cross_entropy_loss.py

class CrossEntropyLoss(Loss):
    def forward(self, X, y):
        """
        Computes the cross entropy loss of x with respect to y.
        Args:
            X: numpy.ndarray of shape (n_batch, n_dim).
            y: numpy.ndarray of shape (n_batch, 1). Should contain class labels
                for each data point in x.
        Returns:
            crossentropy_loss: numpy.float. Cross entropy loss of x with respect to y.
        """
        # calculating crossentropy
        exp_x = np.exp(X)
        probs = exp_x/np.sum(exp_x, axis=1, keepdims=True)
        log_probs = -np.log([probs[i, y[i]] for i in range(len(probs))])
        crossentropy_loss = np.mean(log_probs)

        # caching for backprop
        self.cache['probs'] = probs
        self.cache['y'] = y

        return crossentropy_loss

    def local_grad(self, X, Y):
        probs = self.cache['probs']
        ones = np.zeros_like(probs)
        for row_idx, col_idx in enumerate(Y):
            ones[row_idx, col_idx] = 1.0

        grads = {'X': (probs - ones)/float(len(X))}
        return grads