给我设计一个ai图片生成的代码

以下是一个基于Python的简单AI图片生成代码示例：

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

# 加载MNIST手写数字数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 数据预处理
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# 构建模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1, validation_data=(x_test, y_test))

# 使用模型生成图片
random_image = np.random.rand(1, 28, 28, 1)
generated_image = model.predict(random_image)

# 显示生成的图片
plt.imshow(generated_image[0].reshape(28, 28), cmap='gray')
plt.axis('off')
plt.show()

这个代码使用了卷积神经网络（CNN）对MNIST手写数字数据集进行训练，并用训练好的模型生成一张随机图片。代码中的模型结构可以根据需要进行调整和优化。请确保已经安装了相应的Python库（例如TensorFlow和Matplotlib）并且已经下载了MNIST数据集