Hi Guys,
I’m trying to set a dict with some parameters, like optimizers and others. How I should do?
Here is my code:
class Net(torch.nn.Module):
def __init__(self):
'''
A feedForward neural network.
Argurmets:
n_feature: How many of features in your data
n_hidden: How many of neurons in the hidden layer
n_output: How many of neuros in the output leyar (defaut=1)
'''
super(Net, self).__init__()
self.hidden = torch.nn.Linear(D_in, H, bias=True) # hidden layer
self.predict = torch.nn.Linear(H, D_out, bias=True) # output layer
self.n_feature, self.n_hidden, self.n_output = D_in, H, D_out
def forward(self, x,**kwargs):
'''
Argurmets:
x: Features to predict
'''
torch.nn.init.constant_(self.hidden.bias.data,1)
torch.nn.init.constant_(self.predict.bias.data,1)
x = torch.sigmoid(self.hidden(x)) # activation function for hidden layer
x = torch.sigmoid(self.predict(x)) # linear output
return x
from skorch import NeuralNetRegressor
net = NeuralNetRegressor(Net
, max_epochs=100
, lr=0.001
, verbose=1)
X_trf = X
y_trf = y.reshape(-1, 1)
from sklearn.model_selection import GridSearchCV
#from sklearn.metrics import SCORERS
#SCORERS.keys()
params = {
'lr': [0.001,0.005, 0.01, 0.05, 0.1, 0.2, 0.3],
'max_epochs': list(range(500,5500, 500))
}
gs = GridSearchCV(net, params, refit=False, scoring='r2', verbose=1, cv=10)
gs.fit(X_trf, y_trf)