ai论文写作导论（ai 论文生成器）

生成对抗网络（GAN）是一种深度学习模型，由两个神经网络组成：生成器和判别器。生成器试图生成与真实数据相似的数据，而判别器则试图区分生成的数据和真实数据。这两个网络相互竞争，直到生成器生成的数据足够逼真，以至于判别器无法区分真实数据和生成的数据。

下面是一个简单的GAN实现，用于生成手写数字图像： ```python import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import numpy as np # 定义生成器 def generator(z, reuse=None): with tf.variable_scope('gen', reuse=reuse): hidden1 = tf.layers.dense(inputs=z, units=128) output = tf.layers.dense(inputs=hidden2, units=784, activation=tf.nn.tanh) return output # 定义判别器 def discriminator(X, reuse=None): with tf.variable_scope('dis', reuse=reuse): hidden1 = tf.layers.dense(inputs=X, units=128) logits = tf.layers.dense(hidden2, units=1) output = tf.sigmoid(logits) return output, logits def loss_func(logits_in, labels_in): return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_in, labels=labels_in)) # 定义placeholder real_images = tf.placeholder(tf.float32, shape=[None, 784]) z = tf.placeholder(tf.float32, shape=[None, 100]) # 生成器生成的图像 G = generator(z) # 判别器判断真实图像 _output_real, D_logits_real = discriminator(real_images) # 判别器判断生成图像 D_output_fake, D_logits_fake = discriminator(G, reuse=True) D_real_loss = loss_func(D_logits_real, tf.ones_like(D_logits_real) * 0.9) D_fake_loss = loss_func(D_logits_fake, tf.zeros_like(D_logits_real)) D_loss = D_real_loss + D_fake_loss G_loss = loss_func(D_logits_fake, tf.ones_like(D_logits_fake)) # 定义优化器 learning_rate = 0.001 tvars = tf.trainable_variables() d_vars = [var for var in tvars if 'dis' in var.name] g_vars = [var for var in tvars if 'gen' in var.name] D_trainer = tf.train.AdamOptimizer(learning_rate).minimize(D_loss, var_list=d_vars) G_trainer = tf.train.AdamOptimizer(learning_rate).minimize(G_loss, var_list=g_vars) # 加载MNIST数据集 mnist = input_data.read_data_sets("MNIST_data/") # 定义训练参数 batch_size = 100 epochs = 100 init = tf.global_variables_initializer() # 开始训练 samples = [] with tf.Session() as sess: sess.run(init) 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_trainer, feed_dict={real_images: batch_images, z: batch_z}) _ = sess.run(G_trainer, feed_dict={z: batch_z}) # 每个epoch结束后，输出损失函数和生成的图像 print("Epoch:", epoch, "D loss:", sess.run(D_loss, feed_dict={real_images: batch_images, z: batch_z}), "G loss:", sess.run(G_loss, feed_dict={z: batch_z})) sample_z = np.random.uniform(-1, 1, size=(1, 100)) gen_sample = sess.run(generator(z, reuse=True), feed_dict={z: sample_z}) samples.append(gen_sample) # 输出生成的图像 import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=10, ncols=10, sharex=True, sharey=True, figsize=(20, 20)) for i in range(10): for j in range(10): axes[i, j].imshow(samples[(i * 10) + j][0].reshape((28, 28)), cmap='Greys_r') 本文地址：http://syank.xrbh.cn/quote/6369.html    迅博思语资讯 http://syank.xrbh.cn/ , 查看更多