loss_cce.py

from loss import Loss
import numpy as np

class LossCategoricalCrossentropy ( Loss ):
    def forward ( self , y_pred , y_true ):
        samples = len (y_pred)
        y_pred_clipped = np.clip(y_pred, 1e-7 , 1 - 1e-7 )
        if len (y_true.shape) == 1 :
            correct_confidences = y_pred_clipped[
            range (samples),
            y_true
            ]
        elif len (y_true.shape) == 2 :
            correct_confidences = np.sum(
            y_pred_clipped * y_true,
            axis = 1
            )
        negative_log_likelihoods = - np.log(correct_confidences)
        return negative_log_likelihoods

    def backward ( self , dvalues , y_true ):
        samples = len (dvalues)
        labels = len (dvalues[ 0 ])
        if len (y_true.shape) == 1 :
            y_true = np.eye(labels)[y_true]
        self.dinputs = - y_true / dvalues
        self.dinputs = self.dinputs / samples