Resetting default graph does not remove variables

Question

I am looking for a way to quickly change a graph within an interactive session in Jupyter in order to test different structures. Initially I wanted to simple delete existing variables and recreate them with a different initializer. This does not seem to be possible [1].

I then found [2] and am now attempting to simply discard and recreate the default graph. But this does not seem to work. This is what I do:

a. Start a session

import tensorflow as tf
import math

sess = tf.InteractiveSession()

b. Create a variable in the default graph

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. Use the variable

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

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

d. Reset the graph

tf.reset_default_graph()

e. Recreate the variable

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)

However, I get an exception (see below). So my question is: is it possible to reset/remove the graph and recreate it as before? If so, how?

Appreciate any pointers.

TIA,

Refs

Exception

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).`

You reset the default graph but do not recreate `images_placeholder` and `points_placeholder` — Olivier Moindrot, Sep 06 '16 at 15:43
@OlivierMoindrot you are correct. Thank you. Care to to make your comment an answer so that I can mark it as an answer. — user2051561, Sep 06 '16 at 15:58

score 11 · Accepted Answer · answered Sep 06 '16 at 16:03

When you reset the default graph, you do not remove the previous Tensors created. When calling tf.reset_default_graph(), a new graph is created and set to default.

Here is an example to illustrate:

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

The error you had indicates that two tensors must be from the same graph, which means you are still using some tensors from the previous graph AND from the current default graph.

The easy fix is to create again the two placeholders images_placeholder and points_placeholder

Thanks for the explanation. Recreating the place holders fixes the problem. — user2051561, Sep 06 '16 at 16:34

Resetting default graph does not remove variables

Refs

Exception

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