Python实现的一个简单的GAN（生成对抗网络）例子

一个简单的GAN（生成对抗网络）例子

以下是使用Python实现的一个简单的GAN（生成对抗网络）例子，它可以生成手写数字图像

```python
# Importing libraries
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout
from tensorflow.keras.layers import BatchNormalization, Activation, ZeroPadding2D
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam# Loading the dataset
(X_train, _), (_, _) = mnist.load_data()# Rescaling the images to [-1, 1] range
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)# Defining the generator network
generator = Sequential()
generator.add(Dense(256, input_dim=100))
generator.add(LeakyReLU(alpha=0.2))
generator.add(BatchNormalization(momentum=0.8))
generator.add(Dense(512))
generator.add(LeakyReLU(alpha=0.2))
generator.add(BatchNormalization(momentum=0.8))
generator.add(Dense(1024))
generator.add(LeakyReLU(alpha=0.2))
generator.add(BatchNormalization(momentum=0.8))
generator.add(Dense(28 * 28 * 1, activation='tanh'))
generator.add(Reshape((28, 28, 1)))# Defining the discriminator network
discriminator = Sequential()
discriminator.add(Flatten(input_shape=(28, 28, 1)))
discriminator.add(Dense(512))
discriminator.add(LeakyReLU(alpha=0.2))
discriminator.add(Dense(256))
discriminator.add(LeakyReLU(alpha=0.2))
discriminator.add(Dense(1, activation='sigmoid'))# Compiling the generator
generator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))# Compiling the discriminator
discriminator.compile(loss='binary_crossentropy',optimizer=Adam(0.0002, 0.5),metrics=['accuracy'])# Combining the generator and discriminator
z = Input(shape=(100,))
img = generator(z)
discriminator.trainable = False
valid = discriminator(img)
combined = Model(z, valid)
combined.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))# Training the GAN
epochs = 10000
batch_size = 128
sample_interval = 1000for epoch in range(epochs):# Training the discriminatoridx = np.random.randint(0, X_train.shape[0], batch_size)real_imgs = X_train[idx]noise = np.random.normal(0, 1, (batch_size, 100))fake_imgs = generator.predict(noise)d_loss_real = discriminator.train_on_batch(real_imgs, np.ones((batch_size, 1)))d_loss_fake = discriminator.train_on_batch(fake_imgs, np.zeros((batch_size, 1)))d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)# Training the generatornoise = np.random.normal(0, 1, (batch_size, 100))valid_y = np.ones((batch_size, 1))g_loss = combined.train_on_batch(noise, valid_y)# Printing the progressif epoch % sample_interval == 0:print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))# Saving generated imagesr, c = 5, 5noise = np.random.normal(0, 1, (r * c, 100))gen_imgs = generator.predict(noise)gen_imgs = 0.5 * gen_imgs + 0.5fig, axs = plt.subplots(r, c)cnt = 0for i in range(r):for j in range(c):axs[i, j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')axs[i, j].axis('off')cnt += 1fig.savefig("generated_images/%d.png" % epoch)plt.close()
```

在训练过程中，GAN会不断生成假图像，并将它们与真实图像一起喂给鉴别器（即图像分类器）。鉴别器会尝试将真实图像与假图像分开，同时生成器会尝试产生更逼真的图像。训练完成后，我们可以使用生成器来生成新的手写数字图像。

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