本文整理匯總了Python中tensorflow.python.framework.ops.Variable方法的典型用法代碼示例。如果您正苦於以下問題：Python ops.Variable方法的具體用法？Python ops.Variable怎麽用？Python ops.Variable使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.framework.ops的用法示例。
在下文中一共展示了ops.Variable方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _SliceHelperVar
# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Variable [as 別名]
def _SliceHelperVar(var, slice_spec):
"""Creates a slice helper object given a variable.
This allows creating a sub-tensor from part of the current contents
of a variable. See ${tf.Tensor$`Tensor.__getitem__`}
for detailed examples of slicing.
This function in addition also allows assignment to a sliced range.
This is similar to `__setitem__` functionality in Python. However,
the syntax is different so that the user can capture the assignment
operation for grouping or passing to `sess.run()`.
For example,
```prettyprint
import tensorflow as tf
A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(A[:2, :2]) # => [[1,2], [4,5]]
op = A[:2,:2].assign(22. * tf.ones((2, 2)))
print sess.run(op) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
```
Note that assignments currently do not support NumPy broadcasting
semantics.
Args:
var: An `ops.Variable` object.
slice_spec: The arguments to `Tensor.__getitem__`.
Returns:
The appropriate slice of "tensor", based on "slice_spec".
As an operator. The operator also has a `assign()` method
that can be used to generate an assignment operator.
Raises:
ValueError: If a slice range is negative size.
TypeError: If the slice indices aren't int, slice, or Ellipsis.
"""
return _SliceHelper(var._AsTensor(), slice_spec, var) 示例2: _SliceHelperVar
# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Variable [as 別名]
def _SliceHelperVar(var, slice_spec):
"""Creates a slice helper object given a variable.
This allows creating a sub-tensor from part of the current contents
of a variable.
See
[`Tensor.__getitem__`](../../api_docs/python/framework.md#Tensor.__getitem__)
for detailed examples of slicing.
This function in addition also allows assignment to a sliced range.
This is similar to `__setitem__` functionality in Python. However,
the syntax is different so that the user can capture the assignment
operation for grouping or passing to `sess.run()`.
For example,
```prettyprint
import tensorflow as tf
A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(A[:2, :2]) # => [[1,2], [4,5]]
op = A[:2,:2].assign(22. * tf.ones((2, 2)))
print sess.run(op) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
```
Note that assignments currently do not support NumPy broadcasting
semantics.
Args:
var: An `ops.Variable` object.
slice_spec: The arguments to `Tensor.__getitem__`.
Returns:
The appropriate slice of "tensor", based on "slice_spec".
As an operator. The operator also has a `assign()` method
that can be used to generate an assignment operator.
Raises:
ValueError: If a slice range is negative size.
TypeError: If the slice indices aren't int, slice, or Ellipsis.
"""
return _SliceHelper(var._AsTensor(), slice_spec, var) 示例3: _SliceHelperVar
# 需要導入模塊: from tensorflow.python.framework import ops [as 別名]
# 或者: from tensorflow.python.framework.ops import Variable [as 別名]
def _SliceHelperVar(var, slice_spec):
"""Creates a slice helper object given a variable.
This allows creating a sub-tensor from part of the current contents
of a variable. See ${tf.Tensor$`Tensor.__getitem__`}
for detailed examples of slicing.
This function in addition also allows assignment to a sliced range.
This is similar to `__setitem__` functionality in Python. However,
the syntax is different so that the user can capture the assignment
operation for grouping or passing to `sess.run()`.
For example,
```python
import tensorflow as tf
A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(sess.run(A[:2, :2])) # => [[1,2], [4,5]]
op = A[:2,:2].assign(22. * tf.ones((2, 2)))
print(sess.run(op)) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
```
Note that assignments currently do not support NumPy broadcasting
semantics.
Args:
var: An `ops.Variable` object.
slice_spec: The arguments to `Tensor.__getitem__`.
Returns:
The appropriate slice of "tensor", based on "slice_spec".
As an operator. The operator also has a `assign()` method
that can be used to generate an assignment operator.
Raises:
ValueError: If a slice range is negative size.
TypeError: If the slice indices aren't int, slice, or Ellipsis.
"""
return _SliceHelper(var._AsTensor(), slice_spec, var) 開發者ID:PacktPublishing，項目名稱:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代碼行數:45，代碼來源:array_ops.py