model = keras.models.Sequential([
#第一组卷积
keras.layers.Conv2D(filters=32, kernel_size=3, padding=‘same’,
activation=‘relu’, input_shape=[width, height, channels]),
keras.layers.SeparableConv2D(filters=32, kernel_size=3, padding=‘same’,
activation=‘relu’),
keras.layers.MaxPool2D(pool_size=2),
#第二组翻倍
keras.layers.SeparableConv2D(filters=64, kernel_size=3, padding=‘same’,
activation=‘relu’),
keras.layers.SeparableConv2D(filters=64, kernel_size=3, padding=‘same’,
activation=‘relu’),
keras.layers.MaxPool2D(pool_size=2),
#第三组再翻倍
keras.layers.SeparableConv2D(filters=128, kernel_size=3, padding=‘same’,
activation=‘relu’),
keras.layers.SeparableConv2D(filters=128, kernel_size=3, padding=‘same’,
activation=‘relu’),
keras.layers.MaxPool2D(pool_size=2),
keras.layers.Flatten(),
#展平后，和全连接层做连接
keras.layers.Dense(128, activation=‘relu’),
keras.layers.Dense(num_classes, activation=‘softmax’),
])

model.compile(loss=“categorical_crossentropy”,
optimizer=“adam”, metrics=[‘accuracy’])
model.summary()

老师，把10-monkeys的代码变为深度可分离卷积后，准确率直接变为了一个固定的值，是什么原因

准确率只有0.1172