import  torch
import  numpy as np
import matplotlib.pyplot as plt

#Prepare dataset
xy = np.loadtxt('diabetes.csv', delimiter=',', dtype=np.float32, skiprows=1)
x_data = torch.from_numpy(xy[:, :-1]) # 第一個‘：’是指讀取所有行，第二個‘：’是指從第一列開始，最后一列不要
y_data = torch.from_numpy(xy[:, [-1]]) # [-1] 最后得到的是個矩陣

#Design model using class
class Model(torch.nn.Module):
    def __init__(self):
        torch.nn.Module.__init__(self)
        self.linear1 = torch.nn.Linear(8, 6) # 輸入數據x的特征是8維，x有8個特征
        self.linear2 = torch.nn.Linear(6, 4)
        self.linear3 = torch.nn.Linear(4, 1)
        self.sigmoid = torch.nn.Sigmoid() # 將其看作是網絡的一層，而不是簡單的函數使用
 
    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x)) # y hat
        return x

model = Model()
#Construct loss and optimizer
criterion = torch.nn.BCELoss(reduction='mean')  
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
epoch_list = []
loss_list = []
#Training cycle forward, backward, update
for epoch in range(10000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())
    epoch_list.append(epoch)
    loss_list.append(loss.item())
 
    optimizer.zero_grad()
    loss.backward()
 
    optimizer.step()
 
 
plt.plot(epoch_list, loss_list)
plt.ylabel('loss')
plt.xlabel('epoch')
plt.show()