重置默认图表不会删除变量

Question

我正在寻找一种方法来快速更改Jupyter中的交互式会话中的图形,以便测试不同的结构.最初我想简单地删除现有变量并使用不同的初始化程序重新创建它们.这似乎不可能[1].

然后我找到[2],现在我试图简单地丢弃并重新创建默认图.但这似乎不起作用.这就是我做的:

一个.开始一个会话

import tensorflow as tf
import math

sess = tf.InteractiveSession()

湾 在默认图表中创建变量

IMAGE_PIXELS = 32 * 32
HIDDEN1 = 200

BATCH_SIZE = 100
NUM_POINTS = 30

images_placeholder = tf.placeholder(tf.float32, shape=(BATCH_SIZE, IMAGE_PIXELS))
points_placeholder = tf.placeholder(tf.float32,   shape=(BATCH_SIZE, NUM_POINTS))


# Hidden 1
with tf.name_scope('hidden1'):
  weights_init = tf.truncated_normal([IMAGE_PIXELS, HIDDEN1], stddev=1.0 / math.sqrt(float(IMAGE_PIXELS)))
  weights      = tf.Variable(weights_init, name='weights')
  biases_init  = tf.zeros([HIDDEN1])
  biases       = tf.Variable(biases_init, name='biases')
  hidden1      = tf.nn.relu(tf.matmul(images_placeholder, weights) + biases)

C.使用变量

# Add the variable initializer Op.
init = tf.initialize_all_variables()

# Run the Op to initialize the variables.
sess.run(init) 

d.重置图表

tf.reset_default_graph()

即 重新创建变量

with tf.name_scope('hidden1'):
  weights      = tf.get_variable(name='weights', shape=[IMAGE_PIXELS, HIDDEN1], 
                                 initializer=tf.contrib.layers.xavier_initializer())
  biases_init  = tf.zeros([HIDDEN1])
  biases       = tf.Variable(biases_init, name='biases')
  hidden1      = tf.nn.relu(tf.matmul(images_placeholder, weights) + biases)

但是,我得到一个例外(见下文).所以我的问题是:是否有可能重置/删除图表并像以前一样重新创建它？如果是这样,怎么样？

感谢任何指针.

TIA,

参考文献

1. 在Tensorflow中更改Variable的初始值设定项
2. 从图表中删除节点或重置整个默认图表

例外

ValueError                                Traceback (most recent call last)
<ipython-input-5-e98a82c45473> in <module>()
      5   biases_init  = tf.zeros([HIDDEN1])
      6   biases       = tf.Variable(biases_init, name='biases')
----> 7   hidden1      = tf.nn.relu(tf.matmul(images_placeholder, weights) + biases)
  8 

/home/hmf/my_py3/lib/python3.4/site-packages/tensorflow/python/ops/math_ops.py in matmul(a, b, transpose_a, transpose_b, a_is_sparse, b_is_sparse, name)
   1323     A `Tensor` of the same type as `a`.
   1324   """
-> 1325   with ops.op_scope([a, b], name, "MatMul") as name:
   1326     a = ops.convert_to_tensor(a, name="a")
   1327     b = ops.convert_to_tensor(b, name="b")

/usr/lib/python3.4/contextlib.py in __enter__(self)
     57     def __enter__(self):
     58         try:
 ---> 59             return next(self.gen)
     60         except StopIteration:
     61             raise RuntimeError("generator didn't yield") from None

/home/hmf/my_py3/lib/python3.4/site-packages/tensorflow/python/framework/ops.py in op_scope(values, name, default_name)
   4014     ValueError: if neither `name` nor `default_name` is provided.
   4015   """
-> 4016   g = _get_graph_from_inputs(values)
   4017   n = default_name if name is None else name
   4018   if n is None:

/home/hmf/my_py3/lib/python3.4/site-packages/tensorflow/python/framework/ops.py in _get_graph_from_inputs(op_input_list, graph)
   3812         graph = graph_element.graph
   3813       elif original_graph_element is not None:
-> 3814         _assert_same_graph(original_graph_element, graph_element)
   3815       elif graph_element.graph is not graph:
   3816         raise ValueError(

/home/hmf/my_py3/lib/python3.4/site-packages/tensorflow/python/framework/ops.py in _assert_same_graph(original_item, item)
   3757   if original_item.graph is not item.graph:
   3758     raise ValueError(
-> 3759         "%s must be from the same graph as %s." % (item, original_item))
   3760 
   3761 

ValueError: Tensor("weights:0", shape=(1024, 200), dtype=float32_ref) must be from the same graph as Tensor("Placeholder:0", shape=(100, 1024), dtype=float32).`

Answer 1

重置默认图表时,不会删除以前创建的张量.调用时tf.reset_default_graph(),会创建一个新图并将其设置为默认值.

这是一个例子来说明:

x = tf.constant(1)
print tf.get_default_graph() == x.graph  # prints True

tf.reset_default_graph()
print tf.get_default_graph() == x.graph  # prints False

您所犯的错误表明两个张量必须来自同一个图形,这意味着您仍在使用上一个图形中的一些张量和当前默认图形.

简单的解决方法是再次创建两个占位符images_placeholder和points_placeholder