Untitled

from tensorflow.keras.layers import Reshape, Dropout, Dense, Flatten, BatchNormalization, Activation, ZeroPadding2D, LeakyReLU, UpSampling2D, Conv2D
from tensorflow.keras import Sequential, Model, Input
from tensorflow.keras.optimizers import Adam
import numpy as np
from PIL import Image
import os

# Preview image Frame
PREVIEW_ROWS = 4
PREVIEW_COLS = 7
PREVIEW_MARGIN = 2
SAVE_FREQ = 100

# Size vector to generate images from
NOISE_SIZE = 4112

# Configuration
EPOCHS = 50000  # number of iterations
BATCH_SIZE = 64

GENERATE_RES = 3
IMAGE_SIZE = 128  # rows/cols

IMAGE_CHANNELS = 3

training_data = np.load('fauvism_data.npy')

def build_discriminator(image_shape):

    model = Sequential()

    model.add(Conv2D(32, kernel_size=3, strides=2,
                     input_shape=image_shape, padding="same"))
    model.add(LeakyReLU(alpha=0.4))

    model.add(Dropout(0.5))
    model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(ZeroPadding2D(padding=((0, 1), (0, 1))))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.4))

    model.add(Dropout(0.5))
    model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.4))

    model.add(Dropout(0.5))
    model.add(Conv2D(256, kernel_size=3, strides=2, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.4))

    model.add(Dropout(0.5))
    model.add(Conv2D(512, kernel_size=3, strides=2, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.4))

    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))

    input_image = Input(shape=image_shape)

    validity = model(input_image)

    return Model(input_image, validity)


def build_generator(noise_size, channels):
    model = Sequential()

    model.add(Dense(4 * 4 * 256, activation="relu", input_dim=noise_size))
    model.add(Reshape((4, 4, 256)))

    model.add(UpSampling2D())
    model.add(Conv2D(256, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(UpSampling2D())
    model.add(Conv2D(256, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    for i in range(GENERATE_RES):
        model.add(UpSampling2D())
        model.add(Conv2D(256, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))

    model.summary()
    model.add(Conv2D(channels, kernel_size=3, padding="same"))
    model.add(Activation("tanh"))

    input = Input(shape=(noise_size,))
    generated_image = model(input)

    return Model(input, generated_image)


def save_images(cnt, noise):
    image_array = np.full((
        PREVIEW_MARGIN + (PREVIEW_ROWS * (IMAGE_SIZE + PREVIEW_MARGIN)),
        PREVIEW_MARGIN + (PREVIEW_COLS * (IMAGE_SIZE + PREVIEW_MARGIN)), 3),
        255, dtype=np.uint8)

    generated_images = generator.predict(noise)

    generated_images = 0.5 * generated_images + 0.5

    image_count = 0
    for row in range(PREVIEW_ROWS):
        for col in range(PREVIEW_COLS):
            r = row * (IMAGE_SIZE + PREVIEW_MARGIN) + PREVIEW_MARGIN
            c = col * (IMAGE_SIZE + PREVIEW_MARGIN) + PREVIEW_MARGIN
            image_array[r:r + IMAGE_SIZE, c:c +
                        IMAGE_SIZE] = generated_images[image_count] * 255
            image_count += 1

    output_path = 'output'
    if not os.path.exists(output_path):
        os.makedirs(output_path)

    filename = os.path.join(output_path, f"trained-{cnt}.png")
    im = Image.fromarray(image_array)
    im.save(filename)


image_shape = (IMAGE_SIZE, IMAGE_SIZE, IMAGE_CHANNELS)

optimizer = Adam(1.5e-4, 0.5)

discriminator = build_discriminator(image_shape)
discriminator.compile(loss="binary_crossentropy",
                      optimizer=optimizer, metrics=["accuracy"])
generator = build_generator(NOISE_SIZE, IMAGE_CHANNELS)

random_input = Input(shape=(NOISE_SIZE,))

generated_image = generator(random_input)

discriminator.trainable = False

validity = discriminator(generated_image)
print(validity)
combined = Model(random_input, validity)
combined.compile(loss="binary_crossentropy",
                 optimizer=optimizer, metrics=["accuracy"])

y_real = np.ones((BATCH_SIZE, 1))
y_fake = np.zeros((BATCH_SIZE, 1))

fixed_noise = np.random.normal(0, 1, (PREVIEW_ROWS * PREVIEW_COLS, NOISE_SIZE))
cnt = 1


for epoch in range(EPOCHS):
    idx = np.random.randint(0, training_data.shape[0], BATCH_SIZE)
    x_real = training_data[idx]

    noise = np.random.normal(0, 1, (BATCH_SIZE, NOISE_SIZE))
    x_fake = generator.predict(noise)

    discriminator_metric_real = discriminator.train_on_batch(x_real, y_real)

    discriminator_metric_generated = discriminator.train_on_batch(x_fake, y_fake)

    discriminator_metric = 0.5 * \
        np.add(discriminator_metric_real, discriminator_metric_generated)

    generator_metric = combined.train_on_batch(noise, y_real)
    if epoch % SAVE_FREQ == 0:
        save_images(cnt, fixed_noise)
        cnt += 1

        print(f"{epoch} epoch, Discriminator accuracy: {100* discriminator_metric[1]}, Generator accuracy: {100 * generator_metric[1]}")
        generator.save(os.path.join('output', "modern_art_generator.h5"))