1.可视化train，test的loss acc

1.1 案例：交通指示牌识别案例-history数组

代码地址：E:\项目例程\猫狗分类\迁移学习\猫狗_resnet18_2 \猫狗分类_迁移学习可视化

1. 导入库

 from collections import defaultdict

2. 训练函数中构建一个默认value为list的字典

history = defaultdict(list)  # 构建一个默认value为list的字典

3. 训练函数中保存train_loss,train_acc,test_loss,test_acc结果

history['train_acc'].append(train_accuracy)
        history['train_loss'].append(train_loss)
        history['val_acc'].append(val_accuracy)
        history['val_loss'].append(val_loss)

4. 训练函数返回

return model, history

5. 训练模型调用时

# 调用训练函数训练
model_conv, history = train_model(
    model_conv,
    criterion,
    optimizer_conv,
    exp_lr_scheduler,
    num_epochs=30
)

6. 绘制函数 两张图，每个图两个曲线，写法固定
   注意：若运行不出图，则加plt.show()

# 绘制 loss, acc  写法固定：两张表
def plot_training_history(history):
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 6))
    ax1.plot(history['train_loss'], label='train loss')
    ax1.plot(history['val_loss'], label='val loss')

    ax1.set_ylim([-0.05, 1.05])
    ax1.legend()
    ax1.set_ylabel('Loss')
    ax1.set_xlabel('Epoch')

    ax2.plot(history['train_acc'], label='train acc')
    ax2.plot(history['val_acc'], label='val acc')

    ax2.set_ylim([-0.05, 1.05])
    ax2.legend()
    ax2.set_ylabel('Accuracy')
    ax2.set_xlabel('Epoch')

    fig.suptitle('Training History')
    plt.show()

plot_training_history(history)

结果曲线展示：

1.2 一张图两条曲线loss 写法总结

案例：gan-手写数字 画G_loss，D_loss
代码位置 ：E:\项目例程\GNN\手写数字\3_可视化
步骤：

step1 定义数组

G_losses = []
D_losses = []

step2 添加数据

G_losses.append(sum_loss.item())
D_losses.append(d_loss.item())

step3 画图

x=[i for i in range(len(G_losses))]
figure = plt.figure(figsize=(20, 8), dpi=80)
plt.plot(x,G_losses,label='G_losses')
plt.plot(x,D_losses,label='D_losses')
plt.xlabel("iterations",fontsize=15)
plt.ylabel("loss",fontsize=15)
plt.legend()
plt.grid()
plt.show()

效果

2.可视化测试结果

代码地址：E:\项目例程\猫狗分类\迁移学习\猫狗_resnet18_2 \猫狗分类_迁移学习可视化

1. 定义结果可视化函数
   注意：plt.subplot(4,4,i+1) 应根据batch_size修改，本案例batch_size=16.故为4x4

# 测试结果可视化函数
def visualize_model(model):
    model.eval()
    with torch.no_grad():
        inputs, labels = next(iter(dataloaders['val']))
        inputs = inputs.to(device)
        labels = labels.to(device)

        outputs = model(inputs)
        preds = outputs.argmax(1)

        plt.figure(figsize=(9, 9))
        for i in range(inputs.size(0)):
            plt.subplot(4,4,i+1)  #根据batch_size修改
            plt.axis('off')
            plt.title(f'pred: {class_names[preds[i]]}|true: {class_names[labels[i]]}')
            im = no_normalize(inputs[i].cpu())
            plt.imshow(im)
        plt.savefig('train.jpg')
        plt.show()

2. 调用函数

# 测试结果可视化
visualize_model(model_conv)

效果展示：

3 loss曲线生成及保存

代码参考：E:\项目例程\GNN\DCGAN\02DCGAN_oxford17
DCGAN生成鲜花

figure_save_path="./figures/"
plt.figure(1,figsize=(8, 4))
        plt.title("Generator and Discriminator Loss During Training")
        plt.plot(self.G_loss_list[::10], label="G")
        plt.plot(self.D_loss_list[::10], label="D")
        plt.xlabel("iterations")
        plt.ylabel("Loss")
        plt.axhline(y=0, label="0", c="g")  # asymptote
        plt.legend()
        plt.savefig(self.figure_save_path + str(num_epochs) + 'epochs_' + 'loss.jpg', bbox_inches='tight')

4GAN -真实图片与生成图片

代码参考：E:\项目例程\GNN\DCGAN\02DCGAN_oxford17

（1）真实图片与生成图片对比

plt.figure(4,figsize=(8, 4))
        # Plot the real images
        plt.subplot(1, 2, 1)
        plt.axis("off")
        plt.title("Real Images")
        real = next(iter(self.dataloader))  # real[0]image,real[1]label
        plt.imshow(utils.make_grid(real[0][:self.num_showimage] * 0.5 + 0.5, nrow=10).permute(1, 2, 0))

        # Load the Best Generative Model
        # self.G.load_state_dict(
        #     torch.load(self.model_save_path + 'disc_{}.pth'.format(epoch), map_location=torch.device(self.device)))
        self.G.eval()
        # Generate the Fake Images
        with torch.no_grad():
            fake = self.G(self.fixed_noise).cpu()
        # Plot the fake images
        plt.subplot(1, 2, 2)
        plt.axis("off")
        plt.title("Fake Images")
        fake = utils.make_grid(fake[:self.num_showimage] * 0.5 + 0.5, nrow=10)
        plt.imshow(fake.permute(1, 2, 0))

        # Save the comparation result
        plt.savefig(self.figure_save_path + str(num_epochs) + 'epochs_' + 'result.jpg', bbox_inches='tight')
        plt.show()

（2）生成图片的gif

import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure(3,figsize=(5, 5))
        plt.axis("off")
        ims = [[plt.imshow(item.permute(1, 2, 0), animated=True)] for item in self.img_list]
        ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)
        #HTML(ani.to_jshtml())
        # ani.to_html5_video()
        ani.save(self.figure_save_path + str(num_epochs) + 'epochs_' + 'generation.gif')