using saved model to make prections in tensorflow - tensorflow

Question

i have this code to train a restore a model in tensorflow . but how can I make predictions.
def train_neural_network(x):
prediction=neural_network_model(x)
cost=tf.nn.softmax_cross_entropy_with_logits_v2(logits = prediction, labels = y)
optimizer=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
saver = tf.train.Saver()
with tf.Session() as sess:
#sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())
for epoch in range(hm_epochs):
epoch_loss = 0
i = 0
#while i < len(train_x):
t = len(train_x)
f = t%batch_size
while i < (t-f):
start = i
end = i+batch_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
epoch_loss += c
#epoch_loss = epoch_loss + c
i+=batch_size
#i = i + batch_size
print('Epoch =', epoch+1, '/',hm_epochs,'loss:',epoch_loss)
save_path = saver.save(sess, "sesionestensorflow/model1.ckpt")
print("Model saved in path: %s" % save_path)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))
i see this answer but i can`t make the prediction.
Using saved model for prediction in tensorflow

Answer 1

You just have to create an empy graph, define the network, load the saved weights and thus run inference.
prediction = tf.argmax(neural_network_model(x), 1)
saver = tf.train.Saver()
with tf.Session() as sess:
# load the trained weights into the model
saver.restore(sess, "sesionestensorflow/model1.ckpt")
# just use the model
out = sess.run(prediction, feed_dict={x: <your input> })