I have this issue when I try to upgrade to the latest version of Pytorch 1.2 Traceback (most recent call last): File "/home/phong/data/Work/Paper2/Code/FUNIT/train.py", line 83, in <module> d_acc = trainer.dis_update(co_data, cl_data, config) File "/home/phong/data/Work/Pape...
def train(epochs): for epoch in range(epochs): for (batch, (images, labels)) in enumerate(dataset): train_step(images, labels) print('Epoch {} finished'.format(epoch)) train(epochs=3) plt.plot(loss_history) plt.xlabel('Batch #') plt.ylabel('Loss [entropy]') 1. 2. 3. 4. 5....
forkey, valueincfg.IMAGE_PATHS.items: ifisinstance(value, list): images = [] foriteminvalue: images.append(pygame.image.load(item)) game_images[key] = images else: game_images[key] = pygame.image.load(value) game_sounds = {} forkey, valueincfg.AUDIO_PATHS.items: ifkey =='bgm':c...
for i, data in enumerate(train_loader): optimizer.zero_grad() # reset hidden states model.hidden = model.init_hidden() # get inputs inputs, labels = data inputs = inputs.view(-1,28,28).to(device) labels = labels.to(device) # forward+backward+optimize outputs = model(inputs) loss...
forepochinrange(2):running_loss=0.0fori,datainenumerate(deeplake_loader):images,labels=data['images'],data['labels']# zero the parameter gradientsoptimizer.zero_grad()# forward + backward + optimizeoutputs=net(images)loss=criterion(outputs,labels.reshape(-1))loss.backward()optimizer.step()# ...
The structure decoder portion in the forward pass is defined below: # Get latent variables that were outputs from feature decoder zu2, zv2 = zu, zv # Loop through every MLP layer for u nodes for i, layer in enumerate(self.u_mlp_layers): ...
The reference implementation uses static module loading, but there are several other ways to enumerate modules including directory lookup and configuration. To use a different method of enumeration, simply change this method to instantiate a different enumerator. ConfigureContainer is ...
The reference implementation uses static module loading, but there are several other ways to enumerate modules including directory lookup and configuration. To use a different method of enumeration, simply change this method to instantiate a different enumerator. ConfigureContainer is ...
max_images=10input_locations,output_locations,=[],[]fori,fileinenumerate(glob.glob("data/processedimages/*.png")):input_1_s3_location=upload_image(sess,file,sess.default_bucket())input_locations.append(input_1_s3_location)async_response=base_model_predictor.predict_as...
deftrain_step(model:torch.nn.Module,data_loader:torch.utils.data.DataLoader,loss_fn:torch.nn.Module,optimizer:torch.optim.Optimizer,accuracy_fn,device:torch.device=device):train_loss,train_acc=0,0model.to(device)forbatch,(X,y)inenumerate(data_loader):# Send data to GPUX,y=X.to(device),...