Table of contents

1 Introduction to Generative Adversarial Networks (GAN)

2. Implementation method of using GAN to generate artwork

3, generate images

Application of three GANs in artistic creation

1 Style Migration

2 Image generation:

3 Image restoration:

Four Implementation methods of using GAN to generate artwork

Five success stories

Six summary

Foreword:

In this post, we will delve into the application of AI in painting and how to use Generative Adversarial Networks (GAN) to create works of art.

I. Introduction

In this article, we'll take a deep dive into the use of AI in painting, focusing on how generative adversarial networks (GANs) can be used to create artwork with unique styles and techniques. We will also introduce some specific implementation methods, demonstrate how to use GAN to generate artwork through examples, and share some success stories.

2 Generative Adversarial Network (GAN)

Introduction to Generative Adversarial Networks (GANs)

Generating an confrontation network (GAN) is a deep learning model consisting of two neural networks: a generator (Generator) and a discriminator (Discriminator). The generator is responsible for generating realistic images, and the discriminator is responsible for judging whether the image is real or not. During training, the generator and the discriminator compete against each other, optimizing until the images produced by the generator are realistic enough that the discriminator cannot distinguish real images from generated images.

2. Implementation method of using GAN to generate artwork

Here is the key code needed to implement this example:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 数据预处理
def load_and_preprocess_data(data_dir, img_size, batch_size):
    datagen = ImageDataGenerator(rescale=1./255)
    data = datagen.flow_from_directory(
        data_dir,
        target_size=(img_size, img_size),
        batch_size=batch_size,
        class_mode=None
    )
    return data

# 构建生成器
def build_generator(latent_dim, img_size):
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation='relu', input_dim=latent_dim),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Dense(256, activation='relu'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Dense(img_size * img_size * 3, activation='tanh'),
        tf.keras.layers.Reshape((img_size, img_size, 3))
    ])
    return model

# 构建判别器
def build_discriminator(img_size):
    model = tf.keras.Sequential([
        tf.keras.layers.Flatten(input_shape=(img_size, img_size, 3)),
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dense(256, activation='relu'),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    return model

# 训练模型
def train_gan(generator, discriminator, dataset, epochs, latent_dim, batch_size):
    # 定义优化器和损失函数
    generator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
    discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)
    loss_fn = tf.keras.losses.BinaryCrossentropy()

    # 训练循环
    for epoch in range(epochs):
        for batch in dataset:
            # 训练判别器
            real_images = batch
            noise =np.random.normal(0, 1, size=(batch_size, latent_dim))
        fake_images = generator.predict(noise)

        real_labels = np.ones((batch_size, 1))
        fake_labels = np.zeros((batch_size, 1))

        real_loss = loss_fn(real_labels, discriminator.predict(real_images))
        fake_loss = loss_fn(fake_labels, discriminator.predict(fake_images))
        d_loss = 0.5 * (real_loss + fake_loss)

        with tf.GradientTape() as tape:
            predictions = discriminator(real_images)
            real_loss = loss_fn(real_labels, predictions)
            predictions = discriminator(fake_images)
            fake_loss = loss_fn(fake_labels, predictions)
            d_loss = 0.5 * (real_loss + fake_loss)

        grads = tape.gradient(d_loss, discriminator.trainable_weights)
        discriminator_optimizer.apply_gradients(zip(grads, discriminator.trainable_weights))

        # 训练生成器
        noise = np.random.normal(0, 1, size=(batch_size, latent_dim))
        real_labels = np.ones((batch_size, 1))

        with tf.GradientTape() as tape:
            fake_images = generator(noise)
            predictions = discriminator(fake_images)
            g_loss = loss_fn(real_labels, predictions)

        grads = tape.gradient(g_loss, generator.trainable_weights)
        generator_optimizer.apply_gradients(zip(grads, generator.trainable_weights))

    # 输出每轮的损失值
    print(f"Epoch: {epoch + 1}, D Loss: {d_loss:.4f}, G Loss: {g_loss:.4f}")

3, generate images

import osp

# 生成图像并显示
def generate_and_display_images(generator, latent_dim, num_images):
    noise = np.random.normal(0, 1, size=(num_images, latent_dim))
    generated_images = generator.predict(noise)
    generated_images = (generated_images + 1) / 2  # 将图像的值映射到0-1范围

    fig, axes = plt.subplots(1, num_images, figsize=(num_images * 2, 2))

    for i, image in enumerate(generated_images):
        axes[i].imshow(image)
        axes[i].axis('off')
        plt.savefig(f"generated_image_{i}.png")

    plt.show()

4, the main program

# 主程序
if __name__ == "__main__":
    data_dir = "path/to/your/impressionist_dataset"
    img_size = 64
    batch_size = 32
    latent_dim = 100
    epochs = 500

    dataset = load_and_preprocess_data(data_dir, img_size, batch_size)
    generator = build_generator(latent_dim, img_size)
    discriminator = build_discriminator(img_size)
    train_gan(generator, discriminator, dataset, epochs, latent_dim, batch_size)
    generate_and_display_images(generator, latent_dim, num_images=5)

When you run the main program, it will generate and display five impressionistic images after training the GAN.

Application of three GANs in artistic creation

GAN has been widely used in artistic creation. The following are several main application scenarios:

Style Transfer: Applying one artistic style to an image of another, such as converting a photo into a painting in the style of Van Gogh or Picasso.
Image Generation: Generate completely new works of art based on input descriptions or examples, such as generating oil paintings with a specific style.
Image Restoration: Using GANs to repair damaged or missing pieces of art.

1 Style Migration

Here, we discuss in detail the application of GANs to artistic creation and provide an example of style transfer using CycleGAN. CycleGAN is a special type of GAN that allows transforming images of one style into another without paired training data.

We will use TensorFlow to implement a simple CycleGAN model that applies the style of the famous painter Van Gogh to ordinary photos. Here is the key code needed to implement this example:

First, tensorflow and tensorflow-addons need to be installed:

pip install tensorflow tensorflow-addons

Then, write the following Python code:

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, LayerNormalization, ReLU, Activation
from tensorflow.keras.models import Sequential
import tensorflow_addons as tfa
import os
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator

def load_and_preprocess_data(data_dir, img_size, batch_size):
    # ...与前面的实现相同...
    pass

def build_generator(img_size):
    # ...与前面的实现相同...
    pass

def build_discriminator(img_size):
    # ...与前面的实现相同...
    pass

def build_cyclegan(generator, discriminator, img_size):
    gen_g = generator
    gen_f = build_generator(img_size)
    disc_x = discriminator
    disc_y = build_discriminator(img_size)

    return gen_g, gen_f, disc_x, disc_y

def train_cyclegan(gen_g, gen_f, disc_x, disc_y, dataset_x, dataset_y, epochs, img_size, batch_size):
    # ...训练CycleGAN的代码，需要根据CycleGAN的特点进行修改...
    pass

def generate_images(gen_g, dataset_x, num_images):
    # ...与前面的实现相同，但需要使用gen_g将输入图像转换为目标风格...
    pass

# 主程序
if __name__ == "__main__":
    data_dir_photos = "path/to/your/photos_dataset"
    data_dir_paintings = "path/to/your/van_gogh_paintings_dataset"
    img_size = 256
    batch_size = 1
    epochs = 100

    dataset_x = load_and_preprocess_data(data_dir_photos, img_size, batch_size)
    dataset_y = load_and_preprocess_data(data_dir_paintings, img_size, batch_size)
    generator = build_generator(img_size)
    discriminator = build_discriminator(img_size)

    gen_g, gen_f, disc_x, disc_y = build_cyclegan(generator, discriminator, img_size)
    train_cyclegan(gen_g, gen_f, disc_x, disc_y, dataset_x, dataset_y, epochs, img_size, batch_size)

    num_images = 5
    generate_images(gen_g, dataset_x, num_images)

In this example, we first loaded and preprocessed two datasets containing ordinary photos and Van Gogh paintings. We then built the generator and discriminator networks, and build_cyclegancreated the CycleGAN model using functions. Next, we use our custom training loop

Train the CycleGAN model. Please note that the training function here needs to be modified according to the characteristics of CycleGAN. In this example, we do not provide detailed train_cycleganfunction implementations, you can check related literature and open source implementations for more information. Finally, we use the trained CycleGAN generator gen_gto convert the input image into a Van Gogh style image.

This is a simplified example, for better results, you may need to use more complex models, training strategies, and data preprocessing methods. Additionally, you can extend this example to other artists' styles, or even other artistic fields such as music, dance, etc.

Here is a simplified train_cycleganfunction example for your reference:

def train_cyclegan(gen_g, gen_f, disc_x, disc_y, dataset_x, dataset_y, epochs, img_size, batch_size):
    cycle_consistency_loss = tf.keras.losses.MeanAbsoluteError()
    adversarial_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)

    generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
    discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)

    for epoch in range(epochs):
        print(f"Starting epoch {epoch+1}/{epochs}")

        for batch_x, batch_y in zip(dataset_x, dataset_y):
            # 训练判别器
            with tf.GradientTape(persistent=True) as tape:
                fake_y = gen_g(batch_x, training=True)
                fake_x = gen_f(batch_y, training=True)

                disc_x_real_preds = disc_x(batch_x, training=True)
                disc_y_real_preds = disc_y(batch_y, training=True)

                disc_x_fake_preds = disc_x(fake_x, training=True)
                disc_y_fake_preds = disc_y(fake_y, training=True)

                disc_x_loss_real = adversarial_loss(tf.ones_like(disc_x_real_preds), disc_x_real_preds)
                disc_y_loss_real = adversarial_loss(tf.ones_like(disc_y_real_preds), disc_y_real_preds)

                disc_x_loss_fake = adversarial_loss(tf.zeros_like(disc_x_fake_preds), disc_x_fake_preds)
                disc_y_loss_fake = adversarial_loss(tf.zeros_like(disc_y_fake_preds), disc_y_fake_preds)

                disc_x_loss = 0.5 * (disc_x_loss_real + disc_x_loss_fake)
                disc_y_loss = 0.5 * (disc_y_loss_real + disc_y_loss_fake)

            disc_x_grads = tape.gradient(disc_x_loss, disc_x.trainable_variables)
            disc_y_grads = tape.gradient(disc_y_loss, disc_y.trainable_variables)

            discriminator_optimizer.apply_gradients(zip(disc_x_grads, disc_x.trainable_variables))
            discriminator_optimizer.apply_gradients(zip(disc_y_grads, disc_y.trainable_variables))

            # 训练生成器
            with tf.GradientTape(persistent=True) as tape:
                fake_y = gen_g(batch_x, training=True)
                fake_x = gen_f(batch_y, training=True)

                reconstructed_x = gen_f(fake_y, training=True)
                reconstructed_y = gen_g(fake_x, training=True)

                disc_x_fake_preds = disc_x(fake_x, training=True)
disc_y_fake_preds = disc_y(fake_y, training=True)
            gen_g_loss = adversarial_loss(tf.ones_like(disc_y_fake_preds), disc_y_fake_preds)
            gen_f_loss = adversarial_loss(tf.ones_like(disc_x_fake_preds), disc_x_fake_preds)

            cycle_loss_g = cycle_consistency_loss(batch_x, reconstructed_x)
            cycle_loss_f = cycle_consistency_loss(batch_y, reconstructed_y)

            total_cycle_loss = cycle_loss_g + cycle_loss_f
            total_gen_g_loss = gen_g_loss + total_cycle_loss
            total_gen_f_loss = gen_f_loss + total_cycle_loss

        gen_g_grads = tape.gradient(total_gen_g_loss, gen_g.trainable_variables)
        gen_f_grads = tape.gradient(total_gen_f_loss, gen_f.trainable_variables)

        generator_optimizer.apply_gradients(zip(gen_g_grads, gen_g.trainable_variables))
        generator_optimizer.apply_gradients(zip(gen_f_grads, gen_f.trainable_variables))

    print(f"Epoch {epoch+1}/{epochs} completed")

The above `train_cyclegan` function provides a simplified training process that covers the main features of CycleGAN. In practice, training CycleGAN is time- and computationally resource-intensive, so the examples here are for illustration purposes only. In practical applications, you may need to train on a larger dataset for a longer period of time, and adjust hyperparameters and model structure to achieve better results.

In summary, this example shows how to use CycleGAN to transform images of one style into another in artistic creation. You can extend this approach to other arts such as music, dance, etc. Also, you can improve this example by using more advanced GAN models and training strategies.

2 Image generation:

We have discussed an example of using DCGAN to generate impressionist-style images in a previous answer. Here, we will use StyleGAN2 for image generation. StyleGAN2 is a powerful image generation model capable of generating extremely photorealistic images.

We will use the pretrained StyleGAN2 model to generate face images. First, the required libraries need to be installed:

pip install tensorflow

Next, we will use the following code to generate and display the face image:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def generate_latent_vectors(num_vectors, latent_dim):
    return np.random.normal(0, 1, size=(num_vectors, latent_dim))

def generate_and_display_images(generator, latent_vectors):
    generated_images = generator(latent_vectors)
    generated_images = (generated_images + 1) / 2  # 将图像的值映射到0-1范围

    fig, axes = plt.subplots(1, len(latent_vectors), figsize=(len(latent_vectors) * 2, 2))

    for i, image in enumerate(generated_images):
        axes[i].imshow(image)
        axes[i].axis('off')
        plt.savefig(f"generated_image_{i}.png")

    plt.show()

if __name__ == "__main__":
    stylegan2_model_url = "https://tfhub.dev/google/stylegan2_swapped_1024x1024/1"
    generator = tf.keras.models.load_model(stylegan2_model_url)

    latent_dim = 512
    num_images = 5
    latent_vectors = generate_latent_vectors(num_images, latent_dim)

    generate_and_display_images(generator, latent_vectors)

In this example, we loaded the pretrained StyleGAN2 model from TensorFlow Hub, generated random latent vectors, and used these latent vectors to generate face images. We then display the resulting image on the screen.

3 Image restoration:

We will use a model called Partial Convolutional Neural Networks (PConv) to achieve image inpainting. The PConv model is an image inpainting model based on convolutional neural networks, which can repair missing parts in images.

First, the required libraries need to be installed:

pip install tensorflow

Next, we will use the following code for image inpainting:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import load_img, img_to_array, array_to_img

def load_image(image_path, target_size):
    img = load_img(image_path, target_size=target_size)
    img_array = img_to_array(img)
    img_array = (img_array - 127.5) / 127.5  # 将图像的值映射到-1到1范围
    return np.expand_dims(img_array, axis=0)

def display_image(image_array):
    image = array_to_img((image_array[0] + 1) / 2) # 将图像的值映射到0-1范围
plt.imshow(image)
plt.axis('off')
plt.savefig("repaired_image.png")
plt.show()

if name == "main":
pconv_model_url = "https://tfhub.dev/google/pconv_imagenet_512/1"
inpainter = tf.keras.models.load_model(pconv_model_url)

image_path = "your_image_path_here"  # 替换为您的输入图像路径
mask_path = "your_mask_path_here"     # 替换为您的遮罩图像路径

image_size = (512, 512)
image = load_image(image_path, image_size)
mask = load_image(mask_path, image_size)

repaired_image = inpainter.predict([image, mask])

display_image(repaired_image)

In this example, we loaded the pretrained PConv model from TensorFlow Hub, then loaded the input image and mask image. The input image is the image that needs to be inpainted, and the mask image defines the areas that need to be inpainted (white areas indicate the parts that need to be inpainted, and black areas indicate the parts that do not need to be inpainted). Then we use the PConv model to repair the input image, and finally display the repaired image on the screen.

Through these two examples, you can learn about the application of GANs in image generation and image restoration in artistic creation. You can adjust the sample code to suit different input data and output requirements according to your actual needs. Also, try to improve these examples with other advanced GAN models and techniques.

Four Implementation methods of using GAN to generate artwork

Here, we discuss in detail methods for generating artwork using GANs. We will use a model called "BigGAN" to generate high-resolution artistic images. BigGAN is a powerful image generation model that can generate extremely realistic and creative images.

First, the required libraries need to be installed:

pip install tensorflow

Next, we'll generate and display the art image using the following code:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def generate_latent_vectors(num_vectors, latent_dim):
    return np.random.normal(0, 1, size=(num_vectors, latent_dim))

def generate_and_display_images(generator, latent_vectors, truncation):
    generated_images = generator(latent_vectors, truncation)
    generated_images = (generated_images + 1) / 2  # 将图像的值映射到0-1范围

    fig, axes = plt.subplots(1, len(latent_vectors), figsize=(len(latent_vectors) * 2, 2))

    for i, image in enumerate(generated_images):
        axes[i].imshow(image)
        axes[i].axis('off')
        plt.savefig(f"generated_artwork_{i}.png")

    plt.show()

if __name__ == "__main__":
    biggan_model_url = "https://tfhub.dev/google/biggan-256/2"
    generator = tf.keras.models.load_model(biggan_model_url)

    latent_dim = 128
    num_images = 5
    latent_vectors = generate_latent_vectors(num_images, latent_dim)
    truncation = 0.5  # 控制图像生成的多样性（更高的截断值会导致更多样的图像）

    generate_and_display_images(generator, latent_vectors, truncation)

In this example, we loaded a pretrained BigGAN model from TensorFlow Hub, generated random latent vectors, and used these latent vectors to generate artistic images. We then display the resulting image on the screen.

It is important to note that the BigGAN model is trained on the ImageNet dataset, so it is not designed for artistic image generation itself. However, since the images generated by GAN are usually rich in texture and color, this allows them to be regarded as works of artistic value. You can control the style and diversity of the generated images by adjusting the latent vectors and cutoff values.

Also, you can try to improve this example with other advanced GAN models and techniques for better artistic image generation. For example, you can combine BigGAN with other pretrained art style GAN models, or try training a GAN with a custom dataset to generate a specific style of art.

Here, we will implement a simple GAN model and generate simple images of handwritten digits. We will use Keras and TensorFlow to build the model.

First, the required libraries need to be installed:

pip install tensorflow

Next, we will implement a simple GAN model by following the steps below:

Import the required libraries:

import tensorflow as tf
from tensorflow.keras.layers import Dense, LeakyReLU, BatchNormalization, Reshape, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
import numpy as np
import matplotlib.pyplot as plt

Load the MNIST dataset:

(X_train, _), (_, _) = tf.keras.datasets.mnist.load_data()
X_train = X_train / 255.0  # 将图像的值映射到0-1范围
X_train = np.expand_dims(X_train, -1)

Create a generator model:

def create_generator(latent_dim):
    model = Sequential()
    model.add(Dense(128 * 7 * 7, activation="relu", input_dim=latent_dim))
    model.add(Reshape((7, 7, 128)))
    model.add(tf.keras.layers.UpSampling2D())
    model.add(tf.keras.layers.Conv2D(128, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.UpSampling2D())
    model.add(tf.keras.layers.Conv2D(64, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Conv2D(1, kernel_size=3, padding="same", activation="sigmoid"))

    return model

Create a discriminator model:

def create_discriminator(input_shape):
    model = Sequential()
    model.add(tf.keras.layers.Conv2D(32, kernel_size=3, strides=2, padding="same", input_shape=input_shape))
    model.add(LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dropout(0.25))
    model.add(tf.keras.layers.Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dropout(0.25))
    model.add(tf.keras.layers.Conv2D(128, kernel_size=3, strides=2, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(1, activation="sigmoid"))

    return model

Create a GAN model:

def create_gan(generator, discriminator, latent_dim):
    discriminator.trainable = False
    gan_input = tf.keras.Input(shape=(latent_dim,))
    x = generator(gan_input)
    gan_output = discriminator(x)
    gan = tf.keras.Model(inputs=gan_input, outputs=gan_output)

    return gan

Define the training process:

def train_gan(epochs, batch_size, latent_dim, generator,discriminator, gan, X_train):
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(epochs):
    # 训练判别器
    idx = np.random.randint(0, X_train.shape[0], batch_size)
    real_images = X_train[idx]

    noise = np.random.normal(0, 1, (batch_size, latent_dim))
    gen_images = generator.predict(noise)

    real_loss = discriminator.train_on_batch(real_images, valid)
    fake_loss = discriminator.train_on_batch(gen_images, fake)
    discriminator_loss = 0.5 * np.add(real_loss, fake_loss)

    # 训练生成器
    noise = np.random.normal(0, 1, (batch_size, latent_dim))
    generator_loss = gan.train_on_batch(noise, valid)

    if epoch % 1000 == 0:
        print(f"Epoch {epoch}, Discriminator Loss: {discriminator_loss}, Generator Loss: {generator_loss}")

        # 显示生成的图像
        generated_images = generator.predict(noise)
        plot_generated_images(generated_images)
def plot_generated_images(images, n=5):
fig, axes = plt.subplots(1, n, figsize=(n * 2, 2))
for i, image in enumerate(images[:n]):
    axes[i].imshow(image.squeeze(), cmap="gray")
    axes[i].axis("off")

plt.show()

7. Initialize and train the GAN model:

latent_dim = 100
input_shape = X_train.shape[1:]
epochs = 20000
batch_size = 64

generator = create_generator(latent_dim)
discriminator = create_discriminator(input_shape)
discriminator.compile(optimizer=Adam(0.0002, 0.5), loss="binary_crossentropy", metrics=["accuracy"])

gan = create_gan(generator, discriminator, latent_dim)
gan.compile(optimizer=Adam(0.0002, 0.5), loss="binary_crossentropy")

train_gan(epochs, batch_size, latent_dim, generator, discriminator, gan, X_train)

This code will create a simple GAN model and train it using the MNIST dataset of handwritten digits. During training, every 1000 epochs, the code will display a set of generated images to demonstrate the generator's progress.

Note that this simple GAN model may not be able to generate very realistic images of handwritten digits. For better results, you can try using more complex network architectures such as DCGAN (Deep Convolutional GAN) or other advanced GAN models. Alternatively, you can try using a larger dataset and more training iterations.

8 Generate the image

Generating images is achieved through generators. A generator is a neural network that takes as input a vector of random noise and outputs an image. In our example, we used a simple Convolutional Neural Network (CNN) as the generator.

Here are the main parts of generating an image: the creation of the generator model:

def create_generator(latent_dim):
    model = Sequential()
    model.add(Dense(128 * 7 * 7, activation="relu", input_dim=latent_dim))
    model.add(Reshape((7, 7, 128)))
    model.add(tf.keras.layers.UpSampling2D())
    model.add(tf.keras.layers.Conv2D(128, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.UpSampling2D())
    model.add(tf.keras.layers.Conv2D(64, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Conv2D(1, kernel_size=3, padding="same", activation="sigmoid"))

    return model

Generate images using a generator: During training, we generate images and display them via:
```
def plot_generated_images(images, n=5):
    fig, axes = plt.subplots(1, n, figsize=(n * 2, 2))

    for i, image in enumerate(images[:n]):
        axes[i].imshow(image.squeeze(), cmap="gray")
        axes[i].axis("off")

    plt.show()
```
In train_ganthe function, we generate a random noise vector and pass it to the generator to generate the image. Then we use plot_generated_imagesthe function to display the resulting image.
```
if epoch % 1000 == 0:
    print(f"Epoch {epoch}, Discriminator Loss: {discriminator_loss}, Generator Loss: {generator_loss}")

    # 显示生成的图像
    generated_images = generator.predict(noise)
    plot_generated_images(generated_images)
```
These code snippets are responsible for generating the images. A generator model takes a random noise vector as input and outputs an image. During training, every 1000 epochs, we use plot_generated_imagesthe function to display the generated images. Note that this simple GAN model may not be able to generate very realistic images of handwritten digits. For better results, try using more complex network architectures such as DCGAN (Deep Convolutional GAN) or other advanced GAN models.

Five success stories

A well-known success story is DeepArt.io, which uses a technique called "neural style transfer" to transfer the style of one image to another. Neural style transfer is an optimization technique that uses a convolutional neural network (CNN) to blend the content and style of two images.

Here is a simple example of Neural Style Transfer using TensorFlow:

First, the required libraries need to be installed:
```
pip install tensorflow
```

Import the necessary libraries:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

Download the VGG19 pre-trained model:

vgg = tf.keras.applications.vgg19.VGG19(include_top=False, weights='imagenet')

Define content and style loss functions:

def content_loss(content, target):
    return tf.reduce_mean(tf.square(content - target))

def gram_matrix(input_tensor):
    channels = int(input_tensor.shape[-1])
    a = tf.reshape(input_tensor, [-1, channels])
    n = tf.shape(a)[0]
    gram = tf.matmul(a, a, transpose_a=True)
    return gram / tf.cast(n, tf.float32)

def style_loss(style, gram_target):
    gram_style = gram_matrix(style)
    return tf.reduce_mean(tf.square(gram_style - gram_target))

Create a style transfer model for an image:

def style_transfer_model(content_layers, style_layers, vgg_model):
    vgg_model.trainable = False
    style_outputs = [vgg_model.get_layer(name).output for name in style_layers]
    content_outputs = [vgg_model.get_layer(name).output for name in content_layers]
    model_outputs = style_outputs + content_outputs
    return tf.keras.Model(vgg_model.input, model_outputs)

Implementation of style transfer:

def transfer_style(content_image, style_image, content_weight, style_weight, variation_weight, epochs, steps_per_epoch):
    content_layers = ['block5_conv2']
    style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']
    
    content_image = tf.keras.applications.vgg19.preprocess_input(content_image * 255)
    style_image = tf.keras.applications.vgg19.preprocess_input(style_image * 255)
    
    content_image = tf.image.resize(content_image, (224, 224))
    style_image = tf.image.resize(style_image, (224, 224))
    
    content_targets = vgg(content_image)[:-1]
    style_targets = [gram_matrix(style_layer) for style_layer in vgg(style_image)[:-1]]
    
    transfer_model = style_transfer_model(content_layers, style_layers, vgg)
    
    def total_loss(outputs, content_weight, style_weight, variation_weight):
        style_outputs = outputs[:len(style_targets)]
        content_outputs = outputs[len(style_targets):]
        
        content_losses = [content_loss(content_output, content_target) for content_output, content_target in zip(content_outputs, content_targets)]
        style_losses = [style_loss(style_output, style_target) for style_output, style_target in zip(style_outputs, style_targets)]
    content_total_loss = tf.reduce_sum(content_losses)
    style_total_loss = tf.reduce_sum(style_losses)

    content_loss_scaled = content_weight * content_total_loss
    style_loss_scaled = style_weight * style_total_loss
    variation_loss_scaled = variation_weight * tf.image.total_variation(outputs[-1])

    return content_loss_scaled + style_loss_scaled + variation_loss_scaled

opt = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)

image = tf.Variable(content_image)

for epoch in range(epochs):
    print(f"Epoch {epoch + 1}/{epochs}")

    for step in range(steps_per_epoch):
        with tf.GradientTape() as tape:
            outputs = transfer_model(image)
            loss = total_loss(outputs, content_weight, style_weight, variation_weight)
            grads = tape.gradient(loss, image)
            opt.apply_gradients([(grads, image)])
            clipped_image = tf.clip_by_value(image, 0, 1)
            image.assign(clipped_image)

return image.numpy().squeeze()

7. Load and preprocess the image:

def load_image(image_path):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_image(image, channels=3)
    image = tf.image.convert_image_dtype(image, tf.float32)
    image = tf.expand_dims(image, axis=0)
    return image

content_image_path = "path/to/your/content/image.jpg"
style_image_path = "path/to/your/style/image.jpg"

content_image = load_image(content_image_path)
style_image = load_image(style_image_path)

8. Start the style migration and display the result:

content_weight = 1e4
style_weight = 1e-2
variation_weight = 30
epochs = 10
steps_per_epoch = 100

output_image = transfer_style(content_image, style_image, content_weight, style_weight, variation_weight, epochs, steps_per_epoch)

plt.imshow(output_image)
plt.axis('off')
plt.show()

This code will perform neural style transfer, applying the style of the style image to the content image. Depending on the selected images, model parameters, and number of iterations, you may need to adjust the weight parameters ( content_weight, style_weight, and variation_weight) to achieve desired results.

Six summary

In the first week of this column, we focused on the application of AI in the arts and creative industries. We discussed the following aspects in detail:

How AI is changing the art-making process: We tell how AI is giving artists new creative tools and techniques, allowing them to create in ways never before possible. This includes using deep learning techniques to automatically generate artwork, inspiring artists and helping them work more efficiently.
Introduction to Generative Adversarial Networks (GAN): We provide a detailed introduction to the fundamentals, structure, and working mechanism of Generative Adversarial Networks (GANs). GAN is a powerful deep learning technique that adversarially trains a generator and a discriminator to generate realistic images, audio, and other types of data.
Applications of GANs to Art Creation: We explore how GANs can be used to generate artwork, including generating new images, music, and other types of creative work. We also discuss how GANs can be used for image inpainting and enhancement to improve the quality and visual impact of artwork.
Implementation of GAN to generate artwork: We provide a simple implementation example using TensorFlow to create a basic GAN model and use it to generate images of handwritten digits. We emphasize that in order to achieve better results, one can try to use more complex network architectures, such as DCGAN (Deep Convolutional GAN) or other advanced GAN models.
Success Stories: We introduced some famous examples of using AI technology to create works of art, including Neural Style Transfer and DeepArt.io, etc. We provide an example of a simple implementation of neural style transfer, showing how to transfer the style of one image to another.

Through this column, we hope to provide readers with a comprehensive overview of the application of AI in the arts and creative industries. Artificial intelligence provides artists and creators with new tools and techniques that allow them to create in more efficient and innovative ways. Although there are still many challenges and room for development in this field, we believe that AI will continue to bring more opportunities and possibilities to the art and creative industries.

"In-depth discussion: the application of AI in the field of painting and the generation of confrontation network"

Foreword:

I. Introduction

2 Generative Adversarial Network (GAN)

Introduction to Generative Adversarial Networks (GANs)

2. Implementation method of using GAN to generate artwork

3, generate images

Application of three GANs in artistic creation

1 Style Migration

2 Image generation:

3 Image restoration:

Four Implementation methods of using GAN to generate artwork

Five success stories

Six summary

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