pytorch:实现简单的实现简单的GAN示例示例(MNIST数据集数据集)
我就废话不多说了，直接上代码吧！我就废话不多说了，直接上代码吧！
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 13 10:22:45 2018
@author: www
"""
import torch
from torch import nn
from torch.autograd import Variable
import torchvision.transforms as tfs
from torch.utils.data import DataLoader, sampler
from torchvision.datasets import MNIST
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
plt.rcParams['figure.figsize'] = (10.0, 8.0) # 设置画图的尺寸
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
def show_images(images): # 定义画图工具
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): # 定义一个取样的函数
"""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 = 50000
NUM_VAL = 5000
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