6print(
"running Torch code defining the model....")
16 nn.Conv2d(1, 10, kernel_size=3, padding=1),
19 nn.Conv2d(10, 10, kernel_size=3, padding=1),
36 schedule, schedulerSteps = scheduler
43 for epoch
in range(num_epochs):
47 running_train_loss = 0.0
48 running_val_loss = 0.0
61 print(f
"[{epoch+1}, {i+1}] train loss: {running_train_loss / 4 :.3f}")
62 running_train_loss = 0.0
65 schedule(optimizer, epoch, schedulerSteps)
78 curr_val = running_val_loss /
len(val_loader)
82 best_val =
save_best(model, curr_val, best_val)
85 print(f
"[{epoch+1}] val loss: {curr_val :.3f}")
86 running_val_loss = 0.0
88 print(f
"Finished Training on {epoch+1} Epochs!")
108 X = data[0].to(device)
121print(
"The PyTorch CNN model is created and saved as PyTorchModelCNN.pt")
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t UChar_t len