# Check for GPU availability:
device = my_models.device_gpu_cpu()
print('using device:', device)
dtype = torch.float32 # we will be using float
# Constant to control how frequently we print train loss
print_every = 100
train = True
test = True
model = None
if train:
# Create models:
model = my_models.model_2()
my_models.test_model_size(model, dtype) # test model size output:
optimizer = optim.Adadelta(model.parameters())
# Train model:
model, loss_data = my_models.train_model(model,
optimizer,
train_loader,
val_loader,
device,
dtype,
epoches=2)
# Save model to file:
torch.save(model.state_dict(), MODEL_PATH + MODEL_NAME)
# Check for GPU availability:
device = my_models.device_gpu_cpu()
print('using device:', device)
dtype = torch.float32 # we will be using float
train = True
train_loops = 20
loss_data = []
if train:
for tl in range(train_loops):
# Create models:
model_1 = my_models.model_2()
model_2 = my_models.model_2()
model = (model_1, model_2)
#my_models.test_model_size(model, dtype) # test model size output:
optimizer = optim.Adadelta(model.parameters())
# Train model:
model, current_loss_data = my_models.train_model_5(model, optimizer, train_loader, test_loader, device, dtype, epoches=4, print_every=5)
loss_data.append(current_loss_data)
# Save model to file:
#torch.save(model.state_dict(), MODEL_PATH + MODEL_NAME)
print('Training Loop {}/{} is Finished !'.format(tl + 1, train_loops))
print()
# Add test accuracy and save data to file:
