【Python】利用skorch进行深度学习
2021/5/13 12:29:30
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【Python】利用skorch进行深度学习
利用pytorch能够很好地进行私人定制的深度学习,然而torch中的张量总是感觉充满神秘色彩,导致很多时候要进行很久的debug。具有numpy和sklearn特色的skorch应运而生。本文浅尝辄止,仅给出一个实际案例和代码。**
import skorch from skorch import NeuralNetRegressor from sklearn.model_selection import RandomizedSearchCV import torch.nn as nn import torch.optim as optim import torch.nn.functional as F class MyModule(nn.Module): def __init__(self,num_units=10,nonlin=F.relu,drop=.5): super(MyModule,self).__init__() self.module = nn.Sequential( nn.Linear(7,num_units), nn.LeakyReLU(), nn.Dropout(p=drop), nn.Linear(num_units,1), ) def forward(self,X): X = self.module(X) return X sknet = NeuralNetRegressor( MyModule, criterion=nn.MSELoss, max_epochs=10, optimizer=optim.Adam, optimizer__lr = .005 ) lr = (10**np.random.uniform(-5,-2.5,1000)).tolist() params = { 'optimizer__lr': lr, 'max_epochs':[300,400,500], 'module__num_units': [14,20,28,36,42], 'module__drop' : [0,.1,.2,.3,.4] } gs = RandomizedSearchCV(net,params,refit=True,cv=3,scoring='neg_mean_squared_error',n_iter=100)
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