MNIST kullanarak süper çözünürlük düzgün çalışmıyor

Derin Öğrenmede yeniyim ve 14x14 görüntüyü 28x28'e yükseltirmiş gibi davranan bir model yaptım. Bunun için, bu sorunu çözmek için ilk girişim olarak MNIST deposunu kullanarak newtork'u eğittim.

Model yapısını yapmak için bu makaleyi takip ettim: 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()

Sahip olduğum sorun şu ki, 10 çağda işe yarıyor gibi görünüyor, bu görüntüyü dönüştürüyor: 14x14 MNIST

bunun içine: 10 dönem 28x28

Ama 20 dönem yaptığımda 20 dönem 28x28 alıyorum

Ne olacağını bilmek istiyorum. İlk önce modelin aşırı uyum sağladığını düşündüm, ancak eğitim ve doğrulamanın kayıp işlevini kontrol ettiğimde fazla uyum sağlamıyor gibi görünüyor: eğitim ve doğrulama kaybı