Eu sou novo para uma Aprendizagem mais Profunda e eu fiz um modelo que finge de luxo um 14x14 imagem para um 28x28. Para isso, eu treinei a newtork, caixa usando MNIST repositório como uma primeira tentativa para solucionar este problema.
Para fazer o modelo de estrutura segui este papel: 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()
O problema que eu tenho é que com 10 épocas parece funcionar, ele transforma essa imagem:14x14 MNIST
em um presente: 10 épocas 28x28
Mas quando eu faço 20 épocas eu obter20 épocas 28x28
Eu quero saber o que acontece. Primeiro eu pensei que talvez o modelo foi overfitting, mas quando eu verificar a perda de função de treinamento e de validação de que ele parece não overfit: de treinamento e de validação de perda