1、结构图
2、知识点
生成器(G)：将噪音数据生成一个想要的数据
判别器(D)：将生成器的结果进行判别， 3、代码及案例
# coding: utf-8 # ## 对抗生成网络案例 ## # # # <img src="jpg/3.png" alt="FAO" width="590" > # - 判别器 : 火眼金睛，分辨出生成和真实的 <br /> # <br /> # - 生成器 : 瞒天过海，骗过判别器 <br /> # <br /> # - 损失函数定义 : 一方面要让判别器分辨能力更强，另一方面要让生成器更真 <br /> # <br /> # # <img src="jpg/1.jpg" alt="FAO" width="590" > # In[1]: import tensorflow as tf import numpy as np import pickle import matplotlib.pyplot as plt get_ipython().run_line_magic('matplotlib', 'inline') # # 导入数据 # In[2]: from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('/data') # ## 网络架构 # # ### 输入层 ：待生成图像（噪音）和真实数据 # # ### 生成网络：将噪音图像进行生成 # # ### 判别网络： # - （1）判断真实图像输出结果 # - （2）判断生成图像输出结果 # # ### 目标函数： # - （1）对于生成网络要使得生成结果通过判别网络为真 # - （2）对于判别网络要使得输入为真实图像时判别为真 输入为生成图像时判别为假 # # <img src="jpg/2.png" alt="FAO" width="590" > # ## Inputs # In[3]: #真实数据和噪音数据 def get_inputs(real_size, noise_size): real_img = tf.placeholder(tf.float32, [None, real_size]) noise_img = tf.placeholder(tf.float32, [None, noise_size]) return real_img, noise_img # ## 生成器 # * noise_img: 产生的噪音输入 # * n_units: 隐层单元个数 # * out_dim: 输出的大小（28 * 28 * 1） # In[4]: def get_generator(noise_img, n_units, out_dim, reuse=False, alpha=0.01): with tf.variable_scope("generator", reuse=reuse): # hidden layer hidden1 = tf.layers.dense(noise_img, n_units) # leaky ReLU hidden1 = tf.maximum(alpha * hidden1, hidden1) # dropout hidden1 = tf.layers.dropout(hidden1, rate=0.2) # logits & outputs logits = tf.layers.dense(hidden1, out_dim) outputs = tf.tanh(logits) return logits, outputs # ## 判别器 # * img：输入 # * n_units：隐层单元数量 # * reuse：由于要使用两次 # In[5]: def get_discriminator(img, n_units, reuse=False, alpha=0.01): with tf.variable_scope("discriminator", reuse=reuse): # hidden layer hidden1 = tf.layers.dense(img, n_units) hidden1 = tf.maximum(alpha * hidden1, hidden1) # logits & outputs logits = tf.layers.dense(hidden1, 1) outputs = tf.sigmoid(logits) return logits, outputs # ## 网络参数定义 # * img_size：输入大小 # * noise_size：噪音图像大小 # * g_units：生成器隐层参数 # * d_units：判别器隐层参数 # * learning_rate：学习率 # In[6]: img_size = mnist.train.images[0].shape[0] noise_size = 100 g_units = 128 d_units = 128 learning_rate = 0.001 alpha = 0.01 # ## 构建网络 # In[7]: tf.reset_default_graph() real_img, noise_img = get_inputs(img_size, noise_size) # generator g_logits, g_outputs = get_generator(noise_img, g_units, img_size) # discriminator d_logits_real, d_outputs_real = get_discriminator(real_img, d_units) d_logits_fake, d_outputs_fake = get_discriminator(g_outputs, d_units, reuse=True) # ### 目标函数： # - （1）对于生成网络要使得生成结果通过判别网络为真 # - （2）对于判别网络要使得输入为真实图像时判别为真 输入为生成图像时判别为假 # # <img src="jpg/2.png" alt="FAO" width="590" > # In[8]: # discriminator的loss # 识别真实图片 d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real))) # 识别生成的图片 d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake))) # 总体loss d_loss = tf.add(d_loss_real, d_loss_fake) # generator的loss g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake))) # ## 优化器 # In[9]: train_vars = tf.trainable_variables() # generator g_vars = [var for var in train_vars if var.name.startswith("generator")] # discriminator d_vars = [var for var in train_vars if var.name.startswith("discriminator")] # optimizer 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 # In[10]: # batch_size batch_size = 64 # 训练迭代轮数 epochs = 300 # 抽取样本数 n_sample = 25 # 存储测试样例 samples = [] # 存储loss losses = [] # 保存生成器变量 saver = tf.train.Saver(var_list = g_vars) # 开始训练 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for e in range(epochs): for batch_i in range(mnist.train.num_examples//batch_size): batch = mnist.train.next_batch(batch_size) batch_images = batch[0].reshape((batch_size, 784)) # 对图像像素进行scale，这是因为tanh输出的结果介于(-1,1),real和fake图片共享discriminator的参数 batch_images = batch_images*2 - 1 # generator的输入噪声 batch_noise = np.random.uniform(-1, 1, size=(batch_size, noise_size)) # Run optimizers _ = sess.run(d_train_opt, feed_dict={real_img: batch_images, noise_img: batch_noise}) _ = sess.run(g_train_opt, feed_dict={noise_img: batch_noise}) # 每一轮结束计算loss train_loss_d = sess.run(d_loss, feed_dict = {real_img: batch_images, noise_img: batch_noise}) # real img loss train_loss_d_real = sess.run(d_loss_real, feed_dict = {real_img: batch_images, noise_img: batch_noise}) # fake img loss train_loss_d_fake = sess.run(d_loss_fake, feed_dict = {real_img: batch_images, noise_img: batch_noise}) # generator loss train_loss_g = sess.run(g_loss, feed_dict = {noise_img: batch_noise}) print("Epoch {}/{}...".format(e+1, epochs), "判别器损失: {:.4f}(判别真实的: {:.4f} + 判别生成的: {:.4f})...".format(train_loss_d, train_loss_d_real, train_loss_d_fake), "生成器损失: {:.4f}".format(train_loss_g)) losses.append((train_loss_d, train_loss_d_real, train_loss_d_fake, train_loss_g)) # 保存样本 sample_noise = np.random.uniform(-1, 1, size=(n_sample, noise_size)) gen_samples = sess.run(get_generator(noise_img, g_units, img_size, reuse=True), feed_dict={noise_img: sample_noise}) samples.append(gen_samples) saver.save(sess, './checkpoints/generator.ckpt') # 保存到本地 with open('train_samples.pkl', 'wb') as f: pickle.dump(samples, f) # # loss迭代曲线 # In[11]: fig, ax = plt.subplots(figsize=(20,7)) losses = np.array(losses) plt.plot(losses.T[0], label='判别器总损失') plt.plot(losses.T[1], label='判别真实损失') plt.plot(losses.T[2], label='判别生成损失') plt.plot(losses.T[3], label='生成器损失') plt.title("对抗生成网络") ax.set_xlabel('epoch') plt.legend() # # 生成结果 # In[12]: # Load samples from generator taken while training with open('train_samples.pkl', 'rb') as f: samples = pickle.load(f) # In[13]: #samples是保存的结果 epoch是第多少次迭代 def view_samples(epoch, samples): fig, axes = plt.subplots(figsize=(7,7), nrows=5, ncols=5, sharey=True, sharex=True) for ax, img in zip(axes.flatten(), samples[epoch][1]): # 这里samples[epoch][1]代表生成的图像结果，而[0]代表对应的logits ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) im = ax.imshow(img.reshape((28,28)), cmap='Greys_r') return fig, axes # In[14]: _ = view_samples(-1, samples) # 显示最终的生成结果 # # 显示整个生成过程图片 # In[15]: # 指定要查看的轮次 epoch_idx = [10, 30, 60, 90, 120, 150, 180, 210, 240, 290] show_imgs = [] for i in epoch_idx: show_imgs.append(samples[i][1]) # In[16]: # 指定图片形状 rows, cols = 10, 25 fig, axes = plt.subplots(figsize=(30,12), nrows=rows, ncols=cols, sharex=True, sharey=True) idx = range(0, epochs, int(epochs/rows)) for sample, ax_row in zip(show_imgs, axes): for img, ax in zip(sample[::int(len(sample)/cols)], ax_row): ax.imshow(img.reshape((28,28)), cmap='Greys_r') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) # # 生成新的图片 # In[17]: # 加载我们的生成器变量 saver = tf.train.Saver(var_list=g_vars) with tf.Session() as sess: saver.restore(sess, tf.train.latest_checkpoint('checkpoints')) sample_noise = np.random.uniform(-1, 1, size=(25, noise_size)) gen_samples = sess.run(get_generator(noise_img, g_units, img_size, reuse=True), feed_dict={noise_img: sample_noise}) # In[18]: _ = view_samples(0, [gen_samples])
4、优化目标
