Mini-Batch
代码实现
模型仍为上一讲中多维输入的模型，损失和优化器亦不变
from abc import ABC
import numpy as np
import torch
from torch.utils.data import Dataset # Dataset是抽象类, 不能实例化
from torch.utils.data import DataLoader
class DiabetesDataset(Dataset):
"""
继承自抽象类Dataset
"""
def __init__(self, filepath):
"""
:param filepath: 文件路径
"""
xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32)
self.len = xy.shape[0] # 比如我们的数据集为N行M列, shape[0]取第0列的维度, 也即N. 方便__len__的构造
self.x_data = torch.from_numpy(xy[:, :-1])
self.y_data = torch.from_numpy(xy[:, [-1]])
def __getitem__(self, index):
"""
:param index: 数据下标
:return: 获取下标对应的数据
"""
return self.x_data[index], self.y_data[index] # 直接返回xy对应下标的元素即可, 返回值是一个元组
def __len__(self):
"""
:return: 反馈数据集中的数据条数
"""
return self.len
dataset = DiabetesDataset('diabetes.csv.gz')
train_loader = DataLoader(dataset=dataset, # dataset即数据集
batch_size=2, # batch_size为小批量的容量大小
shuffle=True, # shuffle决定是否打乱数据, True打乱
num_workers=2) # 加载数据的线程数码你
class Model(torch.nn.Module, ABC):
def __init__(self):
super(Model, self).__init__()
self.linear1 = torch.nn.Linear(8, 6) # 维度 8 -> 6 -> 4 -> 1
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(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
if __name__ == '__main__': # 使用多线程的时候需要用if语句包裹起来, 否则会报RuntimeError
for epoch in range(100):
for i, data in enumerate(train_loader, 0):
"""
使用enumerate是为了获取当前是第几[i]次迭代, train_loader中取出的(x, y)给data
因为DataLoader我们设定了batch_size为2, 而我们的训练集中共有759条数据, 所以每个epoch都需要做i: 0~379的迭代
"""
inputs, labels = data
y_pred = model(inputs)
loss = criterion(y_pred, labels)
print(epoch, i, loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch0, epoch99
