我试图在 CNN 上的 tf.keras 中调用 model.predict() 来预测单个图像的类别。出于某种原因，类概率返回高于 1，这是荒谬的。我不确定为什么会发生这种情况。以下是我训练 CNN 的方法：
class_names = ['Angry','Disgust','Fear','Happy','Sad','Surprise','Neutral']
model = models.Sequential()
model.add(layers.Conv2D(64, (3, 3), activation='relu', input_shape=(48, 48, 1), kernel_regularizer=tf.keras.regularizers.l1(0.01)))
model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Dropout(0.5))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Dropout(0.5))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.summary()
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(7))
#model.summary()
model.compile(optimizer='adam',loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),metrics=['accuracy'])
lr_reducer = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=3) #monitors the validation loss for signs of a plateau and then alter the learning rate by the specified factor if a plateau is detected
early_stopper …