利用pytorch構建簡單神經網絡

省略了數據集的處理過程

#轉爲tensor
x = torch.tensor(input_features, dtype = float)

y = torch.tensor(labels, dtype = float)

# 權重參數初始化，設計網路結構 輸入348*14
weights = torch.randn((14, 128), dtype = float, requires_grad = True) #w1 14個變爲128個
biases = torch.randn(128, dtype = float, requires_grad = True) 
weights2 = torch.randn((128, 1), dtype = float, requires_grad = True) #結果是1個值
biases2 = torch.randn(1, dtype = float, requires_grad = True) 

learning_rate = 0.001 
losses = []

for i in range(1000):
    # 計算隱層
    hidden = x.mm(weights) + biases  #mm 矩陣乘法
    # 加入激活函數
    hidden = torch.relu(hidden)
    # 預測結果
    predictions = hidden.mm(weights2) + biases2
    # 通計算損失
    loss = torch.mean((predictions - y) ** 2)  #均方誤差
    losses.append(loss.data.numpy())
    
    # 打印損失值
    if i % 100 == 0:
        print('loss:', loss)
    #返向傳播計算
    loss.backward()
    
    #更新參數
    weights.data.add_(- learning_rate * weights.grad.data)  
    biases.data.add_(- learning_rate * biases.grad.data)
    weights2.data.add_(- learning_rate * weights2.grad.data)
    biases2.data.add_(- learning_rate * biases2.grad.data)
    
    # 每次迭代都得記得清空
    weights.grad.data.zero_()
    biases.grad.data.zero_()
    weights2.grad.data.zero_()
    biases2.grad.data.zero_()

loss: tensor(8347.9924, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(152.3170, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(145.9625, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(143.9453, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(142.8161, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(142.0664, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(141.5386, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(141.1528, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(140.8618, dtype=torch.float64, grad_fn=<MeanBackward0>)
loss: tensor(140.6318, dtype=torch.float64, grad_fn=<MeanBackward0>)

更簡單的方法

input_size = input_features.shape[1]
hidden_size = 128
output_size = 1
batch_size = 16
my_nn = torch.nn.Sequential(
    torch.nn.Linear(input_size, hidden_size),
    torch.nn.Sigmoid(),
    torch.nn.Linear(hidden_size, output_size),
)
cost = torch.nn.MSELoss(reduction='mean')
optimizer = torch.optim.Adam(my_nn.parameters(), lr = 0.001)

# 訓練網絡
losses = []
for i in range(1000):
    batch_loss = []
    # MINI-Batch方法來進行訓練
    for start in range(0, len(input_features), batch_size):
        end = start + batch_size if start + batch_size < len(input_features) else len(input_features)
        xx = torch.tensor(input_features[start:end], dtype = torch.float, requires_grad = True)
        yy = torch.tensor(labels[start:end], dtype = torch.float, requires_grad = True)
        prediction = my_nn(xx)
        loss = cost(prediction, yy)
        optimizer.zero_grad()
        loss.backward(retain_graph=True)
        optimizer.step()
        batch_loss.append(loss.data.numpy())
    
    # 打印損失
    if i % 100==0:
        losses.append(np.mean(batch_loss))
        print(i, np.mean(batch_loss))

0 3950.7627
100 37.9201
200 35.654438
300 35.278366
400 35.116814
500 34.986076
600 34.868954
700 34.75414
800 34.637356
900 34.516705

預測

x = torch.tensor(input_features, dtype = torch.float)
predict = my_nn(x).data.numpy()