基于keras的GAN网络手把手代码教学＋注释

该博客通过一个详细的Keras代码示例介绍了如何构建和训练生成对抗网络（GAN）。GAN是一种深度学习模型，用于从随机噪声中生成逼真的图像。博客内容包括了构建生成器和判别器的步骤，以及训练过程，最后展示了训练过程中生成的图像。

基于keras的GAN网络手把手代码教学＋注释

代码如下
视频手把手教学

代码如下

# by plus_left


from tensorflow.keras import Sequential,Model
from tensorflow.keras.layers import Dense, Reshape, Input, Flatten
from tensorflow.keras.layers import LeakyReLU, BatchNormalization
from tensorflow.keras.datasets import mnist
from tensorflow.keras.optimizers import Adam
import numpy as np
import matplotlib.pyplot as plt


class GAN():
    def __init__(self):
        self.latent_dim = 100
        self.img_rows = 28
        self.img_cols = 28
        self.channel = 1
        self.img_shape = (self.img_rows, self.img_cols, self.channel)

        self.discriminator = self.build_discriminator()  # 构建
        optimizer = Adam(0.0002, 0.5)
        self.discriminator.compile(loss='binary_crossentropy',
                                   optimizer=optimizer,
                                   metrics=['accuracy'])

        self.generator = self.build_generator()  # generator 就可以完成由noise到img的生成

        self.discriminator.trainable = False
        z = Input(shape=(self.latent_dim,))
        img = self.generator(z)
        validity = self.discriminator(img)

        self.combined = Model(z