本文整理汇总了Python中tensorflow.python.ops.gen_array_ops._placeholder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._placeholder方法的具体用法？Python gen_array_ops._placeholder怎么用？Python gen_array_ops._placeholder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._placeholder方法的2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: placeholder
# 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 _placeholder [as 别名]
def placeholder(dtype, shape=None, name=None):
"""Inserts a placeholder for a tensor that will be always fed.
**Important**: This tensor will produce an error if evaluated. Its value must
be fed using the `feed_dict` optional argument to `Session.run()`,
`Tensor.eval()`, or `Operation.run()`.
For example:
```python
x = tf.placeholder(tf.float32, shape=(1024, 1024))
y = tf.matmul(x, x)
with tf.Session() as sess:
print(sess.run(y)) # ERROR: will fail because x was not fed.
rand_array = np.random.rand(1024, 1024)
print(sess.run(y, feed_dict={x: rand_array})) # Will succeed.
```
Args:
dtype: The type of elements in the tensor to be fed.
shape: The shape of the tensor to be fed (optional). If the shape is not
specified, you can feed a tensor of any shape.
name: A name for the operation (optional).
Returns:
A `Tensor` that may be used as a handle for feeding a value, but not
evaluated directly.
"""
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
# pylint: disable=redefined-outer-name 示例2: placeholder
# 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 _placeholder [as 别名]
def placeholder(dtype, shape=None, name=None):
"""Inserts a placeholder for a tensor that will be always fed.
**Important**: This tensor will produce an error if evaluated. Its value must
be fed using the `feed_dict` optional argument to `Session.run()`,
`Tensor.eval()`, or `Operation.run()`.
For example:
```python
x = tf.placeholder(tf.float32, shape=(1024, 1024))
y = tf.matmul(x, x)
with tf.Session() as sess:
print(sess.run(y)) # ERROR: will fail because x was not fed.
rand_array = np.random.rand(1024, 1024)
print(sess.run(y, feed_dict={x: rand_array})) # Will succeed.
```
Args:
dtype: The type of elements in the tensor to be fed.
shape: The shape of the tensor to be fed (optional). If the shape is not
specified, you can feed a tensor of any shape.
name: A name for the operation (optional).
Returns:
A `Tensor` that may be used as a handle for feeding a value, but not
evaluated directly.
"""
shape = tensor_shape.as_shape(shape)
if shape.is_fully_defined():
dim_list = shape.as_list()
else:
dim_list = []
ret = gen_array_ops._placeholder(
dtype=dtype,
shape=dim_list,
name=name)
ret.set_shape(shape)
return ret
# pylint: disable=redefined-outer-name