I understand that we can not re-train the weights from a Frozen Model, but is there any way to load it, and add a layer and train the extra layers at least ?
Hi @Mah_Neh, you can able to add the layers to the base model and train the model. For example
#instantiate a base model with pre-trained weights. base_model = tf.keras.applications.Xception( weights='imagenet', # image shape = 128x128x3 input_shape=(128, 128, 3), include_top=False) # freeze layers base_model.trainable = False #create a new model on top. inputs = tf.keras.Input(shape=(150, 150, 3)) x = base_model(inputs, training=False) x =tf. keras.layers.GlobalAveragePooling2D()(x) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs)
Then you can compile and train your model. Thank You.
Yes, this is great, thank you for your reply.