trained = True
net.load_state_dict(torch.load(PATH))
print('load parameters successfully')
print('for train')
net.eval() # turn on eval, switch off dropout layer
with torch.no_grad():
y_predict = net(Tensor(x_train))
y_predict = y_predict[:, 0]
predict = torch.ones_like(y_predict)
predict[y_predict < 0] = -1
analysis(x_train0, predict.numpy(), y_train)
y_train[y_train == -1] = 0.
predict[predict == -1] = 0.
vize.visualize_sample(x_train0[seqLen - 1::seqLen], y_train)
vize.visualize_prediction(x_train0[seqLen - 1::seqLen], y_train,
predict.numpy())
print('for validate')
net.eval() # turn on eval, switch off dropout layer
with torch.no_grad():
y_predict = net(Tensor(x_validate))
y_predict = y_predict[:, 0]
predict = torch.ones_like(y_predict)
predict[y_predict < 0] = -1
analysis(x_validate0, predict.numpy(), y_validate)
y_validate[y_validate == -1] = 0.
predict[predict == -1] = 0.
vize.visualize_sample(x_validate0[seqLen - 1::seqLen], y_validate)
vize.visualize_prediction(x_validate0[seqLen - 1::seqLen], y_validate,
print('Finished Training')
trained = True
net.load_state_dict(torch.load(PATH))
print('load parameters successfully')
print('for train')
net.eval() # turn on eval, switch off dropout layer
with torch.no_grad():
y_predict = net(Tensor(x_train))
predict = calculate_label(y_predict[:, 0].numpy(), w0)
analysis(x_train0, y_predict[:, 0].numpy(), y_train, w0)
y_train[y_train == -1] = 0.
predict[predict == -1] = 0.
vize.visualize_sample(x_train0[::seqLen], y_train[::seqLen])
vize.visualize_prediction(x_train0[::seqLen], y_train[::seqLen], predict)
print('for validate')
net.eval() # turn on eval, switch off dropout layer
with torch.no_grad():
y_predict = net(Tensor(x_validate))
predict = calculate_label(y_predict[:, 0].numpy(), w0)
analysis(x_validate0, y_predict[:, 0].numpy(), y_validate, w0)
y_validate[y_validate == -1] = 0.
predict[predict == -1] = 0.
vize.visualize_sample(x_validate0[::seqLen], y_validate[::seqLen])
vize.visualize_prediction(x_validate0[::seqLen], y_validate[::seqLen], predict)
#print('for test')
#with torch.no_grad():
PATH = './cifar_net.pth'
net.load_state_dict(torch.load(PATH))
print('load parameters successfully')
print('for train')
net.eval() # turn on eval, switch off dropout layer
with torch.no_grad():
y_predict=net(Tensor(x_train))
predict=calculate_label(y_predict[:,0].numpy(), w0)
predict_prob=calculate_probalbility(y_predict[:,0].numpy(), w0)
analysis(x_train0, y_predict[:,0].numpy(), y_train, w0)
y_train[y_train==-1]=0.
predict[predict==-1]=0.
vize.visualize_sample(x_train0[::seqLen], y_train[::seqLen])
vize.visualize_prediction(x_train0[::seqLen], y_train[::seqLen], predict)
x_train0=x_train0[::seqLen]
y_train=y_train[::seqLen]
y_train[y_train==0]=-1
#predict[predict==0]=-1
data=x_train0[:,1:]
#fig, ax=plt.subplots()
m=1
n=1
vMin=-15
vMax=15
deltaV=1.