utils.py 文件源码

def temp_log_loss(w, X, Y, alpha):
    n_classes = Y.shape[1]
    w = w.reshape(n_classes, -1)
    intercept = w[:, -1]
    w = w[:, :-1]
    z = safe_sparse_dot(X, w.T) + intercept

    denom = expit(z)
    #print denom
    #print denom.sum()
    denom = denom.sum(axis=1).reshape((denom.shape[0], -1))
    #print denom
    p = log_logistic(z)

    loss = - (Y * p).sum()
    loss += np.log(denom).sum()
    loss += 0.5 * alpha * squared_norm(w)

    return loss