return out
net_regr = NeuralNetRegressor(
Net(hidden_size=500),
max_epochs=5000,
lr=0.01,
device='cuda',
optimizer=torch.optim.Adam,
train_split=None,
verbose=1,
)
res = net_regr.fit(t_d_inp, t_d_oup)
# save
net_regr.save_params(f_params='step1result')
pred = net_regr.predict(test_inp)
mse = ((test_oup - pred)**2).mean()
print('test error = ' + str(mse))
# plot 1 loss
loss = net_regr.history[:, 'train_loss']
plt.figure()
plt.plot(loss)
plt.ylabel('loss')
plt.ylim([0, loss[-1] * 4])
# plot 2
plt.figure()
s = 3
plt.scatter(yaxis, pred, s=s, label="Prediction")
plt.scatter(yaxis, test_oup, s=s, label="DNS")
resource.setrlimit(resource.RLIMIT_NOFILE, (2048, rlimit[1]))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))
print("Fitting")
net.fit(train0df, y=None)
print("Fit completed")
history = net.history
train_loss0 = history[:, 'train_loss']
valid_loss0 = history[:, 'valid_loss']
ax1.plot(train_loss0)
ax1.plot(valid_loss0)
ax1.legend(['train_loss', 'valid_loss'])
net.save_params(f_params='dcs0_0005.pkl',
f_optimizer='dcs0_0005_optimizer.pkl',
f_history='dcs0_0005_history.json')
pred = net.predict_proba(valid0)
label = valid0.get_label()
accuracy = concordance_index(pred, label)
print(accuracy)
net1 = NeuralNetRegressor(model,
criterion=NegativeLogLikelihood,
lr=0.00001,
batch_size=512,
max_epochs=100,
optimizer=SGD,
optimizer__momentum=0.9,
optimizer__weight_decay=0.001,