ValueError:形状(无，3)和(无，2)不兼容

Question

所以这是我试图运行的代码

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)

但是我收到这个错误：target.shape.assert_is_compatible_with(output.shape(

ValueError：形状(无，3(和(无，2(不兼容

Answer 1

Shape(None，3(属于来自LabelBinarizer的trainY
Shape(None，2(是模型输出的形状
损失函数需要两个形状相同的数组来计算误差。正如ValueError所说，这两个数组的形状不同
请考虑检查二进制化器的输出形状，或更改网络最后一层中的神经元数量，以匹配数据集标签列中的唯一值数量。