Python gen_math_ops.not_equal方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import not_equal [as 別名]
def count_nonzero(input_tensor,
                  axis=None,
                  keep_dims=False,
                  dtype=dtypes.int64,
                  name=None,
                  reduction_indices=None):
  """Computes number of nonzero elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `axis`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `axis`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `axis` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  **NOTE** Floating point comparison to zero is done by exact floating point
  equality check.  Small values are **not** rounded to zero for purposes of
  the nonzero check.

  For example:

  ```python
  # 'x' is [[0, 1, 0]
  #         [1, 1, 0]]
  tf.count_nonzero(x) ==> 3
  tf.count_nonzero(x, 0) ==> [1, 2, 0]
  tf.count_nonzero(x, 1) ==> [1, 2]
  tf.count_nonzero(x, 1, keep_dims=True) ==> [[1], [2]]
  tf.count_nonzero(x, [0, 1]) ==> 3
  ```

  Args:
    input_tensor: The tensor to reduce. Should be of numeric type, or `bool`.
    axis: The dimensions to reduce. If `None` (the default),
      reduces all dimensions.
    keep_dims: If true, retains reduced dimensions with length 1.
    dtype: The output dtype; defaults to `tf.int64`.
    name: A name for the operation (optional).
    reduction_indices: The old (deprecated) name for axis.

  Returns:
    The reduced tensor (number of nonzero values).
  """
  with ops.name_scope(name, "count_nonzero", [input_tensor]):
    input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor")
    zero = input_tensor.dtype.as_numpy_dtype()
    return cast(
        reduce_sum(
            # int64 reduction happens on GPU
            to_int64(gen_math_ops.not_equal(input_tensor, zero)),
            axis=axis,
            keep_dims=keep_dims,
            reduction_indices=reduction_indices),
        dtype=dtype) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import not_equal [as 別名]
def count_nonzero(input_tensor, reduction_indices=None, keep_dims=False,
                  dtype=dtypes.int64, name=None):
  """Computes number of nonzero elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `reduction_indices`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `reduction_indices` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  **NOTE** Floating point comparison to zero is done by exact floating point
  equality check.  Small values are **not** rounded to zero for purposes of
  the nonzero check.

  For example:

  ```python
  # 'x' is [[0, 1, 0]
  #         [1, 1, 0]]
  tf.count_nonzero(x) ==> 3
  tf.count_nonzero(x, 0) ==> [1, 2, 0]
  tf.count_nonzero(x, 1) ==> [1, 2]
  tf.count_nonzero(x, 1, keep_dims=True) ==> [[1], [2]]
  tf.count_nonzero(x, [0, 1]) ==> 3
  ```

  Args:
    input_tensor: The tensor to reduce. Should be of numeric type, or `bool`.
    reduction_indices: The dimensions to reduce. If `None` (the default),
      reduces all dimensions.
    keep_dims: If true, retains reduced dimensions with length 1.
    dtype: The output dtype; defaults to `tf.int64`.
    name: A name for the operation (optional).

  Returns:
    The reduced tensor (number of nonzero values).
  """
  with ops.name_scope(name, "count_nonzero", [input_tensor]):
    input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor")
    zero = input_tensor.dtype.as_numpy_dtype()
    return cast(
        reduce_sum(
            # int64 reduction happens on GPU
            to_int64(gen_math_ops.not_equal(input_tensor, zero)),
            reduction_indices=reduction_indices,
            keep_dims=keep_dims),
        dtype=dtype) 

# 需要導入模塊: from tensorflow.python.ops import gen_math_ops [as 別名]
# 或者: from tensorflow.python.ops.gen_math_ops import not_equal [as 別名]
def count_nonzero(input_tensor,
                  axis=None,
                  keep_dims=False,
                  dtype=dtypes.int64,
                  name=None,
                  reduction_indices=None):
  """Computes number of nonzero elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `axis`.
  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
  entry in `axis`. If `keep_dims` is true, the reduced dimensions
  are retained with length 1.

  If `axis` has no entries, all dimensions are reduced, and a
  tensor with a single element is returned.

  **NOTE** Floating point comparison to zero is done by exact floating point
  equality check.  Small values are **not** rounded to zero for purposes of
  the nonzero check.

  For example:

  ```python
  x = tf.constant([[0, 1, 0], [1, 1, 0]])
  tf.count_nonzero(x)  # 3
  tf.count_nonzero(x, 0)  # [1, 2, 0]
  tf.count_nonzero(x, 1)  # [1, 2]
  tf.count_nonzero(x, 1, keep_dims=True)  # [[1], [2]]
  tf.count_nonzero(x, [0, 1])  # 3
  ```

  Args:
    input_tensor: The tensor to reduce. Should be of numeric type, or `bool`.
    axis: The dimensions to reduce. If `None` (the default),
      reduces all dimensions. Must be in the range
      `[-rank(input_tensor), rank(input_tensor))`.
    keep_dims: If true, retains reduced dimensions with length 1.
    dtype: The output dtype; defaults to `tf.int64`.
    name: A name for the operation (optional).
    reduction_indices: The old (deprecated) name for axis.

  Returns:
    The reduced tensor (number of nonzero values).
  """
  with ops.name_scope(name, "count_nonzero", [input_tensor]):
    input_tensor = ops.convert_to_tensor(input_tensor, name="input_tensor")
    zero = input_tensor.dtype.as_numpy_dtype()
    return cast(
        reduce_sum(
            # int64 reduction happens on GPU
            to_int64(gen_math_ops.not_equal(input_tensor, zero)),
            axis=axis,
            keep_dims=keep_dims,
            reduction_indices=reduction_indices),
        dtype=dtype)