Why my neural network keep predict the same thing all over again?

edit

I want to build an ANN using opencv ANN MLP Classifier to predict MNIST. I use 350 epoch, but my network keep predict '9' even though I test it using the training image. Below is my code :

import cv2
import numpy as np
import os
from os import listdir
import time

start = time.time()

#create ANN
net = cv2.ml.ANN_MLP_create()
net.setTrainMethod(cv2.ml.ANN_MLP_BACKPROP, 0.0001)
net.setActivationFunction(cv2.ml.ANN_MLP_SIGMOID_SYM)
net.setLayerSizes(np.array([784, 100, 10]))
net.setTermCriteria(( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 100, 0.00001 ))

#output
class_0 = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
class_1 = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
class_2 = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
class_3 = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
class_4 = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
class_5 = [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
class_6 = [0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
class_7 = [0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
class_8 = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
class_9 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
array_class = [class_0, class_1, class_2, class_3, class_4, class_5, class_6, class_7, class_8, class_9]

def image(sample, classification):
    return (np.array([sample], dtype=np.float32), np.array([classification], dtype=np.float32))

#input
print("Processing the images..")
images = []
os.chdir("dataset/mnist_png/training")
for digit in range(0, 10):
    os.chdir(str(digit))
    path = os.getcwd()
    for file in listdir(path):
        img = cv2.imread(file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#        img_blurred = cv2.GaussianBlur(img, (7,7), 0)
        ret, img_th = cv2.threshold(img, 0, 1, cv2.THRESH_BINARY_INV) 
#        img_eroded = cv2.erode(img, np.ones((2,2), np.uint8), iterations = 2)
        if(img_th.shape[0] != 28 or img_th.shape[1] != 28):
            img_th = cv2.resize(img_th, (28, 28), interpolation = cv2.INTER_CUBIC)
        images.append(image(img_th.ravel(), array_class[digit]))
    os.chdir("../")
os.chdir("../")

#train
print("Training..")
epochs = 350
for e in range(0, epochs):
    tmp = 0
    for data, cls in images:
        tmp += 1
        print("Training image " + str(tmp) + " / 60000 (Epoch " + str(e+1) + "/" + str(epochs) + ")")
        net.train(data, cv2.ml.ROW_SAMPLE, cls)

#test  
print("Testing..")
result_class = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

#for count testing files
os.chdir("new_testing")
path = os.getcwd()
tmp = 0
for file in listdir(path):
    tmp += 1
    testing_image = cv2.imread(file)
    testing_image = cv2.cvtColor(testing_image, cv2.COLOR_BGR2GRAY)
    ret, testing_image_th = cv2.threshold(testing_image, 0, 1, cv2.THRESH_BINARY) 
    if(testing_image_th.shape[0] != 28 or testing_image_th.shape[1] != 28):
        testing_image_th = cv2.resize(testing_image_th, (28, 28), interpolation = cv2.INTER_CUBIC)
    print("Testing image " + str(tmp) + " / 10000")
    predict_class = int(net.predict(np.array([testing_image_th.flatten()], dtype=np.float32))[0])
    print ("class predicted: " + str(predict_class))
    if(str(predict_class) == os.path.splitext(file)[0].split('_')[0]):
        result_class[predict_class] += 1

a = 0
for x in range(0, 10):
    a += result_class[x]

print ("Accuracy: " + str(a/tmp))

end = time.time()
print("Time to process: " + str(end - start))

Anything wrong with that?