Kelas MSE 损失和我自己的损失函数有什么区别？

Question

我是 Keras 的新手。我试图在 Keras 中制作自定义损失函数。但是我的代码有问题。 Keras 工作但估计结果很奇怪。我应该在哪里更改代码？

我只是尝试将 MSE 作为自定义损失函数来实现。

这是一个损失函数部分。

def loss_function(ytrue, ypred):

    qx_true = ytrue[:, 0]
    qx_pred = ytrue[:, 0]
    qy_true = ytrue[:, 1]
    qy_pred = ytrue[:, 1]
    qz_true = ytrue[:, 2]
    qz_pred = ytrue[:, 2]
    qw_true = ytrue[:, 3]
    qw_pred = ytrue[:, 3]
    tx_true = ytrue[:, 4]
    tx_pred = ypred[:, 4]
    ty_true = ytrue[:, 5]
    ty_pred = ypred[:, 5]
    tz_true = ytrue[:, 6]
    tz_pred = ypred[:, 6]

    loss = ((tx_true - tx_pred) * (tx_true - tx_pred) 
        + (ty_true - ty_pred) * (ty_true - ty_pred) 
        + (tz_true - tz_pred) * (tz_true - tz_pred) 
        + (qx_true - qx_pred) * (qx_true - qx_pred) 
        + (qy_true - qy_pred) * (qy_true - qy_pred) 
        + (qz_true - qz_pred) * (qz_true - qz_pred) 
        + (qw_true - qw_pred) * (qw_true - qw_pred)) / 7

    return loss

这是调用损失函数部分

    model.add(Dense(7, name='output'))
    model.compile(loss=loss_function, optimizer=keras.optimizers.Adam())

当我尝试 Keras 原始损失函数时，它有效

    model.add(Dense(7, name='output'))
    model.compile(loss=keras.losses.MSE, optimizer=keras.optimizers.Adam())

损失函数的输入是平移的三个参数和四元数的四个参数。当我尝试使用 keras.losses.MSE 时，它成功了，我正在尝试做同样的事情。

哪里错了？谢谢

Answer 1

我相信

qx_true = ytrue[:, 0]
qx_pred = ytrue[:, 0]
qy_true = ytrue[:, 1]
qy_pred = ytrue[:, 1]
qz_true = ytrue[:, 2]
qz_pred = ytrue[:, 2]
qw_true = ytrue[:, 3]
qw_pred = ytrue[:, 3]

应该是

qx_true = ytrue[:, 0]
qx_pred = ypred[:, 0]
qy_true = ytrue[:, 1]
qy_pred = ypred[:, 1]
qz_true = ytrue[:, 2]
qz_pred = ypred[:, 2]
qw_true = ytrue[:, 3]
qw_pred = ypred[:, 3]

Kelas MSE 损失和我自己的损失函数有什么区别？

What is the difference between Kelas MSE loss and my own loss function?

deep-learning

keras

tensorflow

loss-function