深度學習與Pytorch入門實戰（七）Visdom視覺化工具
阿新 • • 發佈：2020-07-16
1. 安裝visdom
2. 開啟監聽程序
python -m visdom.server
3. 訪問
用chrome瀏覽器訪問url連線：http://localhost:8097
4. 視覺化訓練
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在之前定義網路結構（參考上一節）的基礎上加上Visdom視覺化。
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在訓練-測試的迭代過程之前，定義兩條曲線，在訓練-測試的過程中 再不斷填充點 以實現 曲線隨著訓練動態增長：
from visdom import Visdom viz = Visdom() viz.line([0.], [0.], win='train_loss', opts=dict(title='train loss')) viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.', legend=['loss', 'acc.']))
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第二行
Visdom(env="xxx")引數env來設定環境視窗的名稱，這裡什麼都沒傳(在預設的main視窗下)。 -
viz.line的 前兩個引數 是曲線的Y和X的座標（前面是縱軸後面才是橫軸） -
設定了不同的 win引數，它們就會在不同的視窗中展示，
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第四行定義的是 測試集的loss 和 acc兩條曲線，所以在X等於0時，Y給了兩個初始值。
開始訓練：
- 為了知道訓練了多少個batch，設定一個全域性的計數器：
global_step = 0
- 在每個batch訓練完後，為訓練曲線新增點，來讓曲線實時增長：
global_step += 1 viz.line([loss.item()], [global_step], win='train_loss', update='append')
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這裡用 win引數 來選擇是哪條曲線，用
update='append'的方式新增曲線的增長點，前面是Y座標，後面是X座標。 -
在每次測試結束後，並在另外兩個視窗（用win引數設定）中展示影象（.images） 和 預測值（文字用.text）：
viz.line([[test_loss, correct / len(test_loader.dataset)]], [global_step], win='test', update='append') viz.images(data.view(-1, 1, 28, 28), win='x') viz.text(str(pred.detach().numpy()), win='pred', opts=dict(title='pred'))
完整程式碼：
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from visdom import Visdom
# 超引數
batch_size=200
learning_rate=0.01
epochs=10
# 獲取訓練資料
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True, #train=True則得到的是訓練集
transform=transforms.Compose([ #transform進行資料預處理
transforms.ToTensor(), #轉成Tensor型別的資料
# transforms.Normalize((0.1307,), (0.3081,)) # 進行資料標準化(減去均值除以方差),如果要顯示資料就不要標準化
])),
batch_size=batch_size, shuffle=True) #按batch_size分出一個batch維度在最前面,shuffle=True打亂順序
# 獲取測試資料
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.model = nn.Sequential( #定義網路的每一層,
nn.Linear(784, 200),
nn.LeakyReLU(inplace=True),
nn.Linear(200, 200),
nn.LeakyReLU(inplace=True),
nn.Linear(200, 10),
nn.LeakyReLU(inplace=True),
)
def forward(self, x):
x = self.model(x)
return x
net = MLP()
# 定義sgd優化器,指明優化引數、學習率，net.parameters()得到這個類所定義的網路的引數[[w1,b1,w2,b2,...]
optimizer = optim.SGD(net.parameters(), lr=learning_rate)
criteon = nn.CrossEntropyLoss()
# 定義兩條曲線，在訓練-測試的過程中
# 再不斷填充點 以實現 曲線隨著訓練動態增長
viz = Visdom()
viz.line([0.], [0.], win='train_loss', opts=dict(title='train loss'))
# 定義的是 測試集的loss 和 acc兩條曲線，所以在X等於0時，Y給了兩個初始值。
viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.',
legend=['loss', 'acc.']))
global_step = 0
for epoch in range(epochs):
for batch_idx, (data, target) in enumerate(train_loader):
data = data.view(-1, 28*28) # 將二維的圖片資料攤平[樣本數,784]
logits = net(data) # 前向傳播
loss = criteon(logits, target) # nn.CrossEntropyLoss()自帶Softmax
optimizer.zero_grad() # 梯度資訊清空
loss.backward() # 反向傳播獲取梯度
optimizer.step() # 優化器更新
global_step += 1
# 繪製訓練集的loss的圖
viz.line([loss.item()], [global_step], win='train_loss', update='append')
if batch_idx % 100 == 0: # 每100個batch輸出一次資訊
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
test_loss = 0
correct = 0 # correct記錄正確分類的樣本數
for data, target in test_loader:
data = data.view(-1, 28 * 28)
logits = net(data)
test_loss += criteon(logits, target).item() # 其實就是criteon(logits, target)的值，標量
pred = logits.argmax(dim=1)
correct += pred.eq(target.data).float().sum().item()
# 更新訓練集的 loss 和 accuracy
# print('test accuracy: ', correct / len(test_loader.dataset))
viz.line([[test_loss, 100. * correct / len(test_loader.dataset)]], [global_step],
win='test', update='append')
viz.images(data.view(-1, 1, 28, 28), win='x', opts = dict(title = 'Real Image'))
viz.text(str(pred.detach().cpu().numpy()), win='pred',
opts=dict(title='pred'))
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
view result
Train Epoch: 0 [0/60000 (0%)] Loss: 2.300833
Train Epoch: 0 [20000/60000 (33%)] Loss: 2.282752
Train Epoch: 0 [40000/60000 (67%)] Loss: 2.260350
test accuracy: 0.4326
Test set: Average loss: 0.0111, Accuracy: 4326.0/10000 (43%)
Train Epoch: 1 [0/60000 (0%)] Loss: 2.243046
Train Epoch: 1 [20000/60000 (33%)] Loss: 2.179665
Train Epoch: 1 [40000/60000 (67%)] Loss: 2.034719
test accuracy: 0.5407
Test set: Average loss: 0.0093, Accuracy: 5407.0/10000 (54%)
Train Epoch: 2 [0/60000 (0%)] Loss: 1.852168
Train Epoch: 2 [20000/60000 (33%)] Loss: 1.656816
Train Epoch: 2 [40000/60000 (67%)] Loss: 1.399417
test accuracy: 0.7035
Test set: Average loss: 0.0060, Accuracy: 7035.0/10000 (70%)
Train Epoch: 3 [0/60000 (0%)] Loss: 1.263554
Train Epoch: 3 [20000/60000 (33%)] Loss: 1.032210
Train Epoch: 3 [40000/60000 (67%)] Loss: 0.860783
test accuracy: 0.8003
Test set: Average loss: 0.0038, Accuracy: 8003.0/10000 (80%)
Train Epoch: 4 [0/60000 (0%)] Loss: 0.833112
Train Epoch: 4 [20000/60000 (33%)] Loss: 0.625544
Train Epoch: 4 [40000/60000 (67%)] Loss: 0.610665
test accuracy: 0.8596
Test set: Average loss: 0.0027, Accuracy: 8596.0/10000 (86%)
Train Epoch: 5 [0/60000 (0%)] Loss: 0.657896
Train Epoch: 5 [20000/60000 (33%)] Loss: 0.530274
Train Epoch: 5 [40000/60000 (67%)] Loss: 0.471292
test accuracy: 0.8795
Test set: Average loss: 0.0023, Accuracy: 8795.0/10000 (88%)
Train Epoch: 6 [0/60000 (0%)] Loss: 0.418883
Train Epoch: 6 [20000/60000 (33%)] Loss: 0.421887
Train Epoch: 6 [40000/60000 (67%)] Loss: 0.429563
test accuracy: 0.8908
Test set: Average loss: 0.0020, Accuracy: 8908.0/10000 (89%)
Train Epoch: 7 [0/60000 (0%)] Loss: 0.429593
Train Epoch: 7 [20000/60000 (33%)] Loss: 0.368680
Train Epoch: 7 [40000/60000 (67%)] Loss: 0.389523
test accuracy: 0.8963
Test set: Average loss: 0.0019, Accuracy: 8963.0/10000 (90%)
Train Epoch: 8 [0/60000 (0%)] Loss: 0.382745
Train Epoch: 8 [20000/60000 (33%)] Loss: 0.352603
Train Epoch: 8 [40000/60000 (67%)] Loss: 0.347548
test accuracy: 0.8982
Test set: Average loss: 0.0018, Accuracy: 8982.0/10000 (90%)
Train Epoch: 9 [0/60000 (0%)] Loss: 0.346902
Train Epoch: 9 [20000/60000 (33%)] Loss: 0.367318
Train Epoch: 9 [40000/60000 (67%)] Loss: 0.369855
test accuracy: 0.9021
Test set: Average loss: 0.0017, Accuracy: 9021.0/10000 (90%)