gm模型python

如果您要使用 Python 实现 GAN 模型，可以使用 TensorFlow 或 PyTorch 等深度学习框架。以下是一个使用 TensorFlow 实现 GAN 模型的简单示例代码：

import tensorflow as tf
import numpy as np

# 定义生成器网络
def generator(z, output_dim, n_units=128, reuse=False, alpha=0.01):
    with tf.variable_scope('generator', reuse=reuse):
        # 隐藏层
        h1 = tf.layers.dense(z, n_units, activation=None)
        # Leaky ReLU 激活函数
        h1 = tf.maximum(alpha * h1, h1)
        # 输出层
        logits = tf.layers.dense(h1, output_dim, activation=None)
        out = tf.tanh(logits)
        
        return out

# 定义判别器网络
def discriminator(x, n_units=128, reuse=False, alpha=0.01):
    with tf.variable_scope('discriminator', reuse=reuse):
        # 隐藏层
        h1 = tf.layers.dense(x, n_units, activation=None)
        # Leaky ReLU 激活函数
        h1 = tf.maximum(alpha * h1, h1)
        # 输出层
        logits = tf.layers.dense(h1, 1, activation=None)
        out = tf.sigmoid(logits)
        
        return out, logits

# 定义输入占位符
input_real = tf.placeholder(tf.float32, shape=[None, 100], name='input_real')
input_z = tf.placeholder(tf.float32, shape=[None, 100], name='input_z')

# 生成器和判别器
g_model = generator(input_z, 1)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

# 定义损失函数
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

# 定义优化器
learning_rate = 0.001
tvars = tf.trainable_variables()
d_vars = [var for var in tvars if 'discriminator' in var.name]
g_vars = [var for var in tvars if 'generator' in var.name]
d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)

# 训练模型
batch_size = 64
epochs = 300
samples = []
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(epochs):
        for i in range(mnist.train.num_examples // batch_size):
            batch = mnist.train.next_batch(batch_size)
            batch_images = batch[0].reshape((batch_size, 784))
            batch_images = batch_images * 2 - 1
            batch_z = np.random.uniform(-1, 1, size=(batch_size, 100))
            _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
            _ = sess.run(g_train_opt, feed_dict={input_z: batch_z})
            
        # 每 10 个 epoch 保存一次生成样本
        if epoch % 10 == 0:
            sample_z = np.random.uniform(-1, 1, size=(16, 100))
            gen_samples = sess.run(generator(input_z, 1, reuse=True), feed_dict={input_z: sample_z})
            samples.append(gen_samples)

这个示例代码使用 MNIST 数据集进行训练，并将生成的样本保存在 `samples` 列表中。您可以根据您的需求修改此示例代码，并使用您自己的数据集进行训练。