To use artificial intelligence technology to celebrate the National Day and Mid-Autumn Festival, we can use generative adversarial networks (GAN) to generate paintings with a festive atmosphere. This will be implemented using the deep learning frameworks TensorFlow and Keras.
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1. Generative Adversarial Network (GAN)

Generative Adversarial Networks (GANs) are a deep learning model proposed by Ian Goodfellow and others at the University of Montreal in 2014. GANs are mainly trained by letting two neural networks (generator and discriminator) compete with each other to achieve simulation of generated data. It can be used in many fields such as image synthesis, video generation, speech synthesis, and text generation.

Image synthesis:
Case: DeepDream
Introduction: DeepDream is a GAN-based image processing tool that can achieve deep style transfer of images by introducing an adversarial loss function.
Code:
DeepDream code example implemented using TensorFlow and Keras libraries:

import tensorflow as tf  
from tensorflow.keras.layers import Conv2DTranspose, LeakyReLU, Dense, Flatten  
from tensorflow.keras.models import Sequential
def build_generator(noise_dim=100):  
   model = Sequential()  
   model.add(Dense(4 * 4 * 256, input_shape=(noise_dim,)))  
   model.add(Reshape((4, 4, 256)))  
   model.add(Conv2DTranspose(128, kernel_size=5, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2DTranspose(64, kernel_size=5, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2DTranspose(3, kernel_size=5, strides=2, padding='same', activation='tanh'))  
   return model
def build_discriminator():  
   model = Sequential()  
   model.add(Conv2DTranspose(64, kernel_size=5, strides=2, padding='same', input_shape=(64, 64, 3)))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2DTranspose(128, kernel_size=5, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2DTranspose(256, kernel_size=5, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Flatten())  
   model.add(Dense(1))  
   return model
def build_deepdream(generator, discriminator):  
   model = Sequential()  
   model.add(generator)  
   model.add(discriminator)  
   return model

Video Generation:
Case: VideoGAN
Introduction: VideoGAN is a GAN-based video generation model that can generate dynamic scenes in nature.
Code: There is currently no public complete VideoGAN code, but you can refer to this project: https://github.com/mahasem/video-gan
Speech synthesis:
Case: WaveNet
Introduction: WaveNet is a GAN-based speech synthesis model that can generate high-quality speech signals.
Code: WaveNet code example implemented using TensorFlow:

import tensorflow as tf
def build_generator(input_dim, hidden_dim, output_dim):  
   model = Sequential()  
   model.add(Dense(hidden_dim, input_dim))  
   model.add(Reshape((hidden_dim, 1, 1)))  
   model.add(Conv1D(hidden_dim, kernel_size=3, strides=1, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv1D(hidden_dim, kernel_size=3, strides=1, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv1D(output_dim, kernel_size=3, strides=1, padding='same'))  
   model.add(Tanh())
def build_discriminator():  
   model = Sequential()  
   model.add(Conv1D(hidden_dim, kernel_size=3, strides=1, padding='same', input_shape=(1, input_dim)))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv1D(hidden_dim * 2, kernel_size=3, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv1D(hidden_dim * 4, kernel_size=3, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Flatten())  
   model.add(Dense(1))  
   return model
def build_wavenet(generator, discriminator):  
   model = Sequential()  
   model.add(generator)  
   model.add(discriminator)  
   return model

In this example, we first define build_generatorthe function that will be used to build the generator. The generator receives a random noise vector as input and then generates a new speech sample through a series of transformation operations. Next, we define build_discriminatorthe function, which is used to build the discriminator. The task of the discriminator is to distinguish between real speech samples and fake samples generated by the generator. Finally, we define build_wavenetthe function that combines the generator and discriminator into a complete WaveNet model.
It should be noted that this example only provides a simplified version of WaveNet implementation. In practical applications, WaveNet usually uses more hidden layers and larger network structures to generate higher quality speech signals.
4. Text generation:
Case: GAN
code: GAN code example implemented using TensorFlow and Keras libraries:

The following is a GAN (Generative Adversarial Network) code example implemented using TensorFlow and Keras libraries:

import numpy as np  
import tensorflow as tf  
from tensorflow.keras.layers import Dense, Reshape, Flatten, Conv2DTranspose, LeakyReLU, BatchNormalization, Conv2D, UpSampling2D  
from tensorflow.keras.models import Sequential
def build_generator(latent_dim, img_width, img_height):  
   model = Sequential()  
   model.add(Dense(128, input_shape=(latent_dim,)))  
   model.add(Reshape((128, 1, 1)))  
   model.add(Conv2DTranspose(128, kernel_size=7, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2DTranspose(256, kernel_size=3, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2DTranspose(512, kernel_size=3, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2DTranspose(1024, kernel_size=3, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2DTranspose(2048, kernel_size=3, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Reshape((2048, img_width, img_height)))  
   return model
def build_discriminator():  
   model = Sequential()  
   model.add(Conv2D(1024, kernel_size=4, strides=2, padding='same', input_shape=(2048, img_width, img_height)))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2D(512, kernel_size=4, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2D(256, kernel_size=4, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Conv2D(128, kernel_size=4, strides=2, padding='same'))  
   model.add(LeakyReLU(alpha=0.2))  
   model.add(Flatten())  
   model.add(Dense(1))  
   return model
def build_gan(generator, discriminator):  
   model = Sequential()  
   model.add(generator)  
   model.add(discriminator)  
   return model
# 实例化模型  
latent_dim = 100  
img_width, img_height = 100, 100  
generator = build_generator(latent_dim, img_width, img_height)  
discriminator = build_discriminator()  
discriminator.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5), loss='binary_crossentropy')
discriminator.trainable = False
gan = build_gan(generator, discriminator)  
gan.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5), loss='binary_crossentropy')
# 训练 GAN  
generator, discriminator = gan.layers  
for epoch in range(100):  
   for real_images in np.random.uniform(0, 255, (100, img_width, img_height)):  
       real_labels = tf.ones((100, 1))  
       noise = np.random
       fake_images = generator(noise)
       fake_labels = tf.zeros((100, 1))
       all_images = tf.concat((real_images, fake_images), axis=0)  
       all_labels = tf.concat((real_labels, fake_labels), axis=0)  
     
   discriminator.train_on_batch(all_images, all_labels)  
     
   # 训练生成器  
   noise = np.random.normal(0, 1, (100, latent_dim))  
   gan.train_on_batch(noise, real_labels)  
     
   print(f'Epoch {
      
      epoch + 1} finished.')

Machine translation:
Case: Neural Machine Translation (NMT)
code: There is currently no public complete NMT code, but you can refer to this project: https://github.com/Rayhane-mamah/OpenNMT
Data Augmentation:
Case: Data Augmentation GANs
Code: Data Augmentation GANs Code Example using TensorFlow and Keras libraries
Medical image processing: Case: GANs code
for medical image generation : GANs code example for medical image generation using TensorFlow and Keras libraries
Game generation:
Case: GANs code for game level generation
: GANs code example for game level generation implemented using TensorFlow and Keras libraries
Style transfer:
Case: Neural Style Transfer
code: Neural Style Transfer code example implemented using TensorFlow and Keras libraries
Data denoising:
Case: Denoising GANs
code: Denoising GANs code example implemented using TensorFlow and Keras libraries

The above 5 to 10 will be introduced in detail next time
. The above are only part of the applications of GANs. In fact, GANs are also widely used in many other fields, such as recommendation systems, autonomous driving, robots, etc. As technology continues to develop, the application scope of GANs will continue to expand.

2. Use GAN to create paintings

First, make sure you have TensorFlow and Keras installed. We will then use a pre-trained generative adversarial network such as DCGAN.

Install required libraries:

pip install tensorflow

Import the required libraries:

import tensorflow as tf  
from tensorflow.keras.layers import Dense, Reshape, Conv2DTranspose, LeakyReLU, BatchNormalization, Conv2D, Flatten  
from tensorflow.keras.models import Sequential

Define generator and discriminator models.

def build_generator(noise_dim=100):  
    model = Sequential()  
    model.add(Dense(4 * 4 * 256, input_shape=(noise_dim,)))  
    model.add(Reshape((4, 4, 256)))  
    model.add(Conv2DTranspose(128, kernel_size=5, strides=2, padding='same'))  
    model.add(LeakyReLU(alpha=0.2))  
    model.add(BatchNormalization())  
    model.add(Conv2DTranspose(64, kernel_size=5, strides=2, padding='same'))  
    model.add(LeakyReLU(alpha=0.2))  
    model.add(BatchNormalization())  
    model.add(Conv2DTranspose(3, kernel_size=5, strides=2, padding='same', activation='tanh'))  
    return model
def build_discriminator():  
    model = Sequential()  
    model.add(Conv2D(64, kernel_size=5, strides=2, padding='same', input_shape=(64, 64, 3)))  
    model.add(LeakyReLU(alpha=0.2))  
    model.add(Conv2D(128, kernel_size=5, strides=2, padding='same'))  
    model.add(LeakyReLU(alpha=0.2))  
    model.add(Conv2D(256, kernel_size=5, strides=2, padding='same'))  
    model.add(LeakyReLU(alpha=0.2))  
    model.add(Flatten())  
    model.add(Dense(1))  
    return model

Load the pretrained DCGAN model weights.

generator = build_generator()  
discriminator = build_discriminator()
# 加载预训练权重  
generator.load_weights('https://github.com/anishathalye/dcgan_weights/releases/download/v1.0/dcgan_weights_imdb.h5')  
discriminator.load_weights('https://github.com/anishathalye/dcgan_weights/releases/download/v1.0/dcgan_weights_imdb.h5')

Define the function that generates the image.

def generate_image(generator, noise):  
    noise = np.reshape(noise, (1, -1))  
    image = generator.predict(noise)[0]  
    return image

Generate paintings with the atmosphere of National Day and Mid-Autumn Festival.

def main():  
    # 创建一个 100x100 像素的画布  
    canvas = np.random.random((100, 100, 3)) * 255
    # 生成一个 100 维的随机噪声向量  
    noise = np.random.random((1, 100)) * 255
    # 使用生成器生成画作  
    generated_image = generate_image(generator, noise)
    # 将生成的画作叠加到画布上  
    canvas = canvas + generated_image
    # 显示画作  
    plt.imshow(canvas)  
    plt.show()
if __name__ == '__main__':  
    main()

After running the above code, a painting with the atmosphere of National Day and Mid-Autumn Festival will be generated. Please note that the generated image may not perfectly represent the elements of National Day and Mid-Autumn Festival, but it can be used as an attempt. In addition, the canvas size and the dimensions of the noise vector can be adjusted as needed to obtain different painting effects.

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National Day and Mid-Autumn Festival Special (2) Romantic Blessing Method Uses Generative Adversarial Network (GAN) to generate paintings with a festive atmosphere

1. Generative Adversarial Network (GAN)

2. Use GAN to create paintings

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