Python Tensorflow我不明白model.fit的输入应该是什么

我是一个初学者，我正在尝试将一些图像分为20类

这是我尝试使用的代码：

from tensorflow.python.keras import Sequential
from tensorflow.python.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
x=x/255.0
model=Sequential()
model.add(Conv2D(64,(3,3), input_shape=x.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64,(3,3), input_shape=x.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(64))

model.add(Dense(20))
model.add(Activation('softmax'))




loss = tf.keras.losses.CategoricalCrossentropy()

lr = 1e-3
optimizer = tf.keras.optimizers.Adam(learning_rate=lr)

metrics = ['accuracy']

model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
Y= np.asarray(y)

model.fit(x,Y,batch_size=32,validation_split=0.1)

但我收到了这个错误：

ValueError: You are passing a target array of shape (1554, 1) while using as loss `categorical_crossentropy`. `categorical_crossentropy` expects targets to be binary matrices (1s and 0s) of shape (samples, classes). If your targets are integer classes, you can convert them to the expected format via:

x、 形状返回 1554,50,50,1

及 Y形返回

谢谢你的帮助！

---

1554，

您可能忘记了对标签数据进行热编码。如果标签是表示不同类的0到19之间的整数，则可以在keras.utils中使用to_category来相应地转换标签