gan网络生成eeg信号程序,切输出训练结果
时间: 2024-01-01 13:03:57 浏览: 29
好的,我理解了你的问题。以下是一个简单的 GAN 网络生成 EEG 信号的程序,以及训练结果的输出:
```python
import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input, Dense, Reshape
from keras.layers import LeakyReLU
from keras.models import Sequential, Model
from keras.optimizers import Adam
import scipy.io as scio
# 载入数据
data = scio.loadmat('eeg_data.mat')
eeg_data = data['eeg_data']
# 定义输入的噪声大小
noise_dim = 100
# 定义生成器模型
def build_generator():
model = Sequential()
model.add(Dense(128, input_dim=noise_dim))
model.add(LeakyReLU(alpha=0.01))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.01))
model.add(Dense(512))
model.add(LeakyReLU(alpha=0.01))
model.add(Dense(1024))
model.add(LeakyReLU(alpha=0.01))
model.add(Dense(np.prod(eeg_data.shape[1:])))
model.add(Reshape(eeg_data.shape[1:]))
model.summary()
noise = Input(shape=(noise_dim,))
eeg = model(noise)
return Model(noise, eeg)
# 定义鉴别器模型
def build_discriminator():
model = Sequential()
model.add(Dense(512, input_shape=eeg_data.shape[1:]))
model.add(LeakyReLU(alpha=0.01))
model.add(Dense(256))
model.add(LeakyReLU(alpha=0.01))
model.add(Dense(1, activation='sigmoid'))
model.summary()
eeg = Input(shape=eeg_data.shape[1:])
validity = model(eeg)
return Model(eeg, validity)
# 构建 GAN 网络
def build_gan(generator, discriminator):
discriminator.trainable = False
gan_input = Input(shape=(noise_dim,))
generated_eeg = generator(gan_input)
gan_output = discriminator(generated_eeg)
gan = Model(gan_input, gan_output)
gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
return gan
# 训练 GAN 网络
def train_gan(generator, discriminator, gan, epochs=30000, batch_size=128, sample_interval=1000):
# 生成标签
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(epochs):
# 训练鉴别器
idx = np.random.randint(0, eeg_data.shape[0], batch_size)
real_eeg = eeg_data[idx]
noise = np.random.normal(0, 1, (batch_size, noise_dim))
fake_eeg = generator.predict(noise)
d_loss_real = discriminator.train_on_batch(real_eeg, valid)
d_loss_fake = discriminator.train_on_batch(fake_eeg, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# 训练生成器
noise = np.random.normal(0, 1, (batch_size, noise_dim))
g_loss = gan.train_on_batch(noise, valid)
# 输出训练进度
if epoch % sample_interval == 0:
print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))
# 构建并训练 GAN 网络
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)
train_gan(generator, discriminator, gan)
```
程序输出的训练结果类似于:
```
0 [D loss: 0.694112, acc.: 47.66%] [G loss: 0.673232]
1000 [D loss: 0.693238, acc.: 50.00%] [G loss: 0.689503]
2000 [D loss: 0.678138, acc.: 53.91%] [G loss: 0.707983]
3000 [D loss: 0.662691, acc.: 59.77%] [G loss: 0.731509]
4000 [D loss: 0.659102, acc.: 58.20%] [G loss: 0.749005]
5000 [D loss: 0.643681, acc.: 65.23%] [G loss: 0.764280]
6000 [D loss: 0.631345, acc.: 64.06%] [G loss: 0.766279]
7000 [D loss: 0.621716, acc.: 67.58%] [G loss: 0.789148]
8000 [D loss: 0.628255, acc.: 63.67%] [G loss: 0.787704]
9000 [D loss: 0.602451, acc.: 70.31%] [G loss: 0.810093]
...
```
其中,`D loss` 表示鉴别器的损失,`acc` 表示分类准确率,`G loss` 表示生成器的损失。通过观察 G loss 的变化,可以判断 GAN 网络是否成功地对 EEG 数据进行了生成。