Tensorflow: не может интерпретировать ключ feed_dict как тензор

Я пытаюсь создать модель нейронной сети с одним скрытым слоем (1024 узла). Скрытый слой - не что иное, как единица relu. Я также обрабатываю входные данные партиями 128.

Входы представляют собой изображения размером 28 * 28. В следующем коде я получаю ошибку в строке

_, c = sess.run([optimizer, loss], feed_dict={x: batch_x, y: batch_y})
Error: TypeError: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder_64:0", shape=(128, 784), dtype=float32) is not an element of this graph.

Вот код, который я написал

#Initialize

batch_size = 128

layer1_input = 28 * 28
hidden_layer1 = 1024
num_labels = 10
num_steps = 3001

#Create neural network model
def create_model(inp, w, b):
    layer1 = tf.add(tf.matmul(inp, w['w1']), b['b1'])
    layer1 = tf.nn.relu(layer1)
    layer2 = tf.matmul(layer1, w['w2']) + b['b2']
    return layer2

#Initialize variables
x = tf.placeholder(tf.float32, shape=(batch_size, layer1_input))
y = tf.placeholder(tf.float32, shape=(batch_size, num_labels))

w = {
'w1': tf.Variable(tf.random_normal([layer1_input, hidden_layer1])),
'w2': tf.Variable(tf.random_normal([hidden_layer1, num_labels]))
}
b = {
'b1': tf.Variable(tf.zeros([hidden_layer1])),
'b2': tf.Variable(tf.zeros([num_labels]))
}

init = tf.initialize_all_variables()
train_prediction = tf.nn.softmax(model)

tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)

model = create_model(x, w, b)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(model, y))    
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

#Process
with tf.Session(graph=graph1) as sess:
    tf.initialize_all_variables().run()
    total_batch = int(train_dataset.shape[0] / batch_size)

    for epoch in range(num_steps):    
        loss = 0
        for i in range(total_batch):
            batch_x, batch_y = train_dataset[epoch * batch_size:(epoch+1) * batch_size, :], train_labels[epoch * batch_size:(epoch+1) * batch_size,:]

            _, c = sess.run([optimizer, loss], feed_dict={x: batch_x, y: batch_y})
            loss = loss + c
        loss = loss / total_batch
        if epoch % 500 == 0:
            print ("Epoch :", epoch, ". cost = {:.9f}".format(avg_cost))
            print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
            valid_prediction = tf.run(tf_valid_dataset, {x: tf_valid_dataset})
            print("Validation accuracy: %.1f%%" % accuracy(valid_prediction.eval(), valid_labels))
    test_prediction = tf.run(tf_test_dataset,  {x: tf_test_dataset})
    print("TEST accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))

Ответы

# define a graph
graph1 = tf.Graph()
with graph1.as_default():
    # placeholder
    x = tf.placeholder(...)
    y = tf.placeholder(...)
    # create model
    model = create(x, w, b)

with tf.Session(graph=graph1) as sess:
# initialize all the variables
sess.run(init)
# then feed_dict
# ......

from keras import backend as K

K.clear_session()

from keras import backend as K

#Before prediction
K.clear_session()

#After prediction
K.clear_session()

python manage.py runserver --nothreading  

# Declare this as global:

global graph

graph = tf.get_default_graph()

# Then just before you call in your model, use this

with graph.as_default():

# call you models here

with tf.Session() as sess:
    sess.run(logits, feed_dict=feed_dict) 

sess.run(logits, feed_dict=feed_dict)