写一段用GAN生成表格数据的代码
时间: 2024-05-13 16:15:52 浏览: 14
以下是一个简单的示例代码,用GAN生成具有两列的表格数据:
```python
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout, Input, LeakyReLU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
# 定义生成器模型
def create_generator():
generator_input = Input(shape=(100,))
x = Dense(256, activation=LeakyReLU(0.2))(generator_input)
x = Dropout(0.3)(x)
x = Dense(512, activation=LeakyReLU(0.2))(x)
x = Dropout(0.3)(x)
x = Dense(1024, activation=LeakyReLU(0.2))(x)
x = Dropout(0.3)(x)
generator_output = Dense(2, activation='tanh')(x)
generator = Model(generator_input, generator_output)
return generator
# 定义判别器模型
def create_discriminator():
discriminator_input = Input(shape=(2,))
x = Dense(512, activation=LeakyReLU(0.2))(discriminator_input)
x = Dropout(0.3)(x)
x = Dense(256, activation=LeakyReLU(0.2))(x)
x = Dropout(0.3)(x)
x = Dense(128, activation=LeakyReLU(0.2))(x)
x = Dropout(0.3)(x)
discriminator_output = Dense(1, activation='sigmoid')(x)
discriminator = Model(discriminator_input, discriminator_output)
discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
return discriminator
# 定义GAN模型
def create_gan(generator, discriminator):
discriminator.trainable = False
gan_input = Input(shape=(100,))
gan_output = discriminator(generator(gan_input))
gan = Model(gan_input, gan_output)
gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
return gan
# 加载数据(这里不需要)
# data = pd.read_csv('data.csv')
# 定义训练参数
epochs = 10000
batch_size = 32
# 初始化模型
generator = create_generator()
discriminator = create_discriminator()
gan = create_gan(generator, discriminator)
# 训练模型
for epoch in range(epochs):
# 随机生成噪声
noise = np.random.normal(0, 1, size=(batch_size, 100))
# 生成伪造数据
fake_data = generator.predict(noise)
# 加载真实数据(这里不需要)
# real_data = data.sample(batch_size)
# 训练判别器
discriminator_loss_real = discriminator.train_on_batch(real_data.values, np.ones((batch_size, 1)))
discriminator_loss_fake = discriminator.train_on_batch(fake_data, np.zeros((batch_size, 1)))
discriminator_loss = 0.5 * np.add(discriminator_loss_real, discriminator_loss_fake)
# 训练生成器
generator_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))
# 打印损失
if epoch % 100 == 0:
print(f'Epoch: {epoch}, Discriminator Loss: {discriminator_loss}, Generator Loss: {generator_loss}')
# 生成表格数据
noise = np.random.normal(0, 1, size=(1000, 100))
fake_data = generator.predict(noise)
table_data = pd.DataFrame(fake_data, columns=['Column 1', 'Column 2'])
print(table_data.head())
```
注:此代码仅为示例,可能需要根据具体数据集和需求进行调整。
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