custom_layers.py 文件源码

def categorical_crossentropy(logit, y, mask, length_var, need_softmax=False):
    logit_shp = logit.shape
    # (n_samples, n_timesteps_f, n_labels)
    # softmax, predict label prob
    # (n_samples * n_timesteps_f, n_labels)
    if need_softmax:
        probs = T.nnet.softmax(logit.reshape([logit_shp[0]*logit_shp[1], logit_shp[2]]))
    else:
        probs = logit.reshape([logit_shp[0]*logit_shp[1], logit_shp[2]])
    # (n_samples * n_timesteps_f)
    y_flat = y.flatten()
    # clip to avoid nan loss
    probs = T.clip(probs, _EPSILON, 1.0 - _EPSILON)
    loss = lasagne.objectives.categorical_crossentropy(probs, y_flat)
    # (n_samples, n_timesteps_f)
    loss = loss.reshape((logit_shp[0], logit_shp[1]))
    loss = loss * mask
    loss = T.sum(loss, axis=1) / length_var
    probs = probs.reshape(logit_shp)
    return loss, probs