PyTorch,tensorflow.通过学习可以快速掌握的两个机器学习库对应的内容.但是这两个库非常强大,下次换个题目还能不能自己写代码?还是需要系统的从基础来学习才行啊.
import glob, json
from PILimport Image
from tqdmimport tqdm
import numpyas np
import pandasas pd
import torch
import torch.nnas nn
import torch.optimas optim
import torchvision.modelsas models
import torchvision.transformsas transforms
from torch.utils.data.datasetimport Dataset
class SVHNDataset(Dataset):
def __init__(self, img_path, img_label, transform=None):
self.img_path,self.img_label,self.transform = img_path, img_label, transform
def __getitem__(self, index):
img = Image.open(self.img_path[index]).convert('RGB')# 读取数据
img =self.transform(img)# 做相应变换
if self.img_label:
lbl = np.array(self.img_label[index],dtype=np.int)# 制作标签
lbl =list(lbl) + (5 -len(lbl)) * [10]# 标签长度少于五的用10来填充
return img, torch.from_numpy(np.array(lbl[:5]))
else:
return img
def __len__(self):
return len(self.img_path)
# 定义模型
class SVHN_Model1(nn.Module):
def __init__(self):
super(SVHN_Model1,self).__init__()
self.cnn = models.resnet50(pretrained=True)# 加载resnet50
self.cnn.avgpool = nn.AdaptiveAvgPool2d(1)# 将平均池化改为自适应平均池化
self.cnn = nn.Sequential(*list(self.cnn.children())[:-1])# 去除最后的线性层
self.fc1,self.fc2,self.fc3 = nn.Linear(2048,11), nn.Linear(2048,11), nn.Linear(2048,11)
self.fc4,self.fc5 = nn.Linear(2048,11), nn.Linear(2048,11)
def forward(self, img):
feat =self.cnn(img)
feat = feat.view(feat.shape[0], -1)
c1,c2,c3 =self.fc1(feat),self.fc2(feat),self.fc3(feat)
c4,c5 =self.fc4(feat),self.fc5(feat)
return c1, c2, c3, c4, c5
def train(train_loader, model, criterion, optimizer):
model.train()# 切换模型为训练模式
train_loss = []
for input, targetin tqdm(train_loader):# 取出数据与对应标签
if use_cuda:# 如果是gpu版本
input, target = input.cuda(), target.cuda()
target = target.long()
c0, c1, c2, c3, c4 = model(input)# 得到预测值
loss = criterion(c0, target[:,0]) + criterion(c1, target[:,1]) + \
criterion(c2, target[:,2]) + criterion(c3, target[:,3]) + \
criterion(c4, target[:,4])# 计算loss
optimizer.zero_grad()# 梯度清零
loss.backward()# 反向传播
optimizer.step()# 参数更新
train_loss.append(loss.item())
return np.mean(train_loss)
def predict(test_loader, model):
model.eval()# 切换模型为预测模型
test_pred = []
with torch.no_grad():# 不记录模型梯度信息
for inputin tqdm(test_loader):
if use_cuda: input = input.cuda()
c0, c1, c2, c3, c4 = model(input)
if use_cuda:
output = np.concatenate([
c0.data.cpu().numpy(), c1.data.cpu().numpy(), c2.data.cpu().numpy(),# 将结果水平合并，即第一个字符索引为第一列到第十一列，
c3.data.cpu().numpy(), c4.data.cpu().numpy()],axis=1)# 第二个字符为第十二列到第二十二列，依次往下
else:
output = np.concatenate([
c0.data.numpy(), c1.data.numpy(), c2.data.numpy(),
c3.data.numpy(), c4.data.numpy()],axis=1)
test_pred.append(output)
test_pred = np.vstack(test_pred)# 将每个batch的结果垂直堆起来
return test_pred
train_path, test_path = glob.glob('../input/train/*.png'), glob.glob('../input/test_a/*.png')# 读取训练数据和测试数据
train_path.sort(); test_path.sort()
train_json = json.load(open('../input/train.json'))#读取训练集标注文件
train_label = [train_json[x]['label']for xin train_json]# 拿出训练集的标签
trans_fun = transforms.Compose([
transforms.Resize((64,128)),# 将图片裁剪为64*128
transforms.ToTensor(),#转为Tensor
transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])# 标准化
])
train_loader = torch.utils.data.DataLoader(
SVHNDataset(train_path, train_label, trans_fun),
batch_size=40,shuffle=True)# 批量大小40，打乱顺序
test_loader = torch.utils.data.DataLoader(
SVHNDataset(test_path, [], trans_fun),
batch_size=40,shuffle=False)
model = SVHN_Model1()
criterion = nn.CrossEntropyLoss()# 交叉熵损失函数
optimizer = torch.optim.Adam(model.parameters(),0.001)# Adam优化器
use_cuda = torch.cuda.is_available()
if use_cuda: model = model.cuda()
for epochin range(10):
train_loss = train(train_loader, model, criterion, optimizer)# 训练
print(epoch, train_loss)
test_predict_label = predict(test_loader, model)
test_predict_label = np.vstack([
test_predict_label[:, :11].argmax(1), test_predict_label[:,11:22].argmax(1),
test_predict_label[:,22:33].argmax(1), test_predict_label[:,33:44].argmax(1),
test_predict_label[:,44:55].argmax(1),
]).T
test_label_pred = []
for xin test_predict_label:
test_label_pred.append(''.join(map(str, x[x!=10])))# 取出预字符不为10的字符且顺序排列
df_submit = pd.read_csv('../input/sample_submit_A.csv')
df_submit['file_code'] = test_label_pred
df_submit.to_csv('submit.csv',index=None)# 保存结果文件