利用python3.8语言基生成对抗网络算法在MNISTS手写数字识别，并且 选取一定数目的样本分别作为训练和测试数据，固定一组参数设置，实现对MNIST手写数字有效识别，并基于网络训练过程中的可视化图分析识别性能随迭代次数变化情况， 尝试修改训练/测试样本（数目）、隐藏层神经元节点数目、隐藏层数目、学习率等参数，获得不同场景下的识别精度

您可以使用Python 3.8和相关的库（如TensorFlow、Keras）来实现生成对抗网络（GAN）算法在MNIST手写数字识别上的任务。以下是一种可能的实现方法：

1. 导入所需的库：

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

2. 定义生成器和判别器模型：

def build_generator():
    generator = Sequential()
    generator.add(Dense(256, input_dim=100))
    generator.add(LeakyReLU(alpha=0.2))
    generator.add(Dense(512))
    generator.add(LeakyReLU(alpha=0.2))
    generator.add(Dense(784, activation='tanh'))
    generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return generator


def build_discriminator():
    discriminator = Sequential()
    discriminator.add(Dense(512, input_dim=784))
    discriminator.add(LeakyReLU(alpha=0.2))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(256))
    discriminator.add(LeakyReLU(alpha=0.2))
    discriminator.add(Dropout(0.3))
    discriminator.add(Dense(1, activation='sigmoid'))
    discriminator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return discriminator

3. 定义GAN模型：

def build_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

4. 进行模型训练：

def train_gan(generator, discriminator, gan, epochs, batch_size, sample_interval):

    # 加载MNIST数据集
    (X_train, _), (_, _) = mnist.load_data()

    # 数据归一化
    X_train = X_train / 127.5 - 1.
    X_train = np.expand_dims(X_train, axis=3)

    # 创建真实的标签和假的标签
    real = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):

        # 训练判别器
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        real_images = X_train[idx]
        noise = np.random.normal(0, 1, (batch_size, 100))
        fake_images = generator.predict(noise)

        discriminator_loss_real = discriminator.train_on_batch(real_images, real)
        discriminator_loss_fake = discriminator.train_on_batch(fake_images, fake)
        discriminator_loss = 0.5 * np.add(discriminator_loss_real, discriminator_loss_fake)

        # 训练生成器
        noise = np.random.normal(0, 1, (batch_size, 100))
        generator_loss = gan.train_on_batch(noise, real)

        # 每隔一定的间隔保存和打印结果
        if epoch % sample_interval == 0:
            print(f"Epoch {epoch}, 判别器损失: {discriminator_loss}, 生成器损失: {generator_loss}")

            # 生成并保存一些生成的手写数字图像
            sample_images(generator, epoch)

def sample_images(generator, epoch):
    r, c = 5, 5
    noise = np.random.normal(0, 1, (r * c, 100))
    gen_images = generator.predict(noise)
    gen_images = 0.5 * gen_images + 0.5
    fig, axs = plt.subplots(r, c)
    cnt = 0
    for i in range(r):
        for j in range(c):
            axs[i, j].imshow(gen_images[cnt, :, :, 0], cmap='gray')
            axs[i, j].axis('off')
            cnt += 1
    fig.savefig("images/mnist_%d.png" % epoch)
    plt.close()

5. 调用函数进行训练：

epochs = 30000
batch_size = 128
sample_interval = 1000

generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)

train_gan(generator, discriminator, gan, epochs, batch_size, sample_interval)

通过修改训练/测试样本数目、隐藏层神经元节点数目、隐藏层数目、学习率等参数，您可以获得不同场景下的识别精度。在GAN中，训练过程中没有明确的性能评估指标，而是通过生成的样本进行可视化和人工评估。您可以根据需要进行进一步的调整和改进。请注意，这只是一种实现方式，您可以根据需要进行自定义和改进。