def calc(output_network, target_vector):
shape = target_vector.shape[0]
smax = Softmax.calc(output_network)
log_likelihood = -np.log(smax[range(shape),
target_vector.argmax(axis=1)])
loss = np.sum(log_likelihood) / shape
#loss = log_likelihood / shape
return loss
def derived_calc(output_network, target_vector):
shape = target_vector.shape[0]
gradient = Softmax.calc(output_network)
gradient[range(shape), target_vector.argmax(axis=1)] -= 1
gradient = gradient / shape
return gradient