import numpy as np
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
class DiabetesDataset(Dataset):
def __init__(self,filepath):
xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
self.x_data = torch.from_numpy(xy[:, :-1])
self.y_data = torch.from_numpy(xy[:, [-1]])
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
def __len__(self):
return self.len
dataset = DiabetesDataset('diabets.csv.gz')
train_loader = DataLoader(dataset=dataset,
batch_size=32,
shuffle=True,
num_workers=2)
class Model(torch.nn.Modle):
def __init__(self):
super(Model, self).__init__()
self.linear1 = torch.nn.Linear(8, 6)
self.linear2 = torch.nn.Linear(6, 4)
self.linear3 = torch.nn.Linear(4, 1)
self.sigmoid = torch.nn.Sigmoid()
def forward(self, x):
x = self.sigmoid(self.linear1(x))
x = self.sigmoid(self.linear2(x))
x = self.sigmoid(self.linear3(x))
return x
model = Model()
criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.paemeters(), lr=0.1)
for rpoch in range(100):
for i, data in enumerate(train_loader, 0):
inputs, labels = data
y_pred = model(inputs)
loss = criterion(y_pred, labels)
print(epoch, i, loss.item)
optimizer.zero_grad()
loss.backward()
optimizer.step()
作业： Build Dataloader for www.kaggle.com/c/titanic/d… Build a classifier using the DataLoader
