loss.backward() # calculate the gradient and store in .grad attribute.
optimizer.step()
running_loss += loss.item() * trainx.shape[0]
running_corrects += torch.sum(
preds.cpu().squeeze() == trainy.squeeze())
#print("train_size: " + str(train_size))
lr_schedulerr.step() # test it
epoch_loss = running_loss / train_size
epoch_acc = running_corrects.double() / train_size
print("Training loss: {:.2f}; Accuracy: {:.2f}.".format(
epoch_loss, epoch_acc.item()))
#print("Training " + str(epoch) + ": loss: " + str(epoch_loss) + "," + "Accuracy: " + str(epoch_acc.item()) + ".")
state = {
'net': net.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
'loss': epoch_loss
}
savepath = model_path + 'checkpoint' + str(epoch) + '.pth'
#torch.save(state, savepath)
running_loss = 0.0
running_corrects = 0
if epoch % 1 == 0:
net.eval()
# print("Validating...")
with torch.no_grad():
for _, (val_x, val_y) in enumerate(val_loader):
if isinstance(net, timm.models.visformer.Visformer):
val_x = torch.unsqueeze(val_x, dim=1)
],
device=device,
)
# Model training for a specified number of epochs. `y` is None as it is already supplied
# in the dataset.
clf.fit(train_set, y=None, epochs=n_epochs)
clf.load_params(checkpoint=cp) # Load the model with the lowest valid_loss
import os
os.remove('./params.pt') # Delete parameters file
######################################################################
# Save the feature extractor model
# ------------
torch.save(FE_model.state_dict(), './model.pth')
######################################################################
# Plot Results
# ------------
#
######################################################################
# Now we use the history stored by Skorch throughout training to plot
# accuracy and loss curves.
#
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import pandas as pd
