CODEWITHSUNDEEP
pythontensorflow
Vedanshu
Vedanshu

Reputation: 2296

Tensor must be from the same graph as Tensor

I was doing some regression and then I tried to add L2 regularization into it. But it showing me following error:

ValueError: Tensor("Placeholder:0", dtype=float32) must be from the same graph as Tensor("w_hidden:0", shape=(10, 36), dtype=float32_ref).

The code looks like as follows:

def tensorGraph5Fold(initState = 'NSW'):
    weights_obj, biases_obj = loadKernelBias5Fold(initState)

    weights = [tf.convert_to_tensor(w, dtype=tf.float32) for w in weights_obj]
    biases = [tf.convert_to_tensor(b, dtype=tf.float32) for b in biases_obj]

    #RNN designning
    tf.reset_default_graph()

    inputs = x_size #input vector size
    output = y_size #output vector size
    learning_rate = 0.01

    x = tf.placeholder(tf.float32, [inputs, None])
    y = tf.placeholder(tf.float32, [output, None])

    #L2 regulizer
    regularizer = tf.contrib.layers.l2_regularizer(scale=0.2)
    weights = {
        'hidden': tf.get_variable("w_hidden", initializer = weights[0], regularizer=regularizer),
        'output': tf.get_variable("w_output", initializer = weights[1], regularizer=regularizer)
    }

    biases = {
        'hidden': tf.get_variable("b_hidden", initializer = biases[0]),
        'output': tf.get_variable("b_output", initializer = biases[1])
    }

    hidden_layer = tf.add(tf.matmul(weights['hidden'], x), biases['hidden'])
    hidden_layer = tf.nn.relu(hidden_layer)

    output_layer = tf.matmul(weights['output'], hidden_layer) + biases['output']

    loss = tf.reduce_mean(tf.square(output_layer - y))    #define the cost function which evaluates the quality of our model
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)          #gradient descent method
    training_op = optimizer.minimize(loss)          #train the result of the application of the cost_function                                 

    #L2 regulizer
    reg_variables = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
    reg_term = tf.contrib.layers.apply_regularization(regularizer, reg_variables)
    loss += reg_term

    init = tf.global_variables_initializer()           #initialize all the variables
    epochs = 2000     #number of iterations or training cycles, includes both the FeedFoward and Backpropogation

    pred = {'NSW': [], 'QLD': [], 'SA': [], 'TAS': [], 'VIC': []}
    y_pred = {1: pred, 2: pred, 3: pred, 4: pred, 5: pred}

    print("Training the ANN...")
    for st in state.values():
        for fold in np.arange(1,6):
            print("State: ", st, end='\n')
            print("Fold : ", fold)

            with tf.Session() as sess:
                init.run()
                for ep in range(epochs):
                    sess.run(training_op, feed_dict={x: x_batches_train_fold[fold][st], y: y_batches_train_fold[fold][st]})

            print("\n")

The error shows that I'm using two graphs but I don't know where.

Upvotes: 2

Views: 4602

Answers (1)

Stewart_R
Stewart_R

Reputation: 14485

The error message explains that your placeholder for x is not in the same graph as the w_hidden tensor - this means that we cannot complete an operation using these two tensors (presumably this is thrown when running tf.matmul(weights['hidden'], x))

The reason this has come about is that you have used tf.reset_default_graph() after you created the reference to weights but before you created the placeholder x.

In order to fix this, you could move the tf.reset_default_graph() call before all your operations (or remove it altogether)

Upvotes: 1

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