "PolynomialDecay",
        [self.initial_learning_rate, step, self.decay_steps,
         self.end_learning_rate, self.power]
    ) as name:
      initial_learning_rate = ops.convert_to_tensor(
          self.initial_learning_rate, name="initial_learning_rate")
      dtype = initial_learning_rate.dtype
      end_learning_rate = math_ops.cast(self.end_learning_rate, dtype)
      power = math_ops.cast(self.power, dtype)

      global_step_recomp = math_ops.cast(step, dtype)
      decay_steps_recomp = math_ops.cast(self.decay_steps, dtype)
      if self.cycle:
        # Find the first multiple of decay_steps that is bigger than
        # global_step. If global_step is zero set the multiplier to 1
        multiplier = control_flow_ops.cond(
            math_ops.equal(global_step_recomp, 0), lambda: 1.0,
            lambda: math_ops.ceil(global_step_recomp / self.decay_steps))
        decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier)
      else:
        # Make sure that the global_step used is not bigger than decay_steps.
        global_step_recomp = math_ops.minimum(global_step_recomp,
                                              self.decay_steps)

      p = math_ops.div(global_step_recomp, decay_steps_recomp)
      return math_ops.add(
          math_ops.multiply(initial_learning_rate - end_learning_rate,
                            math_ops.pow(1 - p, power)),
          end_learning_rate,
          name=name)

def huber_loss(y_true, y_pred, delta=1.0):
  """Computes Huber loss value.

  For each value x in `error=y_true-y_pred`, the following is calculated:

  ```
  0.5 * x^2                  if |x| <= d
  0.5 * d^2 + d * (|x| - d)  if |x| > d
  ```
  where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss

  Args:
    y_true: tensor of true targets.
    y_pred: tensor of predicted targets.
    delta: A float, the point where the Huber loss function changes from a
      quadratic to linear.

  Returns:
    Tensor with one scalar loss entry per sample.
  """
  y_pred = math_ops.cast(y_pred, dtype=K.floatx())
  y_true = math_ops.cast(y_true, dtype=K.floatx())
  error = math_ops.subtract(y_pred, y_true)
  abs_error = math_ops.abs(error)
  quadratic = math_ops.minimum(abs_error, delta)
  linear = math_ops.subtract(abs_error, quadratic)
  return math_ops.add(
      math_ops.multiply(
          ops.convert_to_tensor(0.5, dtype=quadratic.dtype),
          math_ops.multiply(quadratic, quadratic)),
      math_ops.multiply(delta, linear))

def logloss(y_true, y_pred):
  y_pred = ops.convert_to_tensor(y_pred)
  y_true = math_ops.cast(y_true, y_pred.dtype)
  losses = math_ops.multiply(y_true, math_ops.log(y_pred + K.epsilon()))
  losses += math_ops.multiply((1 - y_true),
                              math_ops.log(1 - y_pred + K.epsilon()))
  return K.mean(-losses, axis=-1)

  def setUp(self):
    """Test setup.

    Structure of the forward graph:
              f
             | |
        -----   -----
        |           |
        d           e
       | |         | |
    ---   ---------  ---
    |         |        |
    a         b        c

    Construct a backward graph using the GradientDescentOptimizer.
    """

    self.a = variables.Variable(1.0, name="a")
    self.b = variables.Variable(2.0, name="b")
    self.c = variables.Variable(4.0, name="c")
    self.d = math_ops.multiply(self.a, self.b, name="d")
    self.e = math_ops.multiply(self.b, self.c, name="e")
    self.f = math_ops.multiply(self.d, self.e, name="f")

    # Gradient descent optimizer that minimizes g.
    gradient_descent.GradientDescentOptimizer(0.01).minimize(
        self.f, name="optim")

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

def normalize_moments(counts, mean_ss, variance_ss, shift, name=None):
  """Calculate the mean and variance of based on the sufficient statistics.

  Args:
    counts: A `Tensor` containing a the total count of the data (one value).
    mean_ss: A `Tensor` containing the mean sufficient statistics: the (possibly
      shifted) sum of the elements to average over.
    variance_ss: A `Tensor` containing the variance sufficient statistics: the
      (possibly shifted) squared sum of the data to compute the variance over.
    shift: A `Tensor` containing the value by which the data is shifted for
      numerical stability, or `None` if no shift was performed.
    name: Name used to scope the operations that compute the moments.

  Returns:
    Two `Tensor` objects: `mean` and `variance`.
  """
  with ops.name_scope(name, "normalize", [counts, mean_ss, variance_ss, shift]):
    divisor = math_ops.reciprocal(counts, name="divisor")
    if shift is not None:
      shifted_mean = math_ops.multiply(mean_ss, divisor, name="shifted_mean")
      mean = math_ops.add(shifted_mean, shift, name="mean")
    else:  # no shift.
      shifted_mean = math_ops.multiply(mean_ss, divisor, name="mean")
      mean = shifted_mean
    variance = math_ops.subtract(math_ops.multiply(variance_ss, divisor),
                                 math_ops.square(shifted_mean),
                                 name="variance")
  return (mean, variance)

  def setUp(self):
    """Test setup.

    Structure of the forward graph:
              f
             | |
        -----   -----
        |           |
        d           e
       | |         | |
    ---   ---------  ---
    |         |        |
    a         b        c

    Construct a backward graph using the GradientDescentOptimizer.
    """

    self.a = variables.Variable(1.0, name="a")
    self.b = variables.Variable(2.0, name="b")
    self.c = variables.Variable(4.0, name="c")
    self.d = math_ops.multiply(self.a, self.b, name="d")
    self.e = math_ops.multiply(self.b, self.c, name="e")
    self.f = math_ops.multiply(self.d, self.e, name="f")

    # Gradient descent optimizer that minimizes g.
    gradient_descent.GradientDescentOptimizer(0.01).minimize(
        self.f, name="optim")

    rewriter_config = rewriter_config_pb2.RewriterConfig(
        disable_model_pruning=True)
    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
    config = config_pb2.ConfigProto(graph_options=graph_options)
    self.sess = session.Session(config=config)
    self.sess.run(variables.global_variables_initializer())

 def Foo():
   x = constant_op.constant(10.0, name="x")
   y = math_ops.multiply(x, c, name="y")
   # Regression test for b/122564611.
   z = math_ops.multiply(c, y, name="z")
   g = gradients_impl.gradients(z, x)
   return g[0]

  def setUp(self):
    self.a = variables.VariableV1(2.0, name="a")
    self.b = variables.VariableV1(3.0, name="b")

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

    self.e = math_ops.multiply(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.VariableV1(2.0, name="x")  # Should be 2.0
    self.y = math_ops.negative(self.x, name="y")  # Should be -2.0.

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

    rewriter_config = rewriter_config_pb2.RewriterConfig(
        disable_model_pruning=True,
        arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
        constant_folding=rewriter_config_pb2.RewriterConfig.OFF)
    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
    config = config_pb2.ConfigProto(graph_options=graph_options)
    self.sess = session.Session(config=config)
    self.sess.run(variables.global_variables_initializer())

def accuracy(predictions, labels, weights=None):
  """Computes the percentage of times that predictions matches labels.

  Args:
    predictions: the predicted values, a `Tensor` whose dtype and shape
                 matches 'labels'.
    labels: the ground truth values, a `Tensor` of any shape and
            bool, integer, or string dtype.
    weights: None or `Tensor` of float values to reweight the accuracy.

  Returns:
    Accuracy `Tensor`.

  Raises:
    ValueError: if dtypes don't match or
                if dtype is not bool, integer, or string.
  """
  if not (labels.dtype.is_integer or
          labels.dtype in (dtypes.bool, dtypes.string)):
    raise ValueError(
        'Labels should have bool, integer, or string dtype, not %r' %
        labels.dtype)
  if not labels.dtype.is_compatible_with(predictions.dtype):
    raise ValueError('Dtypes of predictions and labels should match. '
                     'Given: predictions (%r) and labels (%r)' %
                     (predictions.dtype, labels.dtype))
  with ops.name_scope('accuracy', values=[predictions, labels]):
    is_correct = math_ops.cast(
        math_ops.equal(predictions, labels), dtypes.float32)
    if weights is not None:
      is_correct = math_ops.multiply(is_correct, weights)
      num_values = math_ops.multiply(weights, array_ops.ones_like(is_correct))
      return math_ops.div(math_ops.reduce_sum(is_correct),
                          math_ops.reduce_sum(num_values))
    return math_ops.reduce_mean(is_correct)

  def test_graph_replace_gradients(self):
    ops.reset_default_graph()
    w = variables.VariableV1(0.0, name="w")
    y = math_ops.multiply(math_ops.multiply(w, w, name="mul1"), w, name="mul2")
    g = gradients_impl.gradients(y, w, name="grad")[0]

    # Extract the operations.
    replacement_ts = {w.value(): g}
    original_mul1_grad = (ops.get_default_graph().
                          get_operation_by_name("grad/mul1_grad/Mul_1"))

    # Should not raise exception.
    res = ge.graph_replace(g, replacement_ts, dst_scope="res")

    # Extract the operations after graph_replace.
    result_mul1_grad = (ops.get_default_graph().
                        get_operation_by_name("res/grad/mul1_grad/Mul_1"))

    # Make sure _original_ops are as expected.
    self.assertEqual(original_mul1_grad._original_op.name, u"mul1")
    self.assertEqual(result_mul1_grad._original_op.name, u"res/mul1")
    self.assertNotEqual(res.name, g.name)
    with session.Session() as sess:
      sess.run(variables.global_variables_initializer())
      g_val, res_val = sess.run([g, res])
    self.assertNear(g_val, 0.0, ERROR_TOLERANCE)
    self.assertNear(res_val, 0.0, ERROR_TOLERANCE)

def log_loss(predictions, labels=None, weights=1.0, epsilon=1e-7, scope=None):
  """Adds a Log Loss term to the training procedure.

  `weights` acts as a coefficient for the loss. If a scalar is provided, then
  the loss is simply scaled by the given value. If `weights` is a tensor of size
  [batch_size], then the total loss for each sample of the batch is rescaled
  by the corresponding element in the `weights` vector. If the shape of
  `weights` matches the shape of `predictions`, then the loss of each
  measurable element of `predictions` is scaled by the corresponding value of
  `weights`.

  Args:
    predictions: The predicted outputs.
    labels: The ground truth output tensor, same dimensions as 'predictions'.
    weights: Coefficients for the loss a scalar, a tensor of shape
      [batch_size] or a tensor whose shape matches `predictions`.
    epsilon: A small increment to add to avoid taking a log of zero.
    scope: The scope for the operations performed in computing the loss.

  Returns:
    A scalar `Tensor` representing the loss value.

  Raises:
    ValueError: If the shape of `predictions` doesn't match that of `labels` or
      if the shape of `weights` is invalid.
  """
  with ops.name_scope(scope, "log_loss",
                      [predictions, labels, weights]) as scope:
    predictions.get_shape().assert_is_compatible_with(labels.get_shape())
    predictions = math_ops.to_float(predictions)
    labels = math_ops.to_float(labels)
    losses = -math_ops.multiply(
        labels, math_ops.log(predictions + epsilon)) - math_ops.multiply(
            (1 - labels), math_ops.log(1 - predictions + epsilon))
    return compute_weighted_loss(losses, weights, scope=scope)

 def _Update_global_variables():
   local_vars = [v for g, v in grads_and_vars if g is not None]
   global_center_vars = [self._global_map[var] for var in local_vars]
   local_center_vars = [self._local_map[var] for var in local_vars]
   local_center_vars_update = []
   for lvar, var in zip(local_center_vars, global_center_vars):
     local_center_vars_update.append(lvar.assign(var))
   update_ops = []
   differences = []
   with ops.control_dependencies(local_center_vars_update):
     for v, lv in zip(local_vars, local_center_vars):
       with ops.device(v.device):
         differences.append(math_ops.subtract(v, lv))
     for lvar, diff in zip(local_vars, differences):
       with ops.device(lvar.device):
         update_ops.append(
             state_ops.assign_sub(lvar,
                                  math_ops.multiply(self._moving_rate,
                                                    diff)))
     for var, diff in zip(global_center_vars, differences):
       with ops.device(var.device):
         update_ops.append(
             state_ops.assign_add(var,
                                  math_ops.multiply(self._moving_rate,
                                                    diff)))
     if global_step:
       with ops.colocate_with(global_step):
         update_ops.append(state_ops.assign_add(global_step, 1))
   variable_update = control_flow_ops.group(*(update_ops))
   return variable_update

def _SquareGrad(op, grad):
  x = op.inputs[0]
  # Added control dependencies to prevent 2*x from being computed too early.
  with ops.control_dependencies([grad]):
    x = math_ops.conj(x)
    y = constant_op.constant(2.0, dtype=x.dtype)
    return math_ops.multiply(grad, math_ops.multiply(x, y))

  def decayed_lr(learning_rate, global_step, decay_steps, end_learning_rate,
                 power, cycle, name):
    """Helper to recompute learning rate; most helpful in eager-mode."""
    with ops.name_scope(
        name, "PolynomialDecay",
        [learning_rate, global_step, decay_steps, end_learning_rate, power]
    ) as name:
      learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate")
      dtype = learning_rate.dtype
      end_learning_rate = math_ops.cast(end_learning_rate, dtype)
      power = math_ops.cast(power, dtype)

      global_step_recomp = math_ops.cast(global_step, dtype)
      decay_steps_recomp = math_ops.cast(decay_steps, dtype)
      if cycle:
        # Find the first multiple of decay_steps that is bigger than
        # global_step. If global_step is zero set the multiplier to 1
        multiplier = control_flow_ops.cond(
            math_ops.equal(global_step_recomp, 0), lambda: 1.0,
            lambda: math_ops.ceil(global_step_recomp / decay_steps))
        decay_steps_recomp = math_ops.multiply(decay_steps_recomp, multiplier)
      else:
        # Make sure that the global_step used is not bigger than decay_steps.
        global_step_recomp = math_ops.minimum(global_step_recomp, decay_steps)

      p = math_ops.div(global_step_recomp, decay_steps_recomp)
      return math_ops.add(
          math_ops.multiply(learning_rate - end_learning_rate,
                            math_ops.pow(1 - p, power)),
          end_learning_rate,
          name=name)

  def unregularized_loss(self, examples):
    """Add operations to compute the loss (without the regularization loss).

    Args:
      examples: Examples to compute unregularized loss on.

    Returns:
      An Operation that computes mean (unregularized) loss for given set of
      examples.

    Raises:
      ValueError: if examples are not well defined.
    """
    self._assertSpecified([
        'example_labels', 'example_weights', 'sparse_features', 'dense_features'
    ], examples)
    self._assertList(['sparse_features', 'dense_features'], examples)
    with name_scope('sdca/unregularized_loss'):
      predictions = math_ops.cast(
          self._linear_predictions(examples), dtypes.float64)
      labels = math_ops.cast(
          internal_convert_to_tensor(examples['example_labels']),
          dtypes.float64)
      weights = math_ops.cast(
          internal_convert_to_tensor(examples['example_weights']),
          dtypes.float64)

      if self._options['loss_type'] == 'logistic_loss':
        return math_ops.reduce_sum(math_ops.multiply(
            sigmoid_cross_entropy_with_logits(labels=labels,
                                              logits=predictions),
            weights)) / math_ops.reduce_sum(weights)

      if self._options['loss_type'] == 'poisson_loss':
        return math_ops.reduce_sum(math_ops.multiply(
            log_poisson_loss(targets=labels, log_input=predictions),
            weights)) / math_ops.reduce_sum(weights)

      if self._options['loss_type'] in ['hinge_loss', 'smooth_hinge_loss']:
        # hinge_loss = max{0, 1 - y_i w*x} where y_i \in {-1, 1}. So, we need to
        # first convert 0/1 labels into -1/1 labels.
        all_ones = array_ops.ones_like(predictions)
        adjusted_labels = math_ops.subtract(2 * labels, all_ones)
        # Tensor that contains (unweighted) error (hinge loss) per
        # example.
        error = nn_ops.relu(
            math_ops.subtract(all_ones,
                              math_ops.multiply(adjusted_labels, predictions)))
        weighted_error = math_ops.multiply(error, weights)
        return math_ops.reduce_sum(weighted_error) / math_ops.reduce_sum(
            weights)

      # squared loss
      err = math_ops.subtract(labels, predictions)

      weighted_squared_err = math_ops.multiply(math_ops.square(err), weights)
      # SDCA squared loss function is sum(err^2) / (2*sum(weights))
      return (math_ops.reduce_sum(weighted_squared_err) /
              (2.0 * math_ops.reduce_sum(weights)))