Python math_ops.to_float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 to_float [as 别名]
def _covariance(x, diag):
  """Defines the covariance operation of a matrix.

  Args:
    x: a matrix Tensor. Dimension 0 should contain the number of examples.
    diag: if True, it computes the diagonal covariance.

  Returns:
    A Tensor representing the covariance of x. In the case of
  diagonal matrix just the diagonal is returned.
  """
  num_points = math_ops.to_float(array_ops.shape(x)[0])
  x -= math_ops.reduce_mean(x, 0, keep_dims=True)
  if diag:
    cov = math_ops.reduce_sum(
        math_ops.square(x), 0, keep_dims=True) / (num_points - 1)
  else:
    cov = math_ops.matmul(x, x, transpose_a=True) / (num_points - 1)
  return cov 

# 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 to_float [as 别名]
def _define_full_covariance_probs(self, shard_id, shard):
    """Defines the full covariance probabilties per example in a class.

    Updates a matrix with dimension num_examples X num_classes.

    Args:
      shard_id: id of the current shard.
      shard: current data shard, 1 X num_examples X dimensions.
    """
    diff = shard - self._means
    cholesky = linalg_ops.cholesky(self._covs + self._min_var)
    log_det_covs = 2.0 * math_ops.reduce_sum(
        math_ops.log(array_ops.matrix_diag_part(cholesky)), 1)
    x_mu_cov = math_ops.square(
        linalg_ops.matrix_triangular_solve(
            cholesky, array_ops.transpose(
                diff, perm=[0, 2, 1]), lower=True))
    diag_m = array_ops.transpose(math_ops.reduce_sum(x_mu_cov, 1))
    self._probs[shard_id] = -0.5 * (diag_m + math_ops.to_float(self._dimensions)
                                    * math_ops.log(2 * np.pi) + log_det_covs) 

# 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 to_float [as 别名]
def _lengths_to_masks(lengths, max_length):
  """Creates a binary matrix that can be used to mask away padding.

  Args:
    lengths: A vector of integers representing lengths.
    max_length: An integer indicating the maximum length. All values in
      lengths should be less than max_length.
  Returns:
    masks: Masks that can be used to get rid of padding.
  """
  tiled_ranges = array_ops.tile(
      array_ops.expand_dims(math_ops.range(max_length), 0),
      [array_ops.shape(lengths)[0], 1])
  lengths = array_ops.expand_dims(lengths, 1)
  masks = math_ops.to_float(
      math_ops.to_int64(tiled_ranges) < math_ops.to_int64(lengths))
  return masks 

# 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 to_float [as 别名]
def loss(self, data, labels):
    """The loss to minimize while training."""

    if self.is_regression:
      diff = self.training_inference_graph(data) - math_ops.to_float(labels)
      mean_squared_error = math_ops.reduce_mean(diff * diff)
      root_mean_squared_error = math_ops.sqrt(mean_squared_error, name="loss")
      loss = root_mean_squared_error
    else:
      loss = math_ops.reduce_mean(
          nn_ops.sparse_softmax_cross_entropy_with_logits(
              labels=array_ops.squeeze(math_ops.to_int32(labels)),
              logits=self.training_inference_graph(data)),
          name="loss")
    if self.regularizer:
      loss += layers.apply_regularization(self.regularizer,
                                          variables.trainable_variables())
    return loss 

# 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 to_float [as 别名]
def _length_penalty(sequence_lengths, penalty_factor):
  """Calculates the length penalty. See https://arxiv.org/abs/1609.08144.

  Args:
    sequence_lengths: The sequence length of all hypotheses, a tensor
      of shape [beam_size, vocab_size].
    penalty_factor: A scalar that weights the length penalty.

  Returns:
    The length penalty factor, a tensor fo shape [beam_size].
  """
  penalty_factor = ops.convert_to_tensor(penalty_factor, name="penalty_factor")
  penalty_factor.set_shape(())  # penalty should be a scalar.
  static_penalty = tensor_util.constant_value(penalty_factor)
  if static_penalty is not None and static_penalty == 0:
    return 1.0
  return math_ops.div((5. + math_ops.to_float(sequence_lengths))
                      **penalty_factor, (5. + 1.)**penalty_factor) 

# 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 to_float [as 别名]
def _streaming_auc(predictions, labels, weights=None, class_id=None,
                   curve="ROC"):
  # pylint: disable=missing-docstring
  predictions = math_ops.to_float(predictions)
  if labels.dtype.base_dtype != dtypes.bool:
    logging.warning("Casting %s labels to bool.", labels.dtype)
    labels = math_ops.cast(labels, dtypes.bool)
  weights = _float_weights_or_none(weights)
  if weights is not None:
    weights = weights_broadcast_ops.broadcast_weights(weights, predictions)
  if class_id is not None:
    if weights is not None:
      weights = weights[:, class_id]
    predictions = predictions[:, class_id]
    labels = labels[:, class_id]
  return metrics_lib.streaming_auc(
      predictions, labels, weights=weights, curve=curve) 

# 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 to_float [as 别名]
def create_test_input(batch_size, height, width, channels):
  """Create test input tensor.

  Args:
    batch_size: The number of images per batch or `None` if unknown.
    height: The height of each image or `None` if unknown.
    width: The width of each image or `None` if unknown.
    channels: The number of channels per image or `None` if unknown.

  Returns:
    Either a placeholder `Tensor` of dimension
      [batch_size, height, width, channels] if any of the inputs are `None` or a
    constant `Tensor` with the mesh grid values along the spatial dimensions.
  """
  if None in [batch_size, height, width, channels]:
    return array_ops.placeholder(dtypes.float32,
                                 (batch_size, height, width, channels))
  else:
    return math_ops.to_float(
        np.tile(
            np.reshape(
                np.reshape(np.arange(height), [height, 1]) + np.reshape(
                    np.arange(width), [1, width]), [1, height, width, 1]),
            [batch_size, 1, 1, channels])) 

# 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 to_float [as 别名]
def _IRFFTGradHelper(rank, rfft_fn):
  """Returns a gradient function for an IRFFT of the provided rank."""
  # Can't happen because we don't register a gradient for IRFFT3D.
  assert rank in (1, 2), "Gradient for IRFFT3D is not implemented."

  def _Grad(op, grad):
    """A gradient function for IRFFT with the provided `rank` and `rfft_fn`."""
    # Generate a simple mask like [1.0, 2.0, ..., 2.0, 1.0] for even-length FFTs
    # and [1.0, 2.0, ..., 2.0] for odd-length FFTs. To reduce extra ops in the
    # graph we special-case the situation where the FFT length and last
    # dimension of the input are known at graph construction time.
    fft_length = op.inputs[1]
    is_odd = math_ops.mod(fft_length[-1], 2)
    input_last_dimension = array_ops.shape(op.inputs[0])[-1]
    mask = array_ops.concat(
        [[1.0], 2.0 * array_ops.ones([input_last_dimension - 2 + is_odd]),
         array_ops.ones([1 - is_odd])], 0)

    rsize = math_ops.reciprocal(math_ops.to_float(_FFTSizeForGrad(grad, rank)))

    # The gradient of IRFFT is the RFFT of the incoming gradient times a scaling
    # factor and a mask. The mask scales the gradient for the Hermitian
    # symmetric components of the RFFT by a factor of two, since these
    # components are de-duplicated in the RFFT.
    rfft = rfft_fn(grad, fft_length)
    return rfft * math_ops.cast(rsize * mask, dtypes.complex64), None

  return _Grad 

# 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 to_float [as 别名]
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 shape `[4, 5]`, then `weights` is,
  in effect, tiled to match the shape of `losses`. Following this effective
  tile, the total number of present elements is the number of non-zero weights.

  Args:
    losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
    weights: `Tensor` of shape `[]`, `[batch_size]` 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.
  """
  with ops.name_scope(None, "num_present", (losses, weights)) as scope:
    weights = math_ops.to_float(weights)
    present = array_ops.where(
        math_ops.equal(weights, 0.0),
        array_ops.zeros_like(weights),
        array_ops.ones_like(weights))
    present = weights_broadcast_ops.broadcast_weights(present, losses)
    if per_batch:
      return math_ops.reduce_sum(
          present, axis=math_ops.range(1, array_ops.rank(present)),
          keep_dims=True, name=scope)
    return math_ops.reduce_sum(present, name=scope) 

# 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 to_float [as 别名]
def absolute_difference(
    labels, predictions, weights=1.0, scope=None,
    loss_collection=ops.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
  """Adds an Absolute Difference loss 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
  shape `[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:
    labels: The ground truth output tensor, same dimensions as 'predictions'.
    predictions: The predicted outputs.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `losses` dimension).
    scope: The scope for the operations performed in computing the loss.
    loss_collection: collection to which this loss will be added.
    reduction: Type of reduction to apply to loss.

  Returns:
    Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same
    shape as `labels`; otherwise, it is scalar.

  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, "absolute_difference",
                      (predictions, labels, weights)) as scope:
    predictions = math_ops.to_float(predictions)
    labels = math_ops.to_float(labels)
    predictions.get_shape().assert_is_compatible_with(labels.get_shape())
    losses = math_ops.abs(math_ops.subtract(predictions, labels))
    return compute_weighted_loss(
        losses, weights, scope, loss_collection, reduction=reduction) 

# 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 to_float [as 别名]
def cosine_distance(
    labels, predictions, dim=None, weights=1.0, scope=None,
    loss_collection=ops.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
  """Adds a cosine-distance loss to the training procedure.

  Note that the function assumes that `predictions` and `labels` are already
  unit-normalized.

  Args:
    labels: `Tensor` whose shape matches 'predictions'
    predictions: An arbitrary matrix.
    dim: The dimension along which the cosine distance is computed.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `losses` dimension).
    scope: The scope for the operations performed in computing the loss.
    loss_collection: collection to which this loss will be added.
    reduction: Type of reduction to apply to loss.

  Returns:
    Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same
    shape as `labels`; otherwise, it is scalar.

  Raises:
    ValueError: If `predictions` shape doesn't match `labels` shape, or
      `weights` is `None`.
  """
  if dim is None:
    raise ValueError("`dim` cannot be None.")
  with ops.name_scope(scope, "cosine_distance_loss",
                      (predictions, labels, weights)) as scope:
    predictions = math_ops.to_float(predictions)
    labels = math_ops.to_float(labels)
    predictions.get_shape().assert_is_compatible_with(labels.get_shape())

    radial_diffs = math_ops.multiply(predictions, labels)
    losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(dim,), keep_dims=True)
    return compute_weighted_loss(
        losses, weights, scope, loss_collection, reduction=reduction) 

# 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 to_float [as 别名]
def hinge_loss(labels, logits, weights=1.0, scope=None,
               loss_collection=ops.GraphKeys.LOSSES,
               reduction=Reduction.SUM_BY_NONZERO_WEIGHTS):
  """Adds a hinge loss to the training procedure.

  Args:
    labels: The ground truth output tensor. Its shape should match the shape of
      logits. The values of the tensor are expected to be 0.0 or 1.0.
    logits: The logits, a float tensor.
    weights: Optional `Tensor` whose rank is either 0, or the same rank as
      `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
      be either `1`, or the same as the corresponding `losses` dimension).
    scope: The scope for the operations performed in computing the loss.
    loss_collection: collection to which the loss will be added.
    reduction: Type of reduction to apply to loss.

  Returns:
    Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same
    shape as `labels`; otherwise, it is scalar.

  Raises:
    ValueError: If the shapes of `logits` and `labels` don't match.
  """
  with ops.name_scope(scope, "hinge_loss", (logits, labels)) as scope:
    logits = math_ops.to_float(logits)
    labels = math_ops.to_float(labels)
    logits.get_shape().assert_is_compatible_with(labels.get_shape())
    # We first need to convert binary labels to -1/1 labels (as floats).
    all_ones = array_ops.ones_like(labels)
    labels = math_ops.subtract(2 * labels, all_ones)
    losses = nn_ops.relu(
        math_ops.subtract(all_ones, math_ops.multiply(labels, logits)))
    return compute_weighted_loss(
        losses, weights, scope, loss_collection, reduction=reduction) 

# 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 to_float [as 别名]
def _transform_feature(self, inputs):
    input_tensor = inputs.get(self.key)
    if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
      raise ValueError(
          'The corresponding Tensor of numerical column must be a Tensor. '
          'SparseTensor is not supported. key: {}'.format(self.key))
    if self.normalizer_fn is not None:
      input_tensor = self.normalizer_fn(input_tensor)
    return math_ops.to_float(input_tensor) 

# 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 to_float [as 别名]
def average_size(self):
    """Constructs a TF graph for evaluating the average size of a forest.

    Returns:
      The average number of nodes over the trees.
    """
    sizes = []
    for i in range(self.params.num_trees):
      with ops.device(self.variables.device_dummies[i].device):
        sizes.append(self.trees[i].size())
    return math_ops.reduce_mean(math_ops.to_float(array_ops.stack(sizes)))

  # pylint: disable=unused-argument 

# 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 to_float [as 别名]
def _get_loss(self, features, labels):
    """Constructs, caches, and returns the inference-based loss."""
    if self._loss is not None:
      return self._loss

    def _average_loss():
      probs = self.inference_graph(features)
      return math_ops.reduce_sum(self.loss_fn(
          probs, labels)) / math_ops.to_float(array_ops.shape(labels)[0])

    self._loss = control_flow_ops.cond(
        self.average_size() > 0, _average_loss,
        lambda: constant_op.constant(sys.maxsize, dtype=dtypes.float32))

    return self._loss