Установка Keras Transfer-Learning для Layers.trainable значения True не имеет никакого эффекта. ⇐ Python

[Гость]

I want to finetune efficientnet using tf.keras (tensorflow 2.3) but i cannot change the training status of layers properly. My model looks like this:

data_augmentation_layers = tf.keras.Sequential([ keras.layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"), keras.layers.experimental.preprocessing.RandomRotation(0.8)]) efficientnet = EfficientNetB3(weights="imagenet", include_top=False, input_shape=(*img_size, 3)) #Setting to not trainable as described in the standard keras FAQ efficientnet.trainable = False inputs = keras.layers.Input(shape=(*img_size, 3)) augmented = augmentation_layers(inputs) base = efficientnet(augmented, training=False) pooling = keras.layers.GlobalAveragePooling2D()(base) outputs = keras.layers.Dense(5, activation="softmax")(pooling) model = keras.Model(inputs=inputs, outputs=outputs) model.compile(loss="categorical_crossentropy", optimizer=keras_opt, metrics=["categorical_accuracy"]) This is done so that my random weights on the custom top wont destroy the weights asap.

Model: "functional_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_2 (InputLayer) [(None, 512, 512, 3)] 0 _________________________________________________________________ sequential (Sequential) (None, 512, 512, 3) 0 _________________________________________________________________ efficientnetb3 (Functional) (None, 16, 16, 1536) 10783535 _________________________________________________________________ global_average_pooling2d (Gl (None, 1536) 0 _________________________________________________________________ dense (Dense) (None, 5) 7685 ================================================================= Total params: 10,791,220 Trainable params: 7,685 Non-trainable params: 10,783,535 Everything seems to work until this point. I train my model for 2 epochs and then i want to start fine-tuning the efficientnet base. Thus i call

for l in model.get_layer("efficientnetb3").layers: if not isinstance(l, keras.layers.BatchNormalization): l.trainable = True model.compile(loss="categorical_crossentropy", optimizer=keras_opt, metrics=["categorical_accuracy"]) I recompiled and print the summary again to see that the number of non-trainable weights remained the same. Also fitting does not bring better results that keeping frozen.

dense (Dense) (None, 5) 7685 ================================================================= Total params: 10,791,220 Trainable params: 7,685 Non-trainable params: 10,783,535 Ps: I also tried efficientnet3.trainable = True but this also had no effect.

Could it be that it has something to do with the fact that i'm using a sequential and a functional model at the same time?


Источник: https://stackoverflow.com/questions/659 ... -no-effect