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Model.py
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Model.py
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import os
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import cv2
from tensorflow.keras.datasets import cifar10, mnist
from tensorflow.keras.layers import (BatchNormalization, Conv2D, Conv2DTranspose, Dense,
Dropout, Flatten, Input, Reshape, UpSampling2D,
ZeroPadding2D)
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.optimizers import Adam
from PIL import Image, ImageDraw
# Consistent results
np.random.seed(10)
# The dimension of z
noise_dim = 100
epochs = 1000
save_path = 'outputs'
img_rows, img_cols, channels = 28, 28, 1
optimizer = Adam(0.0001, beta_1=0.5)
# Create path for saving images
def save_original():
counter = 0
plt.figure(figsize=(5, 5))
for filename in os.listdir("handwriting_samples"):
if (filename.endswith(".png") and counter < 25):
original_image = cv2.imread(f'handwriting_samples/{filename}')
plt.subplot(5,5,counter+1)
plt.imshow(original_image)
plt.axis('off')
counter += 1
plt.tight_layout()
plt.savefig(f'{save_path}/original.png')
if not os.path.isdir(save_path):
os.mkdir(save_path)
save_original()
reg_array = []
for filename in os.listdir("handwriting_samples"):
if (filename.endswith(".png")) :
image = cv2.imread(f'handwriting_samples/{filename}')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
reg_array.append(gray_image)
x_train = np.array(reg_array)
batch_size = 8
steps_per_epoch = int(len(reg_array)/batch_size)
x_train = (x_train.astype(np.float32) - 127.5) / 127.5
x_train = x_train.reshape(-1, img_rows*img_cols*channels)
print(x_train.ndim, x_train.shape, x_train.size)
def create_generator():
generator = Sequential()
generator.add(Dense(256, input_dim=noise_dim))
generator.add(LeakyReLU(0.2))
generator.add(BatchNormalization())
generator.add(Dense(512))
generator.add(LeakyReLU(0.2))
generator.add(BatchNormalization())
generator.add(Dense(1024))
generator.add(LeakyReLU(0.2))
generator.add(BatchNormalization())
generator.add(Dense(img_rows*img_cols*channels, activation='tanh'))
generator.compile(loss='binary_crossentropy', optimizer=optimizer)
return generator
def create_descriminator():
discriminator = Sequential()
discriminator.add(Dense(1024, input_dim=img_rows*img_cols*channels))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dense(512))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dense(256))
discriminator.add(LeakyReLU(0.2))
discriminator.add(Dense(1, activation="sigmoid"))
discriminator.compile(loss='binary_crossentropy', optimizer=optimizer)
return discriminator
discriminator = create_descriminator()
generator = create_generator()
discriminator.trainable = False
gan_input = Input(shape=(noise_dim,))
fake_image = generator(gan_input)
gan_output = discriminator(fake_image)
gan = Model(gan_input, gan_output)
gan.compile(loss='binary_crossentropy', optimizer=optimizer)
gan.summary()
def show_images(noise, epoch=None):
generated_images = generator.predict(noise)
plt.figure(figsize=(5, 5))
for i, image in enumerate(generated_images):
plt.subplot(5, 5, i+1)
if channels == 1:
plt.imshow(image.reshape((img_rows, img_cols)), cmap='gray')
else:
plt.imshow(image.reshape((img_rows, img_cols, channels)))
plt.axis('off')
plt.tight_layout()
if epoch != None:
plt.savefig(f'{save_path}/gan-images_epoch-{epoch}.png')
# Constant noise for viewing how the GAN progresses
static_noise = np.random.normal(0, 1, size=(25, noise_dim))
for epoch in range(epochs):
for batch in range(steps_per_epoch):
noise = np.random.normal(0, 1, size=(batch_size, noise_dim))
fake_x = generator.predict(noise)
real_x = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
"""
disc_y_real = np.ones(batch_size)
disc_y_real[0:] = 0.9
d_loss_real = discriminator.train_on_batch(real_x, disc_y_real)
disc_y_fake = np.zeros(batch_size)
d_loss_fake = discriminator.train_on_batch(fake_x, disc_y_fake)
d_loss = 0
"""
x = np.concatenate((real_x, fake_x))
disc_y = np.zeros(2*batch_size)
disc_y[:batch_size] = 0.9
d_loss = discriminator.train_on_batch(x, disc_y)
y_gen = np.ones(batch_size)
g_loss = gan.train_on_batch(noise, y_gen)
if epoch%5==0:
print(f'Epoch: {epoch} \t Discriminator Loss: {d_loss} \t\t Generator Loss: {g_loss}')
show_images(static_noise, epoch)