Keras model.evaluate()失败

model = Sequential()
optimizer = Adam()

model.add(Lambda(lambda x: x / 127.5 - 1., input_shape=(28, 28, 1)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(NUM_CLASSES, activation='softmax'))

model.compile(optimizer=optimizer, loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])

我正在用形状数据训练它

X_train.shape = (48000, 28, 28, 1)
X_val.shape = (12000, 28, 28, 1)

不过,我现在想用 keras.evaluate() 功能:

score = trained_model.evaluate(X_test, y_test, batch_size=128)
# X_test.shape = (10000, 28, 28, 1)
# y_test.shape (10000,)

导致以下错误:

ValueError: Error when checking target: expected dense_2 to have shape (10,) but got array with shape (1,)

我不太理解这个错误,因为我在训练、验证和测试集中使用相同的形状。

你介意解释一下我的错误是什么,以及如何改正吗?

非常感谢!

编辑:输出 trained_model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
lambda_1 (Lambda)            (None, 28, 28, 1)         0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 26, 26, 64)        640
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 64)        0
_________________________________________________________________
dropout_1 (Dropout)          (None, 13, 13, 64)        0
_________________________________________________________________
flatten_1 (Flatten)          (None, 10816)             0
_________________________________________________________________
dense_1 (Dense)              (None, 128)               1384576
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0
_________________________________________________________________
dense_2 (Dense)              (None, 10)                1290
=================================================================
Total params: 1,386,506
Trainable params: 1,386,506
Non-trainable params: 0

意见中给出的解决方案
我忘了再唱一次我的歌 y_train y_val 和 y_test 解决方法:

from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train)