labels.append(idx)#图像类标 fpath.append(path+im)#图像路径名#print(path+im,idx)returnnp.asarray(fpath,np.string_),np.asarray(imgs,np.float32),np.asarray(labels,np.int32)# 读取图像 fpaths,data,label=read_img(path)print(data.shape)#(1000,256,256,3)# 计算有多少类图片 num_classes=...
for pred, label in zip(preds, labels): iou = [] for i in range(C): if i != ignore: # The ignored label is sometimes among predicted classes (ENet - CityScapes) intersection = ((label == i) & (pred == i)).sum() union = ((label == i) | ((pred == i) & (label != ...
#一般流程: loss_fn=CrossEntropyLoss() #定义损失函数 optimizer=torch.optim.Adam(model.classifier.parameters(),lr=0.001) #定义优化器(调整参数)和设定学习率 model.train() for i,(img_tensor,label) in enumerate(tqdm(train_loader,desc=f'第{epoch+1}轮训练开始')): img_tensor=img_tensor.to(devi...
AI代码解释 pred=np.array([[0.8,2.0,2.0]])nClass=pred.shape[1]target=np.array([0])deflabelEncoder(y):tmp=np.zeros(shape=(y.shape[0],nClass))foriinrange(y.shape[0]):tmp[i][y[i]]=1returntmp defcrossEntropy(pred,target):target=labelEncoder(target)pred=softmax(pred)H=-np.sum(ta...
where the input consists of a # single batch of 32 frames and a single batch of 32 labels ...
fn = ((1-x)*y).sum(self.dims) dc = (2*tp+self.smooth)/(2*tp+fp+fn+self.smooth) dc = dc.mean() return 1 - dc bce_fn = nn.BCEWithLogitsLoss() dice_fn = SoftDiceLoss() def loss_fn(y_pred,y_true): bce = bce_fn(y_pred,y_true) ...
To convert target_labels to a one-hot vector, you'll need to create a tensor of zeros with the shape [batch_size, num_classes] and then use the target_labels to index into this tensor and set the corresponding positions to 1. This process essentially spreads the label indices across a ...
Nasledujúca lekcia: Exercise - Create a Microsoft 365 environment-specific Data Loss Prevention policy PredchádzajúciNasledujúci
def focal_loss(pred, y, alpha=0.25, gamma=2): r"""Compute focal loss for predictions. Multi-labels Focal loss formula: FL = -alpha * (z-p)^gamma * log(p) -(1-alpha) * p^gamma * log(1-p) ,which alpha = 0.25, gamma = 2, p = sigmoid(x), z = ta ...
loss_D_fake = loss_GAN(pred_fake, label_fake) 按我的理解,这里应该是生成器与判别器的博弈。 判别器始终认为生成的图片是假的,生成器一直在努力生成逼真的图片。 另外我还有个疑问,如果遇到模式崩溃,判别器生成的四不像图片判别器也认为是真的,是不是可以考虑加入一个 loss_GAN(real_B, label_fake)? 慕...