Python math_ops.reduce_max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_max [as 別名]
def max(x, axis=None, keepdims=False):
  """Maximum value in a tensor.

  Arguments:
      x: A tensor or variable.
      axis: An integer, the axis to find maximum values.
      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 maximum values of `x`.
  """
  axis = _normalize_axis(axis, ndim(x))
  return math_ops.reduce_max(x, 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_max [as 別名]
def _assert_non_singular(self):
    """Private default implementation of _assert_non_singular."""
    logging.warn(
        "Using (possibly slow) default implementation of assert_non_singular."
        "  Requires conversion to a dense matrix and O(N^3) operations.")
    if self._can_use_cholesky():
      return self.assert_positive_definite()
    else:
      singular_values = linalg_ops.svd(
          self._get_cached_dense_matrix(), compute_uv=False)
      # TODO(langmore) Add .eig and .cond as methods.
      cond = (math_ops.reduce_max(singular_values, axis=-1) /
              math_ops.reduce_min(singular_values, axis=-1))
      return check_ops.assert_less(
          cond,
          self._max_condition_number_to_be_non_singular(),
          message="Singular matrix up to precision epsilon.")
    raise NotImplementedError("assert_non_singular is not implemented.") 

# 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_max [as 別名]
def __call__(self, inputs, state, scope=None):
    """Build the CrfDecodeForwardRnnCell.

    Args:
      inputs: A [batch_size, num_tags] matrix of unary potentials.
      state: A [batch_size, num_tags] matrix containing the previous step's
            score values.
      scope: Unused variable scope of this cell.

    Returns:
      backpointers: [batch_size, num_tags], containing backpointers.
      new_state: [batch_size, num_tags], containing new score values.
    """
    # For simplicity, in shape comments, denote:
    # 'batch_size' by 'B', 'max_seq_len' by 'T' , 'num_tags' by 'O' (output).
    state = array_ops.expand_dims(state, 2)                         # [B, O, 1]

    # This addition op broadcasts self._transitions_params along the zeroth
    # dimension and state along the second dimension.
    # [B, O, 1] + [1, O, O] -> [B, O, O]
    transition_scores = state + self._transition_params             # [B, O, O]
    new_state = inputs + math_ops.reduce_max(transition_scores, [1])  # [B, O]
    backpointers = math_ops.argmax(transition_scores, 1)
    backpointers = math_ops.cast(backpointers, dtype=dtypes.int32)    # [B, O]
    return backpointers, new_state 

# 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_max [as 別名]
def masked_maximum(data, mask, dim=1):
  """Computes the axis wise maximum over chosen elements.

  Args:
    data: 2-D float `Tensor` of size [n, m].
    mask: 2-D Boolean `Tensor` of size [n, m].
    dim: The dimension over which to compute the maximum.

  Returns:
    masked_maximums: N-D `Tensor`.
      The maximized dimension is of size 1 after the operation.
  """
  axis_minimums = math_ops.reduce_min(data, dim, keepdims=True)
  masked_maximums = math_ops.reduce_max(
      math_ops.multiply(data - axis_minimums, mask), dim,
      keepdims=True) + axis_minimums
  return masked_maximums 

# 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_max [as 別名]
def masked_minimum(data, mask, dim=1):
  """Computes the axis wise minimum over chosen elements.

  Args:
    data: 2-D float `Tensor` of size [n, m].
    mask: 2-D Boolean `Tensor` of size [n, m].
    dim: The dimension over which to compute the minimum.

  Returns:
    masked_minimums: N-D `Tensor`.
      The minimized dimension is of size 1 after the operation.
  """
  axis_maximums = math_ops.reduce_max(data, dim, keepdims=True)
  masked_minimums = math_ops.reduce_min(
      math_ops.multiply(data - axis_maximums, mask), dim,
      keepdims=True) + axis_maximums
  return masked_minimums 

# 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_max [as 別名]
def masked_maximum(data, mask, dim=1):
  """Computes the axis wise maximum over chosen elements.

  Args:
    data: 2-D float `Tensor` of size [n, m].
    mask: 2-D Boolean `Tensor` of size [n, m].
    dim: The dimension over which to compute the maximum.

  Returns:
    masked_maximums: N-D `Tensor`.
      The maximized dimension is of size 1 after the operation.
  """
  axis_minimums = math_ops.reduce_min(data, dim, keep_dims=True)
  masked_maximums = math_ops.reduce_max(
      math_ops.multiply(
          data - axis_minimums, mask), dim, keep_dims=True) + axis_minimums
  return masked_maximums 

# 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_max [as 別名]
def masked_minimum(data, mask, dim=1):
  """Computes the axis wise minimum over chosen elements.

  Args:
    data: 2-D float `Tensor` of size [n, m].
    mask: 2-D Boolean `Tensor` of size [n, m].
    dim: The dimension over which to compute the minimum.

  Returns:
    masked_minimums: N-D `Tensor`.
      The minimized dimension is of size 1 after the operation.
  """
  axis_maximums = math_ops.reduce_max(data, dim, keep_dims=True)
  masked_minimums = math_ops.reduce_min(
      math_ops.multiply(
          data - axis_maximums, mask), dim, keep_dims=True) + axis_maximums
  return masked_minimums 

# 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_max [as 別名]
def advanced_softmax(logits, mask=None):
    """ Computes softmax function manually.

    Avoids numeric overflow.

    Args:
        logits: A Tensor. The softmax will apply on the last dimension of it.
        mask: A Tensor with the same shape as `logits`.

    Returns: The softmax results.
    """
    num_shapes = logits.get_shape().ndims
    if mask is not None:
        scores_exp = math_ops.exp(logits - math_ops.reduce_max(logits, axis=num_shapes - 1, keepdims=True)) * mask
    else:
        scores_exp = math_ops.exp(logits - math_ops.reduce_max(logits, axis=num_shapes - 1, keepdims=True))
    scores_sum = math_ops.reduce_sum(scores_exp, axis=num_shapes - 1, keepdims=True)
    x_sm = scores_exp / scores_sum
    return x_sm 

# 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_max [as 別名]
def __call__(self, inputs, state, scope=None):
            """Build the CrfDecodeForwardRnnCell.
            Args:
              inputs: A [batch_size, num_tags] matrix of unary potentials.
              state: A [batch_size, num_tags] matrix containing the previous step's
                    score values.
              scope: Unused variable scope of this cell.
            Returns:
              backpointers: [batch_size, num_tags], containing backpointers.
              new_state: [batch_size, num_tags], containing new score values.
            """
            # For simplicity, in shape comments, denote:
            # 'batch_size' by 'B', 'max_seq_len' by 'T' , 'num_tags' by 'O' (output).
            state = array_ops.expand_dims(state, 2)  # [B, O, 1]

            # This addition op broadcasts self._transitions_params along the zeroth
            # dimension and state along the second dimension.
            # [B, O, 1] + [1, O, O] -> [B, O, O]
            transition_scores = state + self._transition_params  # [B, O, O]
            new_state = inputs + math_ops.reduce_max(transition_scores, [1])  # [B, O]
            backpointers = math_ops.argmax(transition_scores, 1)
            backpointers = math_ops.cast(backpointers, dtype=dtypes.int32)  # [B, O]
            return backpointers, new_state 

# 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_max [as 別名]
def visualize(self):
    """Multi-channel visualization of densities as images.

    Creates and returns an image summary visualizing the current probabilty
    density estimates. The image contains one row for each channel. Within each
    row, the pixel intensities are proportional to probability values, and each
    row is centered on the median of the corresponding distribution.

    Returns:
      The created image summary.
    """
    with ops.name_scope(self._name_scope()):
      image = self._pmf
      image *= 255 / math_ops.reduce_max(image, axis=1, keepdims=True)
      image = math_ops.cast(image + .5, dtypes.uint8)
      image = image[None, :, :, None]
    return summary.image("pmf", image, max_outputs=1) 

# 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_max [as 別名]
def call(self, inputs):
    inputs = ops.convert_to_tensor(inputs)
    shape = inputs.get_shape().as_list()
    num_channels = shape[self.axis]
    if num_channels % self.num_units:
      raise ValueError('number of features({}) is not '
               'a multiple of num_units({})'
               .format(num_channels, self.num_units))
    shape[self.axis] = -1
    shape += [num_channels // self.num_units]

    # Dealing with batches with arbitrary sizes
    for i in range(len(shape)):
      if shape[i] is None:
        shape[i] = gen_array_ops.shape(inputs)[i]
    outputs = math_ops.reduce_max(gen_array_ops.reshape(inputs, shape), -1, keep_dims=False)

    return outputs 

# 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_max [as 別名]
def _max_condition_number_to_be_non_singular(self):
    """Return the maximum condition number that we consider nonsingular."""
    with ops.name_scope("max_nonsingular_condition_number"):
      dtype_eps = np.finfo(self.dtype.as_numpy_dtype).eps
      eps = math_ops.cast(
          math_ops.reduce_max([
              100.,
              math_ops.cast(self.range_dimension_tensor(), self.dtype),
              math_ops.cast(self.domain_dimension_tensor(), self.dtype)
          ]), self.dtype) * dtype_eps
      return 1. / eps 

# 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_max [as 別名]
def test_name(self):
    result_lt = ops.reduce_max(self.original_lt, {'channel'})
    self.assertIn('lt_reduce_max', result_lt.name) 

# 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_max [as 別名]
def test(self):
    result_lt = ops.reduce_max(self.original_lt, {'channel'})
    golden_lt = core.LabeledTensor(
        math_ops.reduce_max(self.original_lt.tensor, 1),
        [self.a0, self.a2, self.a3])
    self.assertLabeledTensorsEqual(result_lt, golden_lt) 

# 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_max [as 別名]
def _sample_max(values):
  """Max over sample indices.  In this module this is always [0]."""
  return math_ops.reduce_max(values, reduction_indices=[0])