我想对X射线扫描的6个不同类别进行分类，代码有什么问题？

model = Sequential()model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))model.add(Activation('relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Conv2D(256, (3, 3)))model.add(Activation('relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Flatten())  model.add(Dense(64))model.add(Dense(6))model.add(Activation('softmax'))model.compile(loss='categorical_crossentropy',              optimizer='adam',              metrics=['accuracy'])model.fit(X, y, batch_size=32, epochs=3, validation_split=0.1)

输入的形状是：(50, 50, 1)

我应该删除其中一个MaxPooling层吗？

我看到在这里发布错误跟踪也是很好的做法，所以这里是错误跟踪：

Epoch 1/3---------------------------------------------------------------------------ValueError                                Traceback (most recent call last)(...)ValueError: in user code:    C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:571 train_function  *        outputs = self.distribute_strategy.run(    C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:951 run  **        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)    C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2290 call_for_each_replica        return self._call_for_each_replica(fn, args, kwargs)    C:\Python38\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2649 _call_for_each_replica        return fn(*args, **kwargs)    C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py:532 train_step  **        loss = self.compiled_loss(    C:\Python38\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:205 __call__        loss_value = loss_obj(y_t, y_p, sample_weight=sw)    C:\Python38\lib\site-packages\tensorflow\python\keras\losses.py:143 __call__        losses = self.call(y_true, y_pred)    C:\Python38\lib\site-packages\tensorflow\python\keras\losses.py:246 call        return self.fn(y_true, y_pred, **self._fn_kwargs)    C:\Python38\lib\site-packages\tensorflow\python\keras\losses.py:1527 categorical_crossentropy        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)    C:\Python38\lib\site-packages\tensorflow\python\keras\backend.py:4561 categorical_crossentropy        target.shape.assert_is_compatible_with(output.shape)    C:\Python38\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1117 assert_is_compatible_with        raise ValueError("Shapes %s and %s are incompatible" % (self, other))    ValueError: Shapes (None, 1) and (None, 6) are incompatible

回答：

为了避免误解和可能的错误，我建议你将目标从(586,1)重塑为(586,)，你可以简单地执行y = y.ravel()

你需要正确管理损失函数

如果你有一维整数编码的目标，你可以使用sparse_categorical_crossentropy作为损失函数

X = np.random.randint(0,10, (1000,100))y = np.random.randint(0,3, 1000)model = Sequential([    Dense(128, input_dim = 100),    Dense(3, activation='softmax'),])model.summary()model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])history = model.fit(X, y, epochs=3)

否则，如果你对目标进行了独热编码以获得二维形状(n_samples, n_class)，你可以使用categorical_crossentropy

X = np.random.randint(0,10, (1000,100))y = pd.get_dummies(np.random.randint(0,3, 1000)).valuesmodel = Sequential([    Dense(128, input_dim = 100),    Dense(3, activation='softmax'),])model.summary()model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])history = model.fit(X, y, epochs=3)