这就是我要运行的代码

X_train = data1/255.0
from sklearn.preprocessing import LabelBinarizer
lb = LabelBinarizer()
trainY =lb.fit_transform(label)
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Denseenter 
from tensorflow.keras.layers import concatenate
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import SGD
Model = Sequential()
shape = (100,100, 1)
Model.add(Conv2D(32,(3,3),padding="same",input_shape=shape))
Model.add(Activation("relu"))
Model.add(Conv2D(32,(3,3), padding="same"))
Model.add(Activation("relu"))
Model.add(MaxPooling2D(pool_size=(2,2)))
Model.add(Conv2D(64,(3,3), padding="same"))
Model.add(Activation("relu"))
Model.add(MaxPooling2D(pool_size=(2,2)))
Model.add(Flatten())
Model.add(Dense(512))
Model.add(Activation("relu"))
Model.add(Dense(2))
Model.add(Activation("softmax"))
Model.summary()
Model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
print("start training")
Model.fit(X_train,trainY,batch_size=5,epochs=10)

但我收到此错误：输出图形与目标图形兼容
ValueError：形状（None，3）和（None，2）不兼容