ValueError: Tensor("X:0", shape=(12288, ?), dtype=float32) must be from the same graph as Tensor("W1:0", shape=(25, 12288), dtype=float32_ref)

Question

I used tensorflow to implement a forward-propagating demo. The same code block can be run normally before being packaged into a method, but an error will occur after encapsulation.

def create_placeholders(n_x, n_y):

    X = tf.placeholder(tf.float32, name='X', shape=[n_x, None])
    Y = tf.placeholder(tf.float32, name='Y', shape=[n_y, None])

    return X, Y

def initialize_parameters():

    tf.set_random_seed(1)

    W1 = tf.get_variable(\
        'W1', [25, 12288], initializer=tf.contrib.layers.xavier_initializer(seed=1))

    b1 = tf.get_variable('b1', [25, 1], initializer=tf.zeros_initializer())

    W2 = tf.get_variable(\
        'W2', [12, 25], initializer=tf.contrib.layers.xavier_initializer(seed=1))

    b2 = tf.get_variable('b2', [12, 1], initializer=tf.zeros_initializer())

    W3 = tf.get_variable(\
        'W3', [6, 12], initializer=tf.contrib.layers.xavier_initializer(seed=1))

    b3 = tf.get_variable('b3', [6, 1], initializer=tf.zeros_initializer())

    parameters = {
        "W1":W1,
        "b1":b1,
        "W2":W2,
        "b2":b2,
        "W3":W3,
        "b3":b3
    }

    return parameters

def forward_propagation(X, paramters):

    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']
    W3 = parameters['W3']
    b3 = parameters['b3']

    Z1 = tf.add(tf.matmul(W1, X), b1)
    A1 = tf.nn.relu(Z1)

    Z2 = tf.add(tf.matmul(W2, A1), b2)
    A2 = tf.nn.relu(Z2)

    Z3= tf.add(tf.matmul(W3, A2), b3)

    return Z3

running the code below will work fine:

tf.reset_default_graph()
with tf.Session() as sess:
    X, Y = create_placeholders(12288, 6)
    parameters = initialize_parameters()
    Z3 = forward_propagation(X, parameters)
    print("Z3 = " + str(Z3))

get the following output

Z3 = Tensor("Add_2:0", shape=(6, ?), dtype=float32)

but when i run the following code, i got an error.

def model():
    tf.reset_default_graph()
    with tf.Session() as sess:
        X, Y = create_placeholders(12288, 6)
        parameters = initialize_parameters()
        Z3 = forward_propagation(X, parameters)
        print("Z3 = " + str(Z3))

model()

the error is:

ValueError                                Traceback (most recent call last)
 in 
      7         print("Z3 = " + str(Z3))
      8 
----> 9 model()

 in model()
      4         X, Y = create_placeholders(12288, 6)
      5         parameters = initialize_parameters()
----> 6         Z3 = forward_propagation(X, parameters)
      7         print("Z3 = " + str(Z3))
      8 

 in forward_propagation(X, paramters)
     19     b3 = parameters['b3']
     20 
---> 21     Z1 = tf.add(tf.matmul(W1, X), b1)
     22     A1 = tf.nn.relu(Z1)
     23 

G:\Anaconda3.7\lib\site-packages	ensorflow\python\ops\math_ops.py in matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)
   2385       are both set to True.
   2386   """
-> 2387   with ops.name_scope(name, "MatMul", [a, b]) as name:
   2388     if transpose_a and adjoint_a:
   2389       raise ValueError("Only one of transpose_a and adjoint_a can be True.")

G:\Anaconda3.7\lib\site-packages	ensorflow\python\framework\ops.py in __enter__(self)
   6081       if self._values is None:
   6082         self._values = []
-> 6083       g = _get_graph_from_inputs(self._values)
   6084       self._g_manager = g.as_default()
   6085       self._g_manager.__enter__()

G:\Anaconda3.7\lib\site-packages	ensorflow\python\framework\ops.py in _get_graph_from_inputs(op_input_list, graph)
   5711         graph = graph_element.graph
   5712       elif original_graph_element is not None:
-> 5713         _assert_same_graph(original_graph_element, graph_element)
   5714       elif graph_element.graph is not graph:
   5715         raise ValueError("%s is not from the passed-in graph." % graph_element)

G:\Anaconda3.7\lib\site-packages	ensorflow\python\framework\ops.py in _assert_same_graph(original_item, item)
   5647   if original_item.graph is not item.graph:
   5648     raise ValueError("%s must be from the same graph as %s." % (item,
-> 5649                                                                 original_item))
   5650 
   5651 

ValueError: Tensor("X:0", shape=(12288, ?), dtype=float32) must be from the same graph as Tensor("W1:0", shape=(25, 12288), dtype=float32_ref).

it troubled me for a long time, i will highly appreciate u!

ValueError: Tensor("X:0", shape=(12288, ?), dtype=float32) must be from the same graph as Tensor("W1:0", shape=(25, 12288), dtype=float32_ref)

0 Answers