生成对抗网络python实现手写体数字识别

生成对抗网络（GAN）是一种强大的深度学习技术，可以用于许多应用，包括手写体数字识别。在这里，我将向你展示如何使用Python实现手写体数字识别GAN。

首先，需要安装必要的库，包括TensorFlow和Keras。可以使用以下命令安装它们：

pip install tensorflow
pip install keras

接下来，我们需要准备MNIST数据集。可以使用以下代码加载数据集：

from keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

现在，我们将创建两个神经网络 - 一个生成器和一个判别器。生成器将随机噪声作为输入并生成手写数字图像，而判别器将接受手写数字图像并输出它们的真假。

from keras.models import Sequential
from keras.layers import Dense, Flatten, Reshape, Dropout
from keras.layers.convolutional import Conv2DTranspose, Conv2D
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU

def create_generator():
    generator = Sequential()
    generator.add(Dense(128 * 7 * 7, input_dim=100, activation=LeakyReLU(0.2)))
    generator.add(Reshape((7, 7, 128)))
    generator.add(Conv2DTranspose(64, (3,3), strides=(2,2), padding='same', activation=LeakyReLU(0.2)))
    generator.add(BatchNormalization())
    generator.add(Conv2DTranspose(1, (3,3), strides=(2,2), padding='same', activation='tanh'))
    return generator

def create_discriminator():
    discriminator = Sequential()
    discriminator.add(Conv2D(64, (3,3), padding='same', input_shape=(28,28,1)))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.25))
    discriminator.add(Conv2D(128, (3,3), strides=(2,2), padding='same'))
    discriminator.add(LeakyReLU(0.2))
    discriminator.add(Dropout(0.25))
    discriminator.add(Flatten())
    discriminator.add(Dense(1, activation='sigmoid'))
    return discriminator

generator = create_generator()
discriminator = create_discriminator()

现在，我们将定义GAN模型。我们将训练生成器和判别器，以便它们可以通过相互竞争的方式共同学习并提高性能。

from keras.optimizers import Adam

def create_gan(generator, discriminator):
    discriminator.trainable = False
    gan = Sequential()
    gan.add(generator)
    gan.add(discriminator)
    gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return gan

gan = create_gan(generator, discriminator)

现在我们将定义训练循环：

import numpy as np
import matplotlib.pyplot as plt

def train(generator, discriminator, gan, x_train, epochs=50, batch_size=128):

    # Rescale -1 to 1
    x_train = x_train / 127.5 - 1.
    x_train = np.expand_dims(x_train, axis=3)

    real = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):

        # Train discriminator
        idx = 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, real)
        d_loss_fake = discriminator.train_on_batch(fake_imgs, fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        # Train generator
        noise = np.random.normal(0, 1, (batch_size, 100))
        g_loss = gan.train_on_batch(noise, real)

        # Plot the progress
        print("Epoch {}, Discriminator Loss: {}, Generator Loss: {}".format(epoch, d_loss, g_loss))

        # Generate some digits to check the progress
        if epoch % 10 == 0:
            noise = np.random.normal(0, 1, (25, 100))
            gen_imgs = generator.predict(noise)
            gen_imgs = 0.5 * gen_imgs + 0.5
            fig, axs = plt.subplots(5, 5)
            cnt = 0
            for i in range(5):
                for j in range(5):
                    axs[i,j].imshow(gen_imgs[cnt,:,:,0], cmap='gray')
                    axs[i,j].axis('off')
                    cnt += 1
            plt.show()

train(generator, discriminator, gan, x_train, epochs=1000, batch_size=128)

在训练之后，我们可以使用生成器来生成一些手写数字图像：

noise = np.random.normal(0, 1, (25, 100))
gen_imgs = generator.predict(noise)
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(5, 5)
cnt = 0
for i in range(5):
    for j in range(5):
        axs[i,j].imshow(gen_imgs[cnt,:,:,0], cmap='gray')
        axs[i,j].axis('off')
        cnt += 1
plt.show()

这就是用Python实现手写体数字识别GAN的方法。