本文整理汇总了Python中tensorflow.python.ops.gen_array_ops._split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._split方法的具体用法？Python gen_array_ops._split怎么用？Python gen_array_ops._split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类tensorflow.python.ops.gen_array_ops的用法示例。
在下文中一共展示了gen_array_ops._split方法的1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: split
# 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 _split [as 别名]
def split(split_dim, num_split, value, name="split"):
"""Splits a tensor into `num_split` tensors along one dimension.
Splits `value` along dimension `split_dim` into `num_split` smaller tensors.
Requires that `num_split` evenly divide `value.shape[split_dim]`.
For example:
```python
# 'value' is a tensor with shape [5, 30]
# Split 'value' into 3 tensors along dimension 1
split0, split1, split2 = tf.split(1, 3, value)
tf.shape(split0) ==> [5, 10]
```
Note: If you are splitting along an axis by the length of that axis, consider
using unpack, e.g.
```python
num_items = t.get_shape()[axis].value
[tf.squeeze(s, [axis]) for s in tf.split(axis, num_items, t)]
```
can be rewritten as
```python
tf.unpack(t, axis=axis)
```
Args:
split_dim: A 0-D `int32` `Tensor`. The dimension along which to split.
Must be in the range `[0, rank(value))`.
num_split: A Python integer. The number of ways to split.
value: The `Tensor` to split.
name: A name for the operation (optional).
Returns:
`num_split` `Tensor` objects resulting from splitting `value`.
"""
return gen_array_ops._split(split_dim=split_dim,
num_split=num_split,
value=value,
name=name)