keras报错Error when checking target: expected dense_1 to have shape (5,) but got array with shape (1,) 报错

【keras报错Error when checking target: expected dense_1 to have shape (5,) but got array with shape (1,)】ValueError: Error when checking target: expected dense_1 to have shape (5,) but got array with shape (1,)
描述：五分类
原因： One-hot 编码的原因
解决方法：添加相关代码

from keras.utils import np_utilstrain_label3 = np.load('./label.npy') test_label3 = np.load('./test_label.npy')nb_classes3 = 5 train_label3 = np_utils.to_categorical(train_label3, nb_classes3) test_label3 = np_utils.to_categorical(test_label3, nb_classes3)

最好还添加一下：

train_data3 = train_data3.astype('float32') # 数据归一化 test_data3 = test_data3.astype('float32') train_data3 /= 255 test_data3 /= 255

完整代码：

from keras.utils import np_utilstrain_label3 = np.load('./label.npy') test_label3 = np.load('./test_label.npy')nb_classes3 = 5 train_label3 = np_utils.to_categorical(train_label3, nb_classes3) test_label3 = np_utils.to_categorical(test_label3, nb_classes3) train_data3 = train_data3.astype('float32') # 数据归一化 test_data3 = test_data3.astype('float32') train_data3 /= 255 test_data3 /= 255

另一种解决方案：

将loss='categorical_crossentropy' 改为loss='sparse_categorical_crossentropy'

categorical_crossentropy 和 sparse_categorical_crossentropy 的区别
如果是 one-hot 编码，则使用 categorical_crossentropy
one-hot 编码：[0, 0,1], [1, 0, 0], [0, 1, 0]
如果你的 tagets 是数字编码，用sparse_categorical_crossentropy
数字编码：2, 0, 1