資料擴增
在pytorch中資料是通過dataset進行封裝，並通過dataloder進行並行讀取
class
svhndataset
(dataset)
: def __init__
(self, img_path, img_label, transform=none)
: self.img_path = img_path
self.img_label = img_label
if transform is not none:
self.transform = transform
else
: self.transform = none
def __getitem__
(self, index)
: img = image.
open
(self.img_path[index]).
convert
('rgb'
)if self.transform is not none:
img = self.
transform
(img)
# 原始svhn中類別10為數字0
lbl = np.
array
(self.img_label[index]
, dtype=np.int)
lbl =
list
(lbl)+(
5-len(lbl))*
[10]return img, torch.
from_numpy
(np.
array
(lbl[:5
])) def __len__
(self)
:return
len(self.img_path)
```
train_loader = torch.utils.data.
dataloader
(svhndataset
(train_path, train_label,
transforms.
compose([
transforms.
resize((
64,128)),
transforms.
colorjitter
(0.3
,0.3
,0.2),
transforms.
randomrotation(5
),transforms.
totensor()
, transforms.
normalize([
0.485
,0.456
,0.406],
[0.229
,0.224
,0.225])
])),
batch_size=
10, # 每批樣本個數
shuffle=false, # 是否打亂順序
)
# 定義模型
class
svhn_model1
(nn.module)
: def __init__
(self)
:super
(svhn_model1, self)
.__init__()
# cnn提取特徵模組
self.cnn = nn.
sequential
( nn.
conv2d(3
,16, kernel_size=(3
,3), stride=(2
,2))
, nn.
relu()
, nn.
maxpool2d(2
),nn.
conv2d(16
,32, kernel_size=(3
,3), stride=(2
,2))
, nn.
relu()
, nn.
maxpool2d(2
),) #
self.fc1 = nn.
linear(32
*3*7
,11) self.fc2 = nn.
linear(32
*3*7
,11) self.fc3 = nn.
linear(32
*3*7
,11) self.fc4 = nn.
linear(32
*3*7
,11) self.fc5 = nn.
linear(32
*3*7
,11) self.fc6 = nn.
linear(32
*3*7
,11)
def forward
(self, img)
:
feat = self.
cnn(img)
feat = feat.
view
(feat.shape[0]
,-1)
c1 = self.
fc1(feat)
c2 = self.
fc2(feat)
c3 = self.
fc3(feat)
c4 = self.
fc4(feat)
c5 = self.
fc5(feat)
c6 = self.
fc6(feat)
return c1, c2, c3, c4, c5, c6
# 損失函式
criterion = nn.
crossentropyloss()
# 優化器
optimizer = torch.optim.
adam
(model.
parameters()
,0.005
)# 儲存loss和accuracy
loss_plot, c0_plot =
,[]# 迭代10個epoch
for epoch in
range(10
):for data in train_loader:
c0, c1, c2, c3, c4, c5 =
model
(data[0]
) loss =
criterion
(c0, data[1]
[:,0
])+ \ criterion
(c1, data[1]
[:,1
])+ \ criterion
(c2, data[1]
[:,2
])+ \ criterion
(c3, data[1]
[:,3
])+ \ criterion
(c4, data[1]
[:,4
])+ \ criterion
(c5, data[1]
[:,5
])loss /=
6 optimizer.
zero_grad()
loss.
backward()
optimizer.
step()
loss_plot.
(loss.
item()
) c0_plot.
((c0.
argmax(1
)== data[1]
[:,0
]).sum()
.item()
*1.0
/ c0.shape[0]
)print
(epoch)
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