optimizer = torch.optim.Adam(model.parameters(), lr=0.01) #Adam 0.01
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=0.7)
len_train = len(train_ds)
len_val = len(val_ds)
loss_plt = []
for epoch in range(N_EPOCHS):
train_dl = DataLoader(train_ds,
batch_size=BATCH_SIZE,
shuffle=True,
collate_fn=train_ds.generate_batch)
val_dl = DataLoader(val_ds,
batch_size=BATCH_SIZE,
collate_fn=val_ds.generate_batch)
train_loss, train_acc = train_epoc(train_dl, model, criterion,
optimizer, scheduler)
valid_loss, valid_acc = val_epoc(val_dl, model, criterion)
print("epoch", epoch)
print(
f'\tLoss: {train_loss/len_train:.8f}(train)\t|\tAcc: {train_acc/len_train * 100:.3f}%(train)'
)
print(
f'\tLoss: {valid_loss/len_val:.8f}(valid)\t|\tAcc: {valid_acc/len_val * 100:.3f}%(valid)'
)
loss_plt.append(train_acc / len_train)
torch.save(model.state_dict(), "gender-" + str(epoch) + ".pth")
print(loss_plt)
plt.plot(range(len(loss_plt)), loss_plt)
plt.show()
train_df=train_df.drop(val_df.index)
train_df.reset_index(drop=True, inplace=True)
val_df.reset_index(drop=True, inplace=True)
train_ds=dataset.seq_dataset(train_df,SEQ_LEN,"age")
val_ds=dataset.seq_dataset(val_df,SEQ_LEN,"age")
model = model.baseline_model(VOCAB_SIZE, EMBED_DIM, NUN_CLASS,MODE).to(device)
criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.Adam(model.parameters(),lr=0.01)#Adam 0.01
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size=1,gamma=0.7)
len_train=len(train_ds)
len_val=len(val_ds)
loss_plt=[]
for epoch in range(N_EPOCHS):
train_dl = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True,
collate_fn=train_ds.generate_batch)
val_dl = DataLoader(val_ds, batch_size=BATCH_SIZE,
collate_fn=val_ds.generate_batch)
train_loss, train_acc = train_epoc(train_dl,model,criterion,optimizer,scheduler)
valid_loss, valid_acc = val_epoc(val_dl,model,criterion)
print("epoch",epoch)
print(f'\tLoss: {train_loss/len_train:.8f}(train)\t|\tAcc: {train_acc/len_train * 100:.3f}%(train)')
print(f'\tLoss: {valid_loss/len_val:.8f}(valid)\t|\tAcc: {valid_acc/len_val * 100:.3f}%(valid)')
loss_plt.append(train_acc/len_train)
torch.save(model.state_dict(),"age-"+str(epoch)+".pth")
print(loss_plt)
plt.plot(range(len(loss_plt)), loss_plt)
plt.show()
#print("Time after: ", datetime.datetime.now())
total_accuracy.append(accuracy)
total_loss.append(loss)
if (epoch % 20 == 0):
print("\n\n--- Test epoch " + str(epoch) + " ---\n\n")
validate.validate(model, epoch, optimizer, test_loader, args,
logger, test_acc_dict, episode, criterion)
#memory.flush()
### SAVING CHECKPOINT ###
save_checkpoint({
'epoch': epoch + 1,
'episode': episode,
'state_dict': model.state_dict(),
'accuracy': acc_dict,
'tot_accuracy': total_accuracy,
'tot_loss': total_loss,
})
elapsed_time = time.time() - start_time
print("ELAPSED TIME = " + str(elapsed_time) + " seconds")
answer = input("How many more epochs to train: ")
try:
if int(answer) == 0:
done = True
else:
args.start_epoch = args.epochs + 1
args.epochs += int(answer)
except: