Som nový Hlboké Učenie a ja som urobil model, ktorý predstiera, že upscale a 14x14 obrázok 28x28. Za to, že som nahral do newtork pomocou MNIST úloţiska ako prvý pokus o riešenie tohto problému.
Pre výrobu modelu štruktúry sledoval som túto knihu: 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, ktorý mám, je, že s 10 obdobiami, zdá sa, že prácu, to sa zmení tento obrázok:14x14 MNIST
do tejto jednej: 10 revolúciou 28x28
Ale keď som si 20 obdobiami, mám20 revolúciou 28x28
Chcem vedieť, čo sa stane. Najprv som si myslel, že možno bol model overfitting, ale keď som skontrolovať strata funkcie odborná príprava a overenie nezdá sa overfit: odborná príprava a overenie strata