深度学习：可视化-结果loss acc可视化及测试数据显示

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

导入库
from collections import defaultdict

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

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)

训练函数返回
return model, history

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
)

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 = []
1
2
step2 添加数据
G_losses.append(sum_loss.item())
D_losses.append(d_loss.item())
1
2
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()
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')

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原文链接：https://blog.csdn.net/zhe470719/article/details/120610934