optimizer.step()if(iter+1) % 10 ==0:print('iter [{}/{}], Loss: {:.4f}'.format(iter+1, 300, loss.item()))#writer.add_graph(model, input_to_model=train_text,verbose=False)writer.add_scalar('loss',loss.item(),global_step=iter+1) writer.flush() writer.close() model_path= ...
conv11d(x12d) return x11d def load_from_segnet(self, model_path): s_dict = self.state_dict() # create a copy of the state dict th = torch.load(model_path).state_dict() # load the weigths # for name in th: # s_dict[corresp_name[name]] = th[name] self.load_state_dict(...
path.isdir('./models'): os.mkdir('./models') # Create directory of saving models. n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0 for epoch in range(n_epochs): print(epoch) model.train() # Set your model to train mode. loss_record = [] ...
28*28))# encode,decode=model(x)#print(encode.shape)train_data=get_data()criterion=nn.MSELoss()optimizier=optim.Adam(model.parameters(),lr=lr,weight_decay=weight_decay)iftorch.cuda.is_available():model.cuda()forepochinrange(epoches):ifepochin[epoches*0.25,epoches*0.5]:for...
model_path = os.path.join(out_path, "best_model.pth") root = "../dataset/carvana" epochs = 5 numclasses = 2 train_data = Car(root, train=True) train_dataloader = DataLoader(train_data, batch_size=16, shuffle=True) val_data = Car(root, train=False) ...
tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForMaskedLM.from_pretrained(model_id) #text = "The Chairman of China is [MASK]." text = "The capital of China is [MASK]." #text = "US is [MASK]." inputs = tokenizer(text, return_tensors="pt") ...
model.h status.h tensor.h types.h neural_network_runtime_type.h neural_network_runtime.h native_avcodec_audiodecoder.h native_avcodec_audioencoder.h native_avcodec_base.h native_avcodec_videodecoder.h native_avcodec_videoencoder.h native_averrors.h native_avformat.h native_...
model.h status.h tensor.h types.h neural_network_runtime_type.h neural_network_runtime.h native_avcodec_audiodecoder.h native_avcodec_audioencoder.h native_avcodec_base.h native_avcodec_videodecoder.h native_avcodec_videoencoder.h native_averrors.h native_avformat.h native_...
def classify(x,y,model=model): return array([model.classify([xx,yy]) for (xx,yy) in zip(x,y)]) #绘制分类边界 subplot(1,2,i+1) imtools.plot_2D_boundary([-6,6,-6,6],[class_1,class_2],classify,[1,-1]) titlename=pklfile[:-4] ...
model.h status.h tensor.h types.h neural_network_runtime_type.h neural_network_runtime.h native_avcodec_audiodecoder.h native_avcodec_audioencoder.h native_avcodec_base.h native_avcodec_videodecoder.h native_avcodec_videoencoder.h native_averrors.h native_avformat.h native_...