Keras 我正在尝试使用NSL-KDD数据集训练卷积神经网络

运行上述代码，我得到以下错误：

lstm_output_size = 128
cnn = Sequential()
cnn.add(Convolution1D(64, 3 ,activation="relu",input_shape=(105, 1)))
cnn.add(Convolution1D(64, 3, activation="relu"))
cnn.add(MaxPooling1D(pool_size=(2)))
cnn.add(Flatten())
cnn.add(Dense(128, activation="relu"))
cnn.add(Dropout(0.5))
cnn.add(Dense(5, activation="softmax"))

# define optimizer and objective, compile cnn
cnn.compile(loss="categorical_crossentropy", optimizer="adam",metrics=['accuracy'])

# train
cnn.fit(train, target, epochs=100, batch_size=128 ,validation_data=(test, y_test))

我做错了什么？我错过什么了吗？我在这件事上耽搁了一段时间。 我确实回顾了类似的问题及其解决方案，但似乎没有任何效果

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-1597-0f80c6fb3b4f> in <module>()
 15 
 16 # train
 ---> 17 cnn.fit(train, target, epochs=100, batch_size=128 ,validation_data=(test, y_test))

 9 frames

 /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in     wrapper(*args, **kwargs)
975           except Exception as e:  # pylint:disable=broad-except
976             if hasattr(e, "ag_error_metadata"):

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
    return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
    return fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step  **
    outputs = model.train_step(data)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:756 train_step
    y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:203 __call__
    loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:152 __call__
    losses = call_fn(y_true, y_pred)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:256 call  **
    return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
    return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:1537 categorical_crossentropy
    return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
    return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py:4833 categorical_crossentropy
    target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with
    raise ValueError("Shapes %s and %s are incompatible" % (self, other))

ValueError: Shapes (None, 23) and (None, 5) are incompatible