Python实现Complement Entropy Loss 参考 ICLR 2019论文 COMPLEMENT OBJECTIVE TRAINING

最新推荐文章于 2024-04-11 16:58:08 发布

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最新推荐文章于 2024-04-11 16:58:08 发布 · 991 阅读

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import numpy as np


def zero_hot(labels_dense, num_classes):
    """Convert class labels from scalars to one-hot vectors."""
    
    num_labels = labels_dense.shape[0]
    index_offset = np.arange(num_labels) * num_classes
    labels_one_hot = np.ones((num_labels, num_classes))
    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 0
    return labels_one_hot




def Complememnt_EntropyLoss(labels, predictions, num_classes):
    #get the length of labels
    # get predcitions using softmax function
    num_labels = labels.shape[0]
    
    #temp 
    temp = []
    
    for i in range(num_labels):
        x=np.take(predictions[i], labells[i])
        temp.append(x)
          
    temp = (1- temp) + le-7   
    
    temp2 = np.reshape(temp,(-1, 1))
       
    Px = predictions / temp2
    
    Px_log = np.log(Px + le-10)
    
    output = Px*Px_log
    
    y_zerohot = zero_hot(labels, num_classes)
    
    output = output*y_zerohot
    
    loss = np.sum