如何在多输入模型中停止训练CNN部分，同时继续训练ANN部分

Question

我在Keras中制作了一个多输入模型，该模型采用图像shape=[N, 640, 480, 3]和数值数据shape=[N, 19]，并对12个类进行预测。以下是定义代码部分的模型：

# # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# #                       MODEL === CNN
# # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#
base_model = keras.applications.ResNet50(
weights='imagenet',  # Load weights pre-trained on ImageNet.
input_shape=(640, 480, 3),
include_top=False)  # Do not include the ImageNet classifier at the top.
base_model.trainable = False
input_Cnn = keras.Input(shape=(640, 480, 3))
x = base_model(input_Cnn, training=False)
# Convert features of shape `base_model.output_shape[1:]` to vectors
x = keras.layers.GlobalAveragePooling2D()(x)
# A Dense classifier with a single unit (binary classification)
x1 = keras.layers.Dense(1024, activation="relu")(x)
out_Cnn = keras.layers.Dense(12, activation="relu")(x1)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#                       MODEL === NN
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
inp_num = keras.layers.Input(shape=(19,))  # no. of columns of the numerical data
fc1 = keras.layers.Dense(units=2 ** 6, activation="relu")(inp_num)
fc2 = keras.layers.Dense(units=2 ** 8, activation="relu")(fc1)
fc3 = keras.layers.Dense(units=2 ** 10, activation="relu")(fc2)
fc4 = keras.layers.Dense(units=2 ** 8, activation="relu")(fc3)
fc5 = keras.layers.Dense(units=2 ** 6, activation="relu")(fc4)
out_NN = keras.layers.Dense(12, activation="relu")(fc5)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#                       CONCATENATION
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
result = keras.layers.concatenate((out_Cnn, out_NN), axis=-1)  # [N, 12] --- concatenate [N, 12] ==> [N, 24]
result = keras.layers.Dense(1024, activation='relu')(result)
result = keras.layers.Dense(units=12, activation="softmax")(result)
model = keras.Model([input_Cnn, inp_num], result)
print(model.summary())

问题是CNN部分(如果独立训练(在较少的时期中训练，而ANN部分(如果单独训练(花费更长的时间(更多的时期(。但在这段代码中，当两者结合在一起时，准确率不会超过10%。是否有任何方法可以在一定数量的历元之后阻止梯度流入CNN部分，以便在该模型之后只训练ANN部分？

Answer 1

我不使用keras，但在谷歌上快速搜索后，答案应该是：你可以冻结图层，这样某些参数就无法再学习了：

# this freezes the first N layers
for layer in model.layers[:N]:
layer.trainable = False

其中，N是您拥有的卷积层的数量。