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import matplotlib.pyplot as plt

import torch

import torch.nn as nn

import numpy as np

import torchvision.utils

from torchvision import datasets, transforms

from torch.autograd import Variable

import torch.utils.data

#判断是否能用GPU，如果能就用GPU，不能就用CPU

use_gpu = torch.cuda.is_available()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

#数据转换，Pytorch的底层是tensor(张量)，所有用来训练的图像均需要转换成tensor

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])

#下载数据集

data_train = datasets.MNIST(root="./data/", transform=transform, train=True, download=True)

data_test  = datasets.MNIST(root="./data/", transform=transform, train=False)

#加载数据集，批次大小为64，shuffle表示乱序

data_loader_train = torch.utils.data.DataLoader(dataset=data_train, batch_size=64, shuffle=True)

data_loader_test = torch.utils.data.DataLoader(dataset=data_test, batch_size=64, shuffle=True)

#创建模型即网络架构

class Model(nn.Module):

    def __init__(self):

        super(Model, self).__init__()

        #创建二维卷积

        self.conv1 = nn.Sequential(

         #输入特征数量为1，输出特征数量为64，卷积核大小为3x3，步长为1，边缘填充为1，保证了卷积后的特征尺寸与原来一样

            nn.Conv2d(1, 64, kernel_size=3, stride=1, c=1),

            nn.ReLU(),

            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),

            nn.ReLU(),

            #最大池化，特征数量不变，尺寸减半[(input-kernel_size)/stride + 1]

            nn.MaxPool2d(stride=2, kernel_size=2)

        )

        #创建全连接

        self.dense = nn.Sequential(

            nn.Linear(14*14*128, 1024),

            nn.ReLU(),

            #随机丢弃部分结点，防止过拟合

            nn.Dropout(p=0.5),

            nn.Linear(1024, 10)

        )

#创建好网络结构后，建立前向传播

    def forward(self, x):

     #对数据进行卷积操作

        x = self.conv1(x)

        #改变特征形状

        x = x.c(-1, 14*14*128)

        #对特征进行全连接

        x = self.dense(x)

        return x

#类实例化

model = Model()

#指定数据训练次数

epochs = 5

#设置学习率，即梯度下降的权重，其值越大收敛越快，越小收敛越慢

learning_rate = 0.0001

#选用参数优化器，这里使用Adam

optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

#选用损失函数，这里使用交叉熵函数，来判定实际的输出与期望的输出的接近程度

criterion = nn.CrossEntropyLoss()

#判断是否使用GPU训练

if(use_gpu):

    model = model.cuda()

    loss_f = criterion.cuda()

#用for循环的方式完成数据的批次训练    

for epoch in range(epochs):

#定义并初始化训练过程的损失以及正确率

    running_loss = 0

    running_correct = 0

    for data in data_loader_train:

        x_train, y_train = data

        x_train, y_train =x_train.cuda(), y_train.cuda()

        x_train = x_train.to(device)

        y_train = y_train.to(device)

        #将预处理好的数据加载到实例化好的model模型中，进行训练得到输出

        outputs = model(x_train)

        _, pred = torch.max(outputs.data, 1)

        #每次循环中，梯度必须清零，防止梯度堆叠

        optimizer.zero_grad()

        #调用设定的损失

        loss = criterion(outputs, y_train)

        #反向传播损失

        loss.backward()

        #参数更新

        optimizer.step()

        #更新损失

        running_loss += loss.item()

        #更新正确率

        running_correct += torch.sum(pred == y_train.data)

    testing_correct = 0

    #查看每轮训练后，测试数据集中的正确率

    for data in data_loader_test:

        x_test, y_test = data

        x_test, y_test = Variable(x_test), Variable(y_test)

        x_test = x_test.to(device)

        y_test = y_test.to(device)

        outputs = model(x_test)

        _, pred = torch.max(outputs.data, 1)

        testing_correct += torch.sum(pred == y_test.data)

        print("Loss is {}, Training Accuray is {}%, Test Accurray is {}".format(running_loss/len(data_train), 100*running_correct/len(data_train), 100*testing_correct/len(data_test)))

#测试训练好的模型

#随机加载4个手写数字

data_loader_test = torch.utils.data.DataLoader(dataset=data_test, batch_size=4, shuffle=True)

#函数next相关

#函数iter相关

x_test,y_test = next(iter(data_loader_test))

inputs = Variable(x_test)

inputs = inputs.to(device)

pred = model(inputs)

#_为输出的最大值，pred为最大值的索引值

_,pred = torch.max(pred, 1)

print('Predict Label is :', [i for i in pred.data])

print('Real Label is:', [ i for i in y_test] )

img = torchvision.utils.make_grid(x_test)

img = img.numpy().transpose(1, 2, 0)

std = [0.5]

mean = [0.5]

img = img*std+mean

plt.imshow(img)

plt.show()