ai绘图代码

这里提供一个使用GAN生成图像的Python代码示例，用于生成手写数字图像：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# 定义超参数
batch_size = 100
num_steps = 20000
learning_rate = 0.0002

# 加载MNIST数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义判别器网络
def discriminator(x, reuse=False):
    with tf.variable_scope('discriminator', reuse=reuse):
        x = tf.layers.dense(x, 256, activation=tf.nn.leaky_relu)
        x = tf.layers.dense(x, 1)
        return x

# 定义生成器网络
def generator(z):
    with tf.variable_scope('generator'):
        x = tf.layers.dense(z, 256, activation=tf.nn.leaky_relu)
        x = tf.layers.dense(x, 784, activation=tf.nn.tanh)
        return x

# 定义输入变量
z = tf.placeholder(tf.float32, shape=[None, 100], name='z')
x = tf.placeholder(tf.float32, shape=[None, 784], name='x')

# 构建GAN模型
G = generator(z)
D_real = discriminator(x)
D_fake = discriminator(G, reuse=True)

# 定义损失函数
D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real, labels=tf.ones_like(D_real)))
D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, labels=tf.zeros_like(D_fake)))
D_loss = D_loss_real + D_loss_fake
G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, labels=tf.ones_like(D_fake)))

# 定义优化器
D_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(D_loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'discriminator'))
G_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(G_loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'generator'))

# 训练模型
sess = tf.Session()
sess.run(tf.global_variables_initializer())

for i in range(num_steps):
    batch_x, _ = mnist.train.next_batch(batch_size)
    batch_z = np.random.uniform(-1, 1, size=[batch_size, 100])
    
    _, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={x: batch_x, z: batch_z})
    _, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={z: batch_z})
    
    if i % 1000 == 0:
        print('Step %d: Discriminator loss = %f, Generator loss = %f' % (i, D_loss_curr, G_loss_curr))
        
# 生成手写数字图像
n = 6
canvas = np.empty((28*n, 28*n))
for i in range(n):
    for j in range(n):
        z_ = np.random.uniform(-1, 1, size=[1, 100])
        G_sample = sess.run(G, feed_dict={z: z_})
        canvas[i*28:(i+1)*28, j*28:(j+1)*28] = G_sample.reshape(28, 28)

plt.figure(figsize=(n, n))
plt.imshow(canvas, origin="upper", cmap="gray")
plt.show()

该代码使用了TensorFlow框架，使用了GAN模型生成手写数字图像。您可以根据自己的需求进行修改和调整，例如修改模型结构、优化器等。