import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torchvision
 
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
device

train_ds = torchvision.datasets.MNIST('data', 
                                      train=True, 
                                      transform=torchvision.transforms.ToTensor(), # 將數(shù)據(jù)類型轉(zhuǎn)化為Tensor
                                      download=True)
 
test_ds  = torchvision.datasets.MNIST('data', 
                                      train=False, 
                                      transform=torchvision.transforms.ToTensor(), # 將數(shù)據(jù)類型轉(zhuǎn)化為Tensor
                                      download=True)

batch_size = 32

 
train_dl = torch.utils.data.DataLoader(train_ds, 
                                       batch_size=batch_size, 
                                       shuffle=True)
 
test_dl  = torch.utils.data.DataLoader(test_ds, 
                                       batch_size=batch_size)

import numpy as np
 
 # 指定圖片大小，圖像大小為20寬、5高的繪圖(單位為英寸inch)
plt.figure(figsize=(20, 5)) 
for i, imgs in enumerate(imgs[:20]):
    # 維度縮減
    npimg = imgs.numpy().transpose((1, 2, 0))
    # 將整個figure分成2行10列，繪制第i+1個子圖。
    plt.subplot(2, 10, i+1)
    plt.imshow(npimg, cmap=plt.cm.binary)
    plt.axis('off')

# 取一個批次查看數(shù)據(jù)格式
# 數(shù)據(jù)的shape為：[batch_size, channel, height, weight]
# 其中batch_size為自己設(shè)定，channel，height和weight分別是圖片的通道數(shù)，高度和寬度。
imgs, labels = next(iter(train_dl))
imgs.shape

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential,ReLU
 
num_classes = 10 
 
class Model(nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        # 卷積層
        self.layers = Sequential(
            # 第一層
            Conv2d(3, 64, kernel_size=3),
            MaxPool2d(2),
            ReLU(),
            # 第二層
            Conv2d(64, 64, kernel_size=3),
            MaxPool2d(2),
            ReLU(),
            Conv2d(64, 128, kernel_size=3),
            MaxPool2d(2),
            ReLU(),
            Flatten(),
            Linear(512, 256,bias=True),
            ReLU(),
            Linear(256, 64,bias=True),
            ReLU(),
            Linear(64, num_classes,bias=True)
        )
    def forward(self, x):
        x = self.layers(x)
        return x

from torch import nn
 
vgg16=torchvision.models.vgg16(pretrained=True)#經(jīng)過訓(xùn)練的
class Model(nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        # 卷積層
        self.layers = Sequential(
            vgg16
        )
    def forward(self, x):
        x = self.layers(x)
        return x

loss_fn    = nn.CrossEntropyLoss() # 創(chuàng)建損失函數(shù)
learn_rate = 1e-2 # 學(xué)習(xí)率
opt        = torch.optim.SGD(model.parameters(),lr=learn_rate)

# 訓(xùn)練循環(huán)
def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)  # 訓(xùn)練集的大小，一共60000張圖片
    num_batches = len(dataloader)   # 批次數(shù)目，1875（60000/32）
 
    train_loss, train_acc = 0, 0  # 初始化訓(xùn)練損失和正確率

    for X, y in dataloader:  # 獲取圖片及其標(biāo)簽
        X, y = X.to(device), y.to(device)

        # 計算預(yù)測誤差
        pred = model(X)          # 網(wǎng)絡(luò)輸出
        loss = loss_fn(pred, y)  # 計算網(wǎng)絡(luò)輸出和真實值之間的差距，targets為真實值，計算二者差值即為損失

        # 反向傳播
        optimizer.zero_grad()  # grad屬性歸零
        loss.backward()        # 反向傳播
        optimizer.step()       # 每一步自動更新

        # 記錄acc與loss
        train_acc  += (pred.argmax(1) == y).type(torch.float).sum().item()
        train_loss += loss.item()

    train_acc  /= size
    train_loss /= num_batches
 
    return train_acc, train_loss

def test (dataloader, model, loss_fn):
    size        = len(dataloader.dataset)  # 測試集的大小，一共10000張圖片
    num_batches = len(dataloader)          # 批次數(shù)目，313（10000/32=312.5，向上取整）
    test_loss, test_acc = 0, 0

    # 當(dāng)不進(jìn)行訓(xùn)練時，停止梯度更新，節(jié)省計算內(nèi)存消耗
    with torch.no_grad():
        for imgs, target in dataloader:
            imgs, target = imgs.to(device), target.to(device)

            # 計算loss
            target_pred = model(imgs)
            loss        = loss_fn(target_pred, target)

            test_loss += loss.item()
            test_acc  += (target_pred.argmax(1) == target).type(torch.float).sum().item()
 
    test_acc  /= size
    test_loss /= num_batches
 
    return test_acc, test_loss

epochs     = 20
train_loss = []
train_acc  = []
test_loss  = []
test_acc   = []
 
for epoch in range(epochs):
    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)

    model.eval()
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)

    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)

    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')
    print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')