我就廢話不多說了，直接上代碼吧！
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# -*- coding: utf-8 -*-"""Created on Sat Oct 13 10:22:45 2018@author: www""" import torchfrom torch import nnfrom torch.autograd import Variable import torchvision.transforms as tfsfrom torch.utils.data import DataLoader, samplerfrom torchvision.datasets import MNIST import numpy as np import matplotlib.pyplot as pltimport matplotlib.gridspec as gridspec plt.rcParams['figure.figsize'] = (10.0, 8.0) # 設置畫圖的尺寸plt.rcParams['image.interpolation'] = 'nearest'plt.rcParams['image.cmap'] = 'gray' def show_images(images): # 定義畫圖工具 images = np.reshape(images, [images.shape[0], -1]) sqrtn = int(np.ceil(np.sqrt(images.shape[0]))) sqrtimg = int(np.ceil(np.sqrt(images.shape[1]))) fig = plt.figure(figsize=(sqrtn, sqrtn)) gs = gridspec.GridSpec(sqrtn, sqrtn) gs.update(wspace=0.05, hspace=0.05) for i, img in enumerate(images): ax = plt.subplot(gs[i]) plt.axis('off') ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_aspect('equal') plt.imshow(img.reshape([sqrtimg,sqrtimg])) return def preprocess_img(x): x = tfs.ToTensor()(x) return (x - 0.5) / 0.5 def deprocess_img(x): return (x + 1.0) / 2.0 class ChunkSampler(sampler.Sampler): # 定義一個取樣的函數 """Samples elements sequentially from some offset. Arguments: num_samples: # of desired datapoints start: offset where we should start selecting from """ def __init__(self, num_samples, start=0): self.num_samples = num_samples self.start = start def __iter__(self): return iter(range(self.start, self.start + self.num_samples)) def __len__(self): return self.num_samples NUM_TRAIN = 50000NUM_VAL = 5000 NOISE_DIM = 96batch_size = 128 train_set = MNIST('E:/data', train=True, transform=preprocess_img) train_data = DataLoader(train_set, batch_size=batch_size, sampler=ChunkSampler(NUM_TRAIN, 0)) val_set = MNIST('E:/data', train=True, transform=preprocess_img) val_data = DataLoader(val_set, batch_size=batch_size, sampler=ChunkSampler(NUM_VAL, NUM_TRAIN)) imgs = deprocess_img(train_data.__iter__().next()[0].view(batch_size, 784)).numpy().squeeze() # 可視化圖片效果show_images(imgs) #判別網絡def discriminator(): net = nn.Sequential( nn.Linear(784, 256), nn.LeakyReLU(0.2), nn.Linear(256, 256), nn.LeakyReLU(0.2), nn.Linear(256, 1) ) return net #生成網絡def generator(noise_dim=NOISE_DIM): net = nn.Sequential( nn.Linear(noise_dim, 1024), nn.ReLU(True), nn.Linear(1024, 1024), nn.ReLU(True), nn.Linear(1024, 784), nn.Tanh() ) return net #判別器的 loss 就是將真實數據的得分判斷為 1，假的數據的得分判斷為 0，而生成器的 loss 就是將假的數據判斷為 1 bce_loss = nn.BCEWithLogitsLoss()#交叉熵損失函數 def discriminator_loss(logits_real, logits_fake): # 判別器的 loss size = logits_real.shape[0] true_labels = Variable(torch.ones(size, 1)).float() false_labels = Variable(torch.zeros(size, 1)).float() loss = bce_loss(logits_real, true_labels) + bce_loss(logits_fake, false_labels) return loss def generator_loss(logits_fake): # 生成器的 loss size = logits_fake.shape[0] true_labels = Variable(torch.ones(size, 1)).float() loss = bce_loss(logits_fake, true_labels) return loss # 使用 adam 來進行訓練，學習率是 3e-4, beta1 是 0.5, beta2 是 0.999def get_optimizer(net): optimizer = torch.optim.Adam(net.parameters(), lr=3e-4, betas=(0.5, 0.999)) return optimizer def train_a_gan(D_net, G_net, D_optimizer, G_optimizer, discriminator_loss, generator_loss, show_every=250, noise_size=96, num_epochs=10): iter_count = 0 for epoch in range(num_epochs): for x, _ in train_data: bs = x.shape[0] # 判別網絡 real_data = Variable(x).view(bs, -1) # 真實數據 logits_real = D_net(real_data) # 判別網絡得分 sample_noise = (torch.rand(bs, noise_size) - 0.5) / 0.5 # -1 ~ 1 的均勻分布 g_fake_seed = Variable(sample_noise) fake_images = G_net(g_fake_seed) # 生成的假的數據 logits_fake = D_net(fake_images) # 判別網絡得分 d_total_error = discriminator_loss(logits_real, logits_fake) # 判別器的 loss D_optimizer.zero_grad() d_total_error.backward() D_optimizer.step() # 優化判別網絡 # 生成網絡 g_fake_seed = Variable(sample_noise) fake_images = G_net(g_fake_seed) # 生成的假的數據 gen_logits_fake = D_net(fake_images) g_error = generator_loss(gen_logits_fake) # 生成網絡的 loss G_optimizer.zero_grad() g_error.backward() G_optimizer.step() # 優化生成網絡 if (iter_count % show_every == 0): print('Iter: {}, D: {:.4}, G:{:.4}'.format(iter_count, d_total_error.item(), g_error.item())) imgs_numpy = deprocess_img(fake_images.data.cpu().numpy()) show_images(imgs_numpy[0:16]) plt.show() print() iter_count += 1 D = discriminator()G = generator() D_optim = get_optimizer(D)G_optim = get_optimizer(G) train_a_gan(D, G, D_optim, G_optim, discriminator_loss, generator_loss) |
以上這篇pytorch:實現簡單的GAN示例(MNIST數據集)就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持服務器之家。
原文鏈接：https://blog.csdn.net/xckkcxxck/article/details/83037025
