gan生成eeg数据程序python

生成 EEG 数据的 GAN 程序可以使用 Python 和深度学习框架 TensorFlow 或 PyTorch。以下是一个基本的代码框架：

1. 导入必要的库和数据集

import tensorflow as tf
import numpy as np
import scipy.io as sio

# 读取 EEG 数据集
eeg_data = sio.loadmat('eeg_data.mat')
X = eeg_data['X']

2. 定义生成器和判别器模型

# 定义生成器模型
def build_generator(latent_dim):
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(128, input_dim=latent_dim))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dense(256))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dense(512))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dense(1024))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dense(2048))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dense(4096, activation='tanh'))
    return model

# 定义判别器模型
def build_discriminator(input_shape):
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(2048, input_shape=input_shape))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dropout(0.3))
    model.add(tf.keras.layers.Dense(1024))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dropout(0.3))
    model.add(tf.keras.layers.Dense(512))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dropout(0.3))
    model.add(tf.keras.layers.Dense(256))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dropout(0.3))
    model.add(tf.keras.layers.Dense(128))
    model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
    model.add(tf.keras.layers.Dropout(0.3))
    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
    return model

3. 定义 GAN 模型

# 定义 GAN 模型
def build_gan(generator, discriminator):
    discriminator.trainable = False
    model = tf.keras.Sequential()
    model.add(generator)
    model.add(discriminator)
    model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=0.0002, beta_1=0.5))
    return model

4. 定义训练函数

# 定义训练函数
def train(generator, discriminator, gan, X, latent_dim, epochs, batch_size):
    for epoch in range(epochs):
        # 生成随机的输入噪声
        noise = np.random.normal(0, 1, (batch_size, latent_dim))
        # 使用生成器生成假数据
        gen_data = generator.predict(noise)
        # 随机选择真实数据
        idx = np.random.randint(0, X.shape[0], batch_size)
        real_data = X[idx]
        # 训练判别器
        discriminator_loss_real = discriminator.train_on_batch(real_data, np.ones((batch_size, 1)))
        discriminator_loss_fake = discriminator.train_on_batch(gen_data, np.zeros((batch_size, 1)))
        discriminator_loss = 0.5 * np.add(discriminator_loss_real, discriminator_loss_fake)
        # 训练生成器
        noise = np.random.normal(0, 1, (batch_size, latent_dim))
        generator_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))
        # 输出损失信息
        print('Epoch:', epoch, 'Discriminator Loss:', discriminator_loss, 'Generator Loss:', generator_loss)

5. 运行训练

# 运行训练
latent_dim = 100
input_shape = (X.shape[1],)
batch_size = 32
epochs = 1000

generator = build_generator(latent_dim)
discriminator = build_discriminator(input_shape)
gan = build_gan(generator, discriminator)

train(generator, discriminator, gan, X, latent_dim, epochs, batch_size)

请注意，上述代码仅为示例，实际上需要根据数据集的特征进行调整和优化。此外，还可以尝试使用其他 GAN 变体，如 WGAN 和 WGAN-GP，以获得更好的生成效果。