import torch
from torch.autograd import Variable
import numpy as np

xy = np.loadtxt('./data/diabetes.csv.gz', delimiter=',', dtype=np.float32)
x_data = Variable(torch.from_numpy(xy[:, 0:-1]))
y_data = Variable(torch.from_numpy(xy[:, [-1]]))
print(xy[:, 0:-1].shape)  # 看輸入資料的大小
print(xy[:, [-1]].shape)  # 看輸入資料的大小


class Model(torch.nn.Module):
    def __init__(self):
        """
        In the constructor we instantiate two nn.Linear module
        """
        super(Model, self).__init__()
        self.l1 = torch.nn.Linear(8, 6)
        self.l2 = torch.nn.Linear(6, 4)
        self.l3 = torch.nn.Linear(4, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        out1 = self.sigmoid(self.l1(x))
        out2 = self.sigmoid(self.l2(out1))
        y_pred = self.sigmoid(self.l3(out2))
        return y_pred


# Our model
model = Model()

# Construct our loss function and an Optimizer. The call to model.parameters()
# in the SGD constructor will contain the learnable parameters of the two
# nn.Linear modules which are members of the model.

criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

# Training loop
for epoch in range(100):
    y_pred=model(x_data)

    # Compute and print loss
    loss=criterion(y_pred,y_data)
    print(epoch,loss.data[0])

    # Zero gradients,perform a backward pass,and update the weights
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()