猫狗大战
一、VGG模型迁移学习
1. 数据整理
在使用CNN处理图像时,需要进行预处理。图片将被整理成 的大小,同时还将进行归一化处理。
使用datasets处理
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format = transforms.Compose([
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
data_dir = './dogscats'
dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), vgg_format)
for x in ['train', 'valid']}
dset_sizes = {x: len(dsets[x]) for x in ['train', 'valid']}
dset_classes = dsets['train'].classes
2.创建VGG Model
使用预训练好的VGG模型
model_vgg = models.vgg16(pretrained=True)
with open('./imagenet_class_index.json') as f:
class_dict = json.load(f)
dic_imagenet = [class_dict[str(i)][1] for i in range(len(class_dict))]
inputs_try , labels_try = inputs_try.to(device), labels_try.to(device)
model_vgg = model_vgg.to(device)
outputs_try = model_vgg(inputs_try)
print(outputs_try)
print(outputs_try.shape)
'''
可以看到结果为5行,1000列的数据,每一列代表对每一种目标识别的结果。
但是我也可以观察到,结果非常奇葩,有负数,有正数,
为了将VGG网络输出的结果转化为对每一类的预测概率,我们把结果输入到 Softmax 函数
'''
m_softm = nn.Softmax(dim=1)
probs = m_softm(outputs_try)
vals_try,pred_try = torch.max(probs,dim=1)
print( 'prob sum: ', torch.sum(probs,1))
print( 'vals_try: ', vals_try)
print( 'pred_try: ', pred_try)
print([dic_imagenet[i] for i in pred_try.data])
imshow(torchvision.utils.make_grid(inputs_try.data.cpu()),
title=[dset_classes[x] for x in labels_try.data.cpu()])
3.冻结前面层的参数,只修改最后一层
为了在训练中冻结前面层的参数,
需要设置 required_grad=False。
这样,反向传播训练梯度时,前面层的权重就不会自动更新了。训练中,只会更新最后一层的参数。
print(model_vgg)
model_vgg_new = model_vgg;
for param in model_vgg_new.parameters():
param.requires_grad = False
model_vgg_new.classifier._modules['6'] = nn.Linear(4096, 2)
model_vgg_new.classifier._modules['7'] = torch.nn.LogSoftmax(dim = 1)
model_vgg_new = model_vgg_new.to(device)
print(model_vgg_new.classifier)
4.训练测试
''''''
第一步:创建损失函数和优化器
损失函数 NLLLoss() 的 输入 是一个对数概率向量和一个目标标签.
它不会为我们计算对数概率,适合最后一层是log_softmax()的网络.
''''''
criterion = nn.NLLLoss()
# 学习率
lr = 0.001
# 随机梯度下降
optimizer_vgg = torch.optim.SGD(model_vgg_new.classifier[6].parameters(),lr = lr)
'''
第二步:训练模型
'''
def train_model(model,dataloader,size,epochs=1,optimizer=None):
model.train()
for epoch in range(epochs):
running_loss = 0.0
running_corrects = 0
count = 0
for inputs,classes in dataloader:
inputs = inputs.to(device)
classes = classes.to(device)
outputs = model(inputs)
loss = criterion(outputs,classes)
optimizer = optimizer
optimizer.zero_grad()
loss.backward()
optimizer.step()
_,preds = torch.max(outputs.data,1)
# statistics
running_loss += loss.data.item()
running_corrects += torch.sum(preds == classes.data)
count += len(inputs)
print('Training: No. ', count, ' process ... total: ', size)
epoch_loss = running_loss / size
epoch_acc = running_corrects.data.item() / size
print('Loss: {:.4f} Acc: {:.4f}'.format(
epoch_loss, epoch_acc))
# 模型训练
train_model(model_vgg_new,loader_train,size=dset_sizes['train'], epochs=1,
optimizer=optimizer_vgg)
def test_model(model,dataloader,size):
model.eval()
predictions = np.zeros(size)
all_classes = np.zeros(size)
all_proba = np.zeros((size,2))
i = 0
running_loss = 0.0
running_corrects = 0
for inputs,classes in dataloader:
inputs = inputs.to(device)
classes = classes.to(device)
outputs = model(inputs)
loss = criterion(outputs,classes)
_,preds = torch.max(outputs.data,1)
# statistics
running_loss += loss.data.item()
running_corrects += torch.sum(preds == classes.data)
predictions[i:i+len(classes)] = preds.to('cpu').numpy()
all_classes[i:i+len(classes)] = classes.to('cpu').numpy()
all_proba[i:i+len(classes),:] = outputs.data.to('cpu').numpy()
i += len(classes)
print('Testing: No. ', i, ' process ... total: ', size)
epoch_loss = running_loss / size
epoch_acc = running_corrects.data.item() / size
print('Loss: {:.4f} Acc: {:.4f}'.format(
epoch_loss, epoch_acc))
return predictions, all_proba, all_classes
predictions, all_proba, all_classes = test_model(model_vgg_new,loader_valid,size=dset_sizes['valid'])
学会利用预训练好的网络处理工程问题
二、猫狗大战
代码
大部分未改动,改动如下:
训练时 记录下 ecoch_acc的最大值,最终保存下最好的模型
#测试
ndsets = datasets.ImageFolder('/content/cat_dog', vgg_format)
final = {} #结果数组
loader_test = torch.utils.data.DataLoader(ndsets, batch_size=1, shuffle=False, num_workers=0)
model_vgg_new = torch.load("/content/model_best.pth")
def test(model,dataloader,size):
model.eval() #参数固定
cnt = 0 #count
for inputs,_ in dataloader:
if cnt < size:
inputs = inputs.to(device)
outputs = model(inputs)
_,preds = torch.max(outputs.data,1) #预测值最大化
key = ndsets.imgs[cnt][0].split("/")[-1].split('.')[0] #对目录项进行分割
final[key] = preds[0]
cnt += 1
else:
break;
test(model_vgg_new,loader_test,size=2000)
# 存储csv结果
with open("/content/test.csv",'a+') as f:
for key in range(2000):
f.write("{},{}\n".format(key,final[str(key)]))
结果
直接利用了 使用catsdogs 训练好的模型,epoch=1
为了提高准确率,可以使用数据集中的数据训练,增大epoch
利用cat_dog/train 训练的结果 epoch=1