Building model

buildilg_model.py

feature_extractor = hub.KerasLayer(MODULE_HANDLE,
                                   input_shape=IMAGE_SIZE+(3,),
                                   output_shape=[FV_SIZE])
do_fine_tuning = False #@param {type:"boolean"}
 if do_fine_tuning:
    feature_extractor.trainable = True
    # unfreeze some layers of base network for fine-tuning
    for layer in feature_extractor.layers[-30:]:
       layer.trainable =True
  
  else:
    feature_extractor.trainable = False

model = tf.keras.Sequential([
    feature_extractor,
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(rate=0.2),
    tf.keras.layers.Dense(train_generator.num_classes, activation='softmax',
                           kernel_regularizer=tf.keras.regularizers.l2(0.0001))
])