Python math_ops.reduce_mean方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
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 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def var(x, axis=None, keepdims=False):
  """Variance of a tensor, alongside the specified axis.

  Arguments:
      x: A tensor or variable.
      axis: An integer, the axis to compute the variance.
      keepdims: A boolean, whether to keep the dimensions or not.
          If `keepdims` is `False`, the rank of the tensor is reduced
          by 1. If `keepdims` is `True`,
          the reduced dimension is retained with length 1.

  Returns:
      A tensor with the variance of elements of `x`.
  """
  axis = _normalize_axis(axis, ndim(x))
  if x.dtype.base_dtype == dtypes_module.bool:
    x = math_ops.cast(x, floatx())
  m = math_ops.reduce_mean(x, reduction_indices=axis, keep_dims=True)
  devs_squared = math_ops.square(x - m)
  return math_ops.reduce_mean(
      devs_squared, reduction_indices=axis, keep_dims=keepdims) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
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 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 231, 231
    eval_height, eval_width = 281, 281
    num_classes = 1000
    with self.test_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = overfeat.overfeat(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = overfeat.overfeat(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 300, 400
    num_classes = 1000
    with self.test_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = alexnet.alexnet_v2(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = alexnet.alexnet_v2(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 4, 7, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 256, 256
    num_classes = 1000
    with self.test_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = vgg.vgg_16(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = vgg.vgg_16(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 231, 231
    eval_height, eval_width = 281, 281
    num_classes = 1000
    with self.cached_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = overfeat.overfeat(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = overfeat.overfeat(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEqual(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 300, 400
    num_classes = 1000
    with self.cached_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = alexnet.alexnet_v2(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = alexnet.alexnet_v2(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 4, 7, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEqual(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def testTrainEvalWithReuse(self):
    train_batch_size = 2
    eval_batch_size = 1
    train_height, train_width = 224, 224
    eval_height, eval_width = 256, 256
    num_classes = 1000
    with self.cached_session():
      train_inputs = random_ops.random_uniform(
          (train_batch_size, train_height, train_width, 3))
      logits, _ = vgg.vgg_a(train_inputs)
      self.assertListEqual(logits.get_shape().as_list(),
                           [train_batch_size, num_classes])
      variable_scope.get_variable_scope().reuse_variables()
      eval_inputs = random_ops.random_uniform(
          (eval_batch_size, eval_height, eval_width, 3))
      logits, _ = vgg.vgg_a(
          eval_inputs, is_training=False, spatial_squeeze=False)
      self.assertListEqual(logits.get_shape().as_list(),
                           [eval_batch_size, 2, 2, num_classes])
      logits = math_ops.reduce_mean(logits, [1, 2])
      predictions = math_ops.argmax(logits, 1)
      self.assertEqual(predictions.get_shape().as_list(), [eval_batch_size]) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
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(
              self.training_inference_graph(data),
              array_ops.squeeze(math_ops.to_int32(labels))),
          name="loss")
    if self.regularizer:
      loss += layers.apply_regularization(self.regularizer,
                                          variables.trainable_variables())
    return loss 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def _rescale_eval_loss(loss, weights):
  """Rescales evaluation loss according to the given weights.

  The rescaling is needed because in the training loss weights are not
  considered in the denominator, whereas  for the evaluation loss we should
  divide by the sum of weights.

  The rescaling factor is:
    R = sum_{i} 1 / sum_{i} w_{i}

  Args:
    loss: the scalar weighted loss.
    weights: weight coefficients. Either a scalar, or a `Tensor` of shape
      [batch_size].

  Returns:
    The given loss multiplied by the rescaling factor.
  """
  rescaling_factor = math_ops.reduce_mean(weights)
  return math_ops.div(loss, rescaling_factor) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def per_image_standardization(image):
  """Linearly scales `image` to have zero mean and unit norm.

  This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
  of all values in image, and
  `adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.

  `stddev` is the standard deviation of all values in `image`. It is capped
  away from zero to protect against division by 0 when handling uniform images.

  Args:
    image: 3-D tensor of shape `[height, width, channels]`.

  Returns:
    The standardized image with same shape as `image`.

  Raises:
    ValueError: if the shape of 'image' is incompatible with this function.
  """
  image = ops.convert_to_tensor(image, name='image')
  image = control_flow_ops.with_dependencies(
      _Check3DImage(image, require_static=False), image)
  num_pixels = math_ops.reduce_prod(array_ops.shape(image))

  image = math_ops.cast(image, dtype=dtypes.float32)
  image_mean = math_ops.reduce_mean(image)

  variance = (math_ops.reduce_mean(math_ops.square(image)) -
              math_ops.square(image_mean))
  variance = gen_nn_ops.relu(variance)
  stddev = math_ops.sqrt(variance)

  # Apply a minimum normalization that protects us against uniform images.
  min_stddev = math_ops.rsqrt(math_ops.cast(num_pixels, dtypes.float32))
  pixel_value_scale = math_ops.maximum(stddev, min_stddev)
  pixel_value_offset = image_mean

  image = math_ops.subtract(image, pixel_value_offset)
  image = math_ops.div(image, pixel_value_scale)
  return image 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def zero_fraction(value, name=None):
  """Returns the fraction of zeros in `value`.

  If `value` is empty, the result is `nan`.

  This is useful in summaries to measure and report sparsity.  For example,

  ```python
      z = tf.nn.relu(...)
      summ = tf.summary.scalar('sparsity', tf.nn.zero_fraction(z))
  ```

  Args:
    value: A tensor of numeric type.
    name: A name for the operation (optional).

  Returns:
    The fraction of zeros in `value`, with type `float32`.
  """
  with ops.name_scope(name, "zero_fraction", [value]):
    value = ops.convert_to_tensor(value, name="value")
    zero = constant_op.constant(0, dtype=value.dtype, name="zero")
    return math_ops.reduce_mean(
        math_ops.cast(math_ops.equal(value, zero), dtypes.float32))


# pylint: disable=redefined-builtin 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
def _do_layer_inference(self, layer, data):

    # If this is a collection of layers, return the mean of their inference
    # results.
    if isinstance(layer, collections.Iterable):
      return math_ops.reduce_mean(
          array_ops.stack([l.inference_graph(data) for l in layer]), 0)
    # If this is a single layer, return its inference result.
    else:
      return layer.inference_graph(data) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import reduce_mean [as 別名]
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