python类div()的实例源码

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 setUp(self):
    super(CoreBinaryOpsTest, self).setUp()

    self.x_probs_broadcast_tensor = array_ops.reshape(
        self.x_probs_lt.tensor, [self.x_size, 1, self.probs_size])

    self.channel_probs_broadcast_tensor = array_ops.reshape(
        self.channel_probs_lt.tensor, [1, self.channel_size, self.probs_size])

    # == and != are not element-wise for tf.Tensor, so they shouldn't be
    # elementwise for LabeledTensor, either.
    self.ops = [
        ('add', operator.add, math_ops.add, core.add),
        ('sub', operator.sub, math_ops.subtract, core.sub),
        ('mul', operator.mul, math_ops.multiply, core.mul),
        ('div', operator.truediv, math_ops.div, core.div),
        ('mod', operator.mod, math_ops.mod, core.mod),
        ('pow', operator.pow, math_ops.pow, core.pow_function),
        ('equal', None, math_ops.equal, core.equal),
        ('less', operator.lt, math_ops.less, core.less),
        ('less_equal', operator.le, math_ops.less_equal, core.less_equal),
        ('not_equal', None, math_ops.not_equal, core.not_equal),
        ('greater', operator.gt, math_ops.greater, core.greater),
        ('greater_equal', operator.ge, math_ops.greater_equal,
         core.greater_equal),
    ]
    self.test_lt_1 = self.x_probs_lt
    self.test_lt_2 = self.channel_probs_lt
    self.test_lt_1_broadcast = self.x_probs_broadcast_tensor
    self.test_lt_2_broadcast = self.channel_probs_broadcast_tensor
    self.broadcast_axes = [self.a0, self.a1, self.a3]

def __truediv__(self, other):
    return div(self, other)

def __rtruediv__(self, other):
    return div(other, self)

def _variational_recurrent_dropout_value(
      self, index, value, noise, keep_prob):
    """Performs dropout given the pre-calculated noise tensor."""
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob + noise

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = math_ops.floor(random_tensor)
    ret = math_ops.div(value, keep_prob) * binary_tensor
    ret.set_shape(value.get_shape())
    return ret

def _variational_recurrent_dropout_value(
      self, index, value, noise, keep_prob):
    """Performs dropout given the pre-calculated noise tensor."""
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob + noise

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = math_ops.floor(random_tensor)
    ret = math_ops.div(value, keep_prob) * binary_tensor
    ret.set_shape(value.get_shape())
    return ret

def _variational_recurrent_dropout_value(
      self, index, value, noise, keep_prob):
    """Performs dropout given the pre-calculated noise tensor."""
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob + noise

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = math_ops.floor(random_tensor)
    ret = math_ops.div(value, keep_prob) * binary_tensor
    ret.set_shape(value.get_shape())
    return ret

def _num_present(losses, weight, per_batch=False):
  """Computes the number of elements in the loss function induced by `weight`.

  A given weight tensor induces different numbers of usable elements in the
  `losses` tensor. The `weight` tensor is broadcast across `losses` for all
  possible dimensions. For example, if `losses` is a tensor of dimension
  [4, 5, 6, 3] and weight is a tensor of size [4, 5], then weight is, in effect,
  tiled to match the size of `losses`. Following this effective tile, the total
  number of present elements is the number of non-zero weights.

  Args:
    losses: A tensor of size [batch_size, d1, ... dN].
    weight: A tensor of size [1] or [batch_size, d1, ... dK] where K < N.
    per_batch: Whether to return the number of elements per batch or as a sum
      total.

  Returns:
    The number of present (non-zero) elements in the losses tensor. If
      `per_batch` is True, the value is returned as a tensor of size
      [batch_size]. Otherwise, a single scalar tensor is returned.
  """
  # To ensure that dims of [2, 1] gets mapped to [2,]
  weight = array_ops.squeeze(weight)

  # If the weight is a scalar, its easy to compute:
  if weight.get_shape().ndims == 0:
    batch_size = array_ops.reshape(array_ops.slice(array_ops.shape(losses),
                                                   [0], [1]), [])
    num_per_batch = math_ops.div(math_ops.to_float(array_ops.size(losses)),
                                 math_ops.to_float(batch_size))
    num_per_batch = math_ops.select(math_ops.equal(weight, 0),
                                    0.0, num_per_batch)
    num_per_batch = math_ops.mul(array_ops.ones(
        array_ops.reshape(batch_size, [1])), num_per_batch)
    return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)

  # First, count the number of nonzero weights:
  if weight.get_shape().ndims >= 1:
    reduction_indices = list(range(1, weight.get_shape().ndims))
    num_nonzero_per_batch = math_ops.reduce_sum(
        math_ops.to_float(math_ops.not_equal(weight, 0)),
        reduction_indices=reduction_indices)

  # Next, determine the number of elements that weight would broadcast to:
  broadcast_dims = array_ops.slice(array_ops.shape(losses),
                                   [weight.get_shape().ndims], [-1])
  num_to_broadcast = math_ops.to_float(math_ops.reduce_prod(broadcast_dims))

  num_per_batch = math_ops.mul(num_nonzero_per_batch, num_to_broadcast)
  return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)

def streaming_mean_relative_error(predictions, labels, normalizer, weights=None,
                                  metrics_collections=None,
                                  updates_collections=None,
                                  name=None):
  """Computes the mean relative error by normalizing with the given values.

  The `streaming_mean_relative_error` function creates two local variables,
  `total` and `count` that are used to compute the mean relative absolute error.
  This average is weighted by `weights`, and it is ultimately returned as
  `mean_relative_error`: an idempotent operation that simply divides `total` by
  `count`.

  For estimation of the metric over a stream of data, the function creates an
  `update_op` operation that updates these variables and returns the
  `mean_reative_error`. Internally, a `relative_errors` operation divides the
  absolute value of the differences between `predictions` and `labels` by the
  `normalizer`. Then `update_op` increments `total` with the reduced sum of the
  product of `weights` and `relative_errors`, and it increments `count` with the
  reduced sum of `weights`.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    predictions: A `Tensor` of arbitrary shape.
    labels: A `Tensor` of the same shape as `predictions`.
    normalizer: A `Tensor` of the same shape as `predictions`.
    weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
    metrics_collections: An optional list of collections that
      `mean_relative_error` should be added to.
    updates_collections: An optional list of collections that `update_op` should
      be added to.
    name: An optional variable_scope name.

  Returns:
    mean_relative_error: A tensor representing the current mean, the value of
      `total` divided by `count`.
    update_op: An operation that increments the `total` and `count` variables
      appropriately and whose value matches `mean_relative_error`.

  Raises:
    ValueError: If `predictions` and `labels` have mismatched shapes, or if
      `weights` is not `None` and its shape doesn't match `predictions`, or if
      either `metrics_collections` or `updates_collections` are not a list or
      tuple.
  """
  predictions, labels = metric_ops_util.remove_squeezable_dimensions(
      predictions, labels)
  predictions.get_shape().assert_is_compatible_with(labels.get_shape())

  predictions, normalizer = metric_ops_util.remove_squeezable_dimensions(
      predictions, normalizer)
  predictions.get_shape().assert_is_compatible_with(normalizer.get_shape())
  relative_errors = math_ops.select(
      math_ops.equal(normalizer, 0.0),
      array_ops.zeros_like(labels),
      math_ops.div(math_ops.abs(labels - predictions), normalizer))
  return streaming_mean(relative_errors, weights, metrics_collections,
                        updates_collections, name or 'mean_relative_error')

def _num_present(losses, weights, per_batch=False):
  """Computes the number of elements in the loss function induced by `weights`.

  A given weights tensor induces different numbers of usable elements in the
  `losses` tensor. The `weights` tensor is broadcast across `losses` for all
  possible dimensions. For example, if `losses` is a tensor of dimension
  [4, 5, 6, 3] and `weights` is a tensor of size [4, 5], then `weights` is, in
  effect, tiled to match the size of `losses`. Following this effective tile,
  the total number of present elements is the number of non-zero weights.

  Args:
    losses: A tensor of size [batch_size, d1, ... dN].
    weights: A tensor of size [1] or [batch_size, d1, ... dK] where K < N.
    per_batch: Whether to return the number of elements per batch or as a sum
      total.

  Returns:
    The number of present (non-zero) elements in the losses tensor. If
      `per_batch` is True, the value is returned as a tensor of size
      [batch_size]. Otherwise, a single scalar tensor is returned.
  """
  # If weights is a scalar, its easy to compute:
  if weights.get_shape().ndims == 0:
    batch_size = array_ops.reshape(array_ops.slice(array_ops.shape(losses),
                                                   [0], [1]), [])
    num_per_batch = math_ops.div(math_ops.to_float(array_ops.size(losses)),
                                 math_ops.to_float(batch_size))
    num_per_batch = math_ops.select(math_ops.equal(weights, 0),
                                    0.0, num_per_batch)
    num_per_batch = math_ops.mul(array_ops.ones(
        array_ops.reshape(batch_size, [1])), num_per_batch)
    return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)

  # First, count the number of nonzero weights:
  if weights.get_shape().ndims >= 1:
    reduction_indices = list(range(1, weights.get_shape().ndims))
    num_nonzero_per_batch = math_ops.reduce_sum(
        math_ops.to_float(math_ops.not_equal(weights, 0)),
        reduction_indices=reduction_indices)

  # Next, determine the number of elements that weight would broadcast to:
  broadcast_dims = array_ops.slice(array_ops.shape(losses),
                                   [weights.get_shape().ndims], [-1])
  num_to_broadcast = math_ops.to_float(math_ops.reduce_prod(broadcast_dims))

  num_per_batch = math_ops.mul(num_nonzero_per_batch, num_to_broadcast)
  return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)

def weighted_moving_average(value,
                            decay,
                            weight,
                            truediv=True,
                            collections=None,
                            name=None):
  """Compute the weighted moving average of `value`.

  Conceptually, the weighted moving average is:
    `moving_average(value * weight) / moving_average(weight)`,
  where a moving average updates by the rule
    `new_value = decay * old_value + (1 - decay) * update`
  Internally, this Op keeps moving average variables of both `value * weight`
  and `weight`.

  Args:
    value: A numeric `Tensor`.
    decay: A float `Tensor` or float value.  The moving average decay.
    weight:  `Tensor` that keeps the current value of a weight.
      Shape should be able to multiply `value`.
    truediv:  Boolean, if `True`, dividing by `moving_average(weight)` is
      floating point division.  If `False`, use division implied by dtypes.
    collections:  List of graph collections keys to add the internal variables
      `value * weight` and `weight` to.  Defaults to `[GraphKeys.VARIABLES]`.
    name: Optional name of the returned operation.
      Defaults to "WeightedMovingAvg".

  Returns:
    An Operation that updates and returns the weighted moving average.
  """
  # Unlike assign_moving_average, the weighted moving average doesn't modify
  # user-visible variables. It is the ratio of two internal variables, which are
  # moving averages of the updates.  Thus, the signature of this function is
  # quite different than assign_moving_average.
  if collections is None:
    collections = [ops.GraphKeys.VARIABLES]
  with variable_scope.variable_op_scope(
      [value, weight, decay], name, "WeightedMovingAvg") as scope:
    value_x_weight_var = variable_scope.get_variable(
        "value_x_weight",
        initializer=init_ops.zeros_initializer(value.get_shape(),
                                               dtype=value.dtype),
        trainable=False,
        collections=collections)
    weight_var = variable_scope.get_variable(
        "weight",
        initializer=init_ops.zeros_initializer(weight.get_shape(),
                                               dtype=weight.dtype),
        trainable=False,
        collections=collections)
    numerator = assign_moving_average(value_x_weight_var, value * weight, decay)
    denominator = assign_moving_average(weight_var, weight, decay)

    if truediv:
      return math_ops.truediv(numerator, denominator, name=scope.name)
    else:
      return math_ops.div(numerator, denominator, name=scope.name)

def _num_present(losses, weights, per_batch=False):
  """Computes the number of elements in the loss function induced by `weights`.

  A given weights tensor induces different numbers of usable elements in the
  `losses` tensor. The `weights` tensor is broadcast across `losses` for all
  possible dimensions. For example, if `losses` is a tensor of dimension
  [4, 5, 6, 3] and `weights` is a tensor of size [4, 5], then `weights` is, in
  effect, tiled to match the size of `losses`. Following this effective tile,
  the total number of present elements is the number of non-zero weights.

  Args:
    losses: A tensor of size [batch_size, d1, ... dN].
    weights: A tensor of size [1] or [batch_size, d1, ... dK] where K < N.
    per_batch: Whether to return the number of elements per batch or as a sum
      total.

  Returns:
    The number of present (non-zero) elements in the losses tensor. If
      `per_batch` is True, the value is returned as a tensor of size
      [batch_size]. Otherwise, a single scalar tensor is returned.
  """
  # If weights is a scalar, its easy to compute:
  if weights.get_shape().ndims == 0:
    batch_size = array_ops.reshape(array_ops.slice(array_ops.shape(losses),
                                                   [0], [1]), [])
    num_per_batch = math_ops.div(math_ops.to_float(array_ops.size(losses)),
                                 math_ops.to_float(batch_size))
    num_per_batch = array_ops.where(math_ops.equal(weights, 0),
                                    0.0, num_per_batch)
    num_per_batch = math_ops.multiply(array_ops.ones(
        array_ops.reshape(batch_size, [1])), num_per_batch)
    return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)

  # First, count the number of nonzero weights:
  if weights.get_shape().ndims >= 1:
    reduction_indices = list(range(1, weights.get_shape().ndims))
    num_nonzero_per_batch = math_ops.reduce_sum(
        math_ops.to_float(math_ops.not_equal(weights, 0)),
        reduction_indices=reduction_indices)

  # Next, determine the number of elements that weights would broadcast to:
  broadcast_dims = array_ops.slice(array_ops.shape(losses),
                                   [weights.get_shape().ndims], [-1])
  num_to_broadcast = math_ops.to_float(math_ops.reduce_prod(broadcast_dims))

  num_per_batch = math_ops.multiply(num_nonzero_per_batch, num_to_broadcast)
  return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)

def _define_maximization_operation(self, num_batches):
    """Maximization operations."""
    # TODO(xavigonzalvo): some of these operations could be moved to C++.
    # Compute the effective number of data points assigned to component k.
    with ops.control_dependencies(self._w):
      points_in_k = array_ops.squeeze(
          math_ops.add_n(self._points_in_k), squeeze_dims=[0])
      # Update alpha.
      if 'w' in self._params:
        final_points_in_k = points_in_k / num_batches
        num_examples = math_ops.to_float(math_ops.reduce_sum(final_points_in_k))
        self._alpha_op = self._alpha.assign(final_points_in_k /
                                            (num_examples + MEPS))
      else:
        self._alpha_op = control_flow_ops.no_op()
      self._train_ops = [self._alpha_op]

      # Update means.
      points_in_k_expanded = array_ops.reshape(points_in_k,
                                               [self._num_classes, 1, 1])
      if 'm' in self._params:
        self._means_op = self._means.assign(
            math_ops.div(
                math_ops.add_n(self._w_mul_x), points_in_k_expanded + MEPS))
      else:
        self._means_op = control_flow_ops.no_op()
      # means are (num_classes x 1 x dims)

      # Update covariances.
      with ops.control_dependencies([self._means_op]):
        b = math_ops.add_n(self._w_mul_x2) / (points_in_k_expanded + MEPS)
        new_covs = []
        for k in range(self._num_classes):
          mean = self._means.value()[k, :, :]
          square_mean = math_ops.matmul(mean, mean, transpose_a=True)
          new_cov = b[k, :, :] - square_mean + self._min_var
          if self._covariance_type == FULL_COVARIANCE:
            new_covs.append(array_ops.expand_dims(new_cov, 0))
          elif self._covariance_type == DIAG_COVARIANCE:
            new_covs.append(
                array_ops.expand_dims(array_ops.diag_part(new_cov), 0))
        new_covs = array_ops.concat(new_covs, 0)
        if 'c' in self._params:
          # Train operations don't need to take care of the means
          # because covariances already depend on it.
          with ops.control_dependencies([self._means_op, new_covs]):
            self._train_ops.append(
                state_ops.assign(
                    self._covs, new_covs, validate_shape=False))