Python 形状不匹配：标签的形状（接收到的（128，））应与logit的形状相同，但最后一个尺寸除外（接收到的（16424））

错误 ValueError:在转换的代码中：

<ipython-input-63-1e3afece3370>:10 train_step  *
    loss += loss_func(targ, logits)
<ipython-input-43-44b2a8f6794e>:11 loss_func  *
    loss_ = loss_object(real, pred)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/losses.py:124 __call__
    losses = self.call(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/losses.py:216 call
    return self.fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/losses.py:973 sparse_categorical_crossentropy
    y_true, y_pred, from_logits=from_logits, axis=axis)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py:4431 sparse_categorical_crossentropy
    labels=target, logits=output)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_ops.py:3477 sparse_softmax_cross_entropy_with_logits_v2
    labels=labels, logits=logits, name=name)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_ops.py:3393 sparse_softmax_cross_entropy_with_logits
    logits.get_shape()))

ValueError: Shape mismatch: The shape of labels (received (128,)) should equal the shape of logits except for the last dimension (received (16, 424)).

损失函数

    optimizer = tf.keras.optimizers.Adam()
    loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
                     from_logits=True,
                     reduction='none')

    def loss_func(real, pred):
        mask = tf.math.logical_not(tf.math.equal(real, 0))
        loss_ = loss_object(real, pred)
        mask = tf.cast(mask, dtype=loss_.dtype)
        loss_ *= mask   
        return tf.reduce_mean(loss_)

火车站

    @tf.function
    def train_step(inp, targ):
        loss = 0
        with tf.GradientTape() as tape:
             logits = model(inp)
             loss += loss_func(targ, logits)

        variables = model.trainable_variables
        gradients = tape.gradient(loss, variables)
        optimizer.apply_gradients(zip(gradients, variables))
        return loss

输入：[135,144,0,0,0,0,0,0,0]

---

目标：[144127,0,0,0,0,0,0,0]

问题已解决。如果有人面临上述问题。请发表评论。我只是浏览了我网络中的形状，在我的LSTM层中发现了问题。我的2个神经元（softmax）的输出，错误是：形状不匹配：标签的形状（接收到的（8，））应该与Logit的形状相等，除了最后一个维度（接收到的（4，2））。解决方案？@datdinhquoc请分享一些代码？以下是代码，它抱怨形状：@datdinhquoc这段代码工作正常。我没有遇到任何类型的错误，请提供更多关于如何复制错误的详细信息。

    @tf.function
    def train_step(inp, targ):
        loss = 0
        with tf.GradientTape() as tape:
             logits = model(inp)
             loss += loss_func(targ, logits)

        variables = model.trainable_variables
        gradients = tape.gradient(loss, variables)
        optimizer.apply_gradients(zip(gradients, variables))
        return loss