SuperResolution pomocí MNIST nefunguje správně

Jsem nový Hluboké Učení a udělal jsem model, který předstírá, luxusní a 14x14 obraz na 28x28. Za to, že jsem trénoval newtork pomocí MNIST úložiště jako první pokus o vyřešení tohoto problému.

Pro výrobu modelu struktury sledoval jsem tento dokument: https://arxiv.org/pdf/1608.00367.pdf

import numpy as np
from tensorflow.keras import optimizers
from tensorflow.keras import layers
from tensorflow.keras import models
import os
import cv2
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras import initializers
import matplotlib.pyplot as plt
import pickle
import time

# Tensorboard Stuff:
NAME = "MNIST_FSRCNN_test -{}".format(
    int(time.time()))  # This is the name of our try, change it if it's a
# new try.
tensorboard = TensorBoard(log_dir='logs/{}'.format(NAME))  # defining tensorboard directory.

# Path of the data
train_small_path = "D:/MNIST/training/small_train"
train_normal_path = "D:/MNIST/training/normal_train"

test_small_path = "D:/MNIST/testing/small_test"
test_normal_path = "D:/MNIST/testing/normal_test"

# Image reading from the directories. MNIST is in grayscale so we read it that way.
train_small_array = []
for img in os.listdir(train_small_path):
    try:
        train_small_array.append(np.array(cv2.imread(os.path.join(train_small_path, img), cv2.IMREAD_GRAYSCALE)))
    except Exception as e:
        print("problem with image reading in train small")
        pass
train_normal_array = []
for img in os.listdir(train_normal_path):
    try:
        train_normal_array.append(np.array(cv2.imread(os.path.join(train_normal_path, img), cv2.IMREAD_GRAYSCALE)))
    except Exception as e:
        print("problem with image reading in train normal")
        pass
test_small_array = []
for img in os.listdir(test_small_path):
    try:
        test_small_array.append(cv2.imread(os.path.join(test_small_path, img), cv2.IMREAD_GRAYSCALE))
    except Exception as e:
        print("problem with image reading in test small")
        pass

test_normal_array = []
for img in os.listdir(test_normal_path):
    try:
        test_normal_array.append(cv2.imread(os.path.join(test_normal_path, img), cv2.IMREAD_GRAYSCALE))
    except Exception as e:
        print("problem with image reading in test normal")
        pass

train_small_array = np.array(train_small_array).reshape((60000, 14, 14, 1))
train_normal_array = np.array(train_normal_array).reshape((60000, 28, 28, 1))

test_small_array = np.array(test_small_array).reshape((10000, 14, 14, 1))
test_normal_array = np.array(test_normal_array).reshape((10000, 28, 28, 1))




training_data = []
training_data.append([train_small_array, train_normal_array])

testing_data = []
testing_data.append([test_small_array, test_normal_array])


# ---SAVE DATA--
# We are saving our data
pickle_out = open("X.pickle", "wb")
pickle.dump(y, pickle_out)
pickle_out.close()
# for reading it:
pickle_in = open("X.pickle", "rb")
X = pickle.load(pickle_in)
# -----------


# MAKING THE NETWORK
d = 56
s = 12
m = 4
upscaling = 2

model = models.Sequential()
bias = True

# Feature extraction:
model.add(layers.Conv2D(filters=d,
                        kernel_size=5,
                        padding='SAME',
                        data_format="channels_last",
                        use_bias=bias,
                        kernel_initializer=initializers.he_normal(),
                        input_shape=(None, None, 1),
                        activation='relu'))

# Shrinking:
model.add(layers.Conv2D(filters=s,
                        kernel_size=1,
                        padding='same',
                        use_bias=bias,
                        kernel_initializer=initializers.he_normal(),
                        activation='relu'))

for i in range(m):
    model.add(layers.Conv2D(filters=s,
                            kernel_size=3,
                            padding="same",
                            use_bias=bias,
                            kernel_initializer=initializers.he_normal(),
                            activation='relu'),
              )

# Expanding
model.add(layers.Conv2D(filters=d,
                        kernel_size=1,
                        padding='same',
                        use_bias=bias,
                        kernel_initializer=initializers.he_normal,
                        activation='relu'))

# Deconvolution
model.add(layers.Conv2DTranspose(filters=1,
                                 kernel_size=9,
                                 strides=(upscaling, upscaling),
                                 padding='same',
                                 use_bias=bias,
                                 kernel_initializer=initializers.random_normal(mean=0.0, stddev=0.001),
                                 activation='relu'))

# MODEL COMPILATION
model.compile(loss='mse',
              optimizer=optimizers.RMSprop(learning_rate=1e-3),  
              metrics=['acc'])




model.fit(x=train_small_array, y=train_normal_array,
          epochs=10,
          batch_size=1500,
          validation_split=0.2,
          callbacks=[tensorboard])


print(model.evaluate(test_small_array, test_normal_array))



# -DEMO-----------------------------------------------------------------
from PIL import Image
import PIL.ImageOps
import os

dir = 'C:/Users/marcc/OneDrive/Escritorio'
os.chdir(dir)

myImage = Image.open("ImageTest.PNG").convert('L')  # convert to black and white
myImage = myImage.resize((14, 14))


myImage_array = np.array(myImage)

plt.imshow(myImage_array, cmap=plt.cm.binary)
plt.show()

myImage_array = myImage_array.astype('float32') / 255
myImage_array = myImage_array.reshape(1, 14, 14, 1)



newImage = model.predict(myImage_array)
newImage = newImage.reshape(28,28)
plt.imshow(newImage, cmap=plt.cm.binary)
plt.show()

Problém, který mám je, že s 10 epoch zdá se, že práce, převádí tento obraz:14x14 MNIST

do tohoto: 10 epoch 28x28

Ale když jsem se 20 epoch dostanu20 epoch 28x28

Chci vědět, co se stane. Nejdřív jsem si myslel, že možná byl model overfitting, ale když jsem zkontrolovat ztráta funkce školení a ověření nezdá se, že overfit: školení a ověření ztráty