Keras model shape incompatible / ValueError: Shapes (None, 3) and (None, 3, 3) are incompatible - numpy

Question

I'm trying to train my keras model but shapes are incompatible.
The error says
ValueError: Shapes (None, 3) and (None, 3, 3) are incompatible
My train set's shape is (2000, 3, 768) and lable's shape is (2000, 3).
What is the wrong the point?
Model define & fit code
input_shape = x_train.shape[1:]
model = my_dnn(input_shape, 3)
model.fit(x_train, y_train, epochs=25, verbose=1)
Model code
def my_dnn(input, num_classes):
model = Sequential()
model.add(tf.keras.Input(input))
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(225))
model.add(Activation('relu'))
model.add(Dense(100))
model.add(Activation('relu'))
model.add(Dense(num_classes))
model.add(Activation('sigmoid'))
model.compile( loss='categorical_crossentropy',
optimizer='adam',
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
return model

Answer 1

In addition to what's said, it seems you are carrying the second dimension of the input data until the end of the model. So your model summary is something like this:
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 3, 1024) 787456
_________________________________________________________________
activation_1 (Activation) (None, 3, 1024) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 3, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 3, 512) 524800
_________________________________________________________________
activation_2 (Activation) (None, 3, 512) 0
_________________________________________________________________
dense_3 (Dense) (None, 3, 225) 115425
_________________________________________________________________
activation_3 (Activation) (None, 3, 225) 0
_________________________________________________________________
dense_4 (Dense) (None, 3, 100) 22600
_________________________________________________________________
activation_4 (Activation) (None, 3, 100) 0
_________________________________________________________________
dense_5 (Dense) (None, 3, 3) 303
_________________________________________________________________
activation_5 (Activation) (None, 3, 3) 0
=================================================================
Total params: 1,450,584
Trainable params: 1,450,584
Non-trainable params: 0
As you can see, the output shape of the model (None, 3, 3) is not compatible with the label's shape (None, 3), and at some point, you need to use a Flatten layer.

Answer 2

There are two possible reasons:
Your problem is multi-class classification, hence you need softmax instead of sigmoid + accuracy or CategoricalAccuracy() as a metric.
Your problem is multi-label classification, hence you need binary_crossentropy and tf.keras.metrics.BinaryAccuracy()
Depending on how your dataset is built/the task you are trying to solve, you need to opt for one of those.
For case 1, ensure your data is OHE(one-hot encoded).
Also, Marco Cerliani and Amir (in the comment below) point out that the data output needs to be in a 2D format rather than 3D : you should either preprocess the data accordingly before feeding it to the network or use, as suggested in the comment below, a Flatten() at a point (probably before the final Dense())