Estoy buscando una manera de cambiar rápidamente un gráfico dentro de una sesión interactiva en Jupyter para probar diferentes estructuras. Inicialmente, quería eliminar simplemente las variables existentes y volver a crearlas con un inicializador diferente. Esto no parece ser posible [1].

Luego encontré [2] y ahora estoy intentando simplemente descartar y volver a crear el gráfico predeterminado. Pero esto no parece funcionar. Esto es lo que hago:

a. Comience una sesión

import tensorflow as tf
import math

sess = tf.InteractiveSession()

si. Crear una variable en el gráfico predeterminado

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. Usa la variable

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

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

re. Restablecer el gráfico

tf.reset_default_graph()

mi. Recrea la 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)

Sin embargo, obtengo una excepción (ver más abajo). Entonces mi pregunta es: ¿es posible restablecer / eliminar el gráfico y volver a crearlo como antes? ¿Si es así, cómo?

Apreciamos cualquier puntero.

TIA

RefsCambiar inicializador de variable en TensorflowEliminar nodos del gráfico o restablecer todo el gráfico predeterminadoExcepción

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).`