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Python gen_array_ops._squeeze方法代码示例

本文整理汇总了Python中tensorflow.python.ops.gen_array_ops._squeeze方法的典型用法代码示例。如果您正苦于以下问题:Python gen_array_ops._squeeze方法的具体用法?Python gen_array_ops._squeeze怎么用?Python gen_array_ops._squeeze使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow.python.ops.gen_array_ops的用法示例。


在下文中一共展示了gen_array_ops._squeeze方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: squeeze

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _squeeze [as 别名]
def squeeze(input, squeeze_dims=None, name=None):
  # pylint: disable=redefined-builtin
  """Removes dimensions of size 1 from the shape of a tensor.

  Given a tensor `input`, this operation returns a tensor of the same type with
  all dimensions of size 1 removed. If you don't want to remove all size 1
  dimensions, you can remove specific size 1 dimensions by specifying
  `squeeze_dims`.

  For example:

  ```prettyprint
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  shape(squeeze(t)) ==> [2, 3]
            ```

  Or, to remove specific size 1 dimensions:

  ```prettyprint
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
  ```

  Args:
    input: A `Tensor`. The `input` to squeeze.
    squeeze_dims: An optional list of `ints`. Defaults to `[]`.
      If specified, only squeezes the dimensions listed. The dimension
      index starts at 0. It is an error to squeeze a dimension that is not 1.
    name: A name for the operation (optional).

  Returns:
    A `Tensor`. Has the same type as `input`.
    Contains the same data as `input`, but has one or more dimensions of
    size 1 removed.
  """
  if np.isscalar(squeeze_dims):
    squeeze_dims = [squeeze_dims]
  return gen_array_ops._squeeze(input, squeeze_dims, name) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:40,代码来源:array_ops.py

示例2: squeeze

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _squeeze [as 别名]
def squeeze(input, axis=None, name=None, squeeze_dims=None):
  # pylint: disable=redefined-builtin
  """Removes dimensions of size 1 from the shape of a tensor.

  Given a tensor `input`, this operation returns a tensor of the same type with
  all dimensions of size 1 removed. If you don't want to remove all size 1
  dimensions, you can remove specific size 1 dimensions by specifying
  `axis`.

  For example:

  ```prettyprint
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  shape(squeeze(t)) ==> [2, 3]
  ```

  Or, to remove specific size 1 dimensions:

  ```prettyprint
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
  ```

  Args:
    input: A `Tensor`. The `input` to squeeze.
    axis: An optional list of `ints`. Defaults to `[]`.
      If specified, only squeezes the dimensions listed. The dimension
      index starts at 0. It is an error to squeeze a dimension that is not 1.
    name: A name for the operation (optional).
    squeeze_dims: Deprecated keyword argument that is now axis.

  Returns:
    A `Tensor`. Has the same type as `input`.
    Contains the same data as `input`, but has one or more dimensions of
    size 1 removed.

  Raises:
    ValueError: When both `squeeze_dims` and `axis` are specified.
  """
  if squeeze_dims is not None:
    if axis is not None:
      raise ValueError("Cannot specify both 'squeeze_dims' and 'axis'")
    axis = squeeze_dims
  if np.isscalar(axis):
    axis = [axis]
  return gen_array_ops._squeeze(input, axis, name) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:48,代码来源:array_ops.py

示例3: squeeze

# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _squeeze [as 别名]
def squeeze(input, axis=None, name=None, squeeze_dims=None):
  # pylint: disable=redefined-builtin
  """Removes dimensions of size 1 from the shape of a tensor.

  Given a tensor `input`, this operation returns a tensor of the same type with
  all dimensions of size 1 removed. If you don't want to remove all size 1
  dimensions, you can remove specific size 1 dimensions by specifying
  `axis`.

  For example:

  ```python
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  tf.shape(tf.squeeze(t))  # [2, 3]
  ```

  Or, to remove specific size 1 dimensions:

  ```python
  # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
  tf.shape(tf.squeeze(t, [2, 4]))  # [1, 2, 3, 1]
  ```

  Args:
    input: A `Tensor`. The `input` to squeeze.
    axis: An optional list of `ints`. Defaults to `[]`.
      If specified, only squeezes the dimensions listed. The dimension
      index starts at 0. It is an error to squeeze a dimension that is not 1.
      Must be in the range `[-rank(input), rank(input))`.
    name: A name for the operation (optional).
    squeeze_dims: Deprecated keyword argument that is now axis.

  Returns:
    A `Tensor`. Has the same type as `input`.
    Contains the same data as `input`, but has one or more dimensions of
    size 1 removed.

  Raises:
    ValueError: When both `squeeze_dims` and `axis` are specified.
  """
  if squeeze_dims is not None:
    if axis is not None:
      raise ValueError("Cannot specify both 'squeeze_dims' and 'axis'")
    axis = squeeze_dims
  if np.isscalar(axis):
    axis = [axis]
  return gen_array_ops._squeeze(input, axis, name) 
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:49,代码来源:array_ops.py


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