Mendapatkan TypeError: tidak bisa membuat acar objek _thread.RLock

Question

Baca sejumlah pertanyaan serupa, kebanyakan dari mereka menyebutkan bahwa Anda tidak boleh mencoba membuat serial objek yang tidak dapat diserialisasi. Saya tidak dapat memahami masalahnya. Saya dapat menyimpan model sebagai file .h5 tetapi itu tidak memenuhi tujuan dari apa yang saya coba lakukan. Tolong bantu!

    def image_generator(train_data_dir, test_data_dir):
        train_datagen = ImageDataGenerator(rescale=1/255,
                                          rotation_range = 30,  
                                          zoom_range = 0.2, 
                                          width_shift_range=0.1,  
                                          height_shift_range=0.1,
                                          validation_split = 0.15)
      
        test_datagen = ImageDataGenerator(rescale=1/255)
        
        train_generator = train_datagen.flow_from_directory(train_data_dir,
                                      target_size = (160,160),
                                      batch_size = 32,
                                      class_mode = 'categorical',
                                      subset='training')
        
        val_generator = train_datagen.flow_from_directory(train_data_dir,
                                      target_size = (160,160),
                                      batch_size = 32,
                                      class_mode = 'categorical',
                                      subset = 'validation')
        
        test_generator = test_datagen.flow_from_directory(test_data_dir,
                                     target_size=(160,160),
                                     batch_size = 32,
                                     class_mode = 'categorical')
        return train_generator, val_generator, test_generator
    
    def model_output_for_TL (pre_trained_model, last_output):    
        x = Flatten()(last_output)
        
        # Dense hidden layer
        x = Dense(512, activation='relu')(x)
        x = Dropout(0.2)(x)
        
        # Output neuron. 
        x = Dense(2, activation='softmax')(x)
        
        model = Model(pre_trained_model.input, x)
        
        return model
    
    train_generator, validation_generator, test_generator = image_generator(train_dir,test_dir)
    
    pre_trained_model = InceptionV3(input_shape = (160, 160, 3), 
                                    include_top = False, 
                                    weights = 'imagenet')
    for layer in pre_trained_model.layers:
      layer.trainable = False
    last_layer = pre_trained_model.get_layer('mixed5')
    last_output = last_layer.output
    model_TL = model_output_for_TL(pre_trained_model, last_output)
    
    model_TL.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    
    history_TL = model_TL.fit(
          train_generator,
          steps_per_epoch=10,  
          epochs=10,
          verbose=1,
          validation_data = validation_generator)
    
    pickle.dump(model_TL,open('img_model.pkl','wb'))

Answer 1

Saya dapat mereplikasi masalah Anda di TF 2.3.0 menggunakan Google Colab

import pickle
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(1, input_dim=42, activation='sigmoid'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

with open('model.pkl', 'wb') as f:
    pickle.dump(model, f)

Keluaran:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-afb2bf58a891> in <module>()
      8 
      9 with open('model.pkl', 'wb') as f:
---> 10     pickle.dump(model, f)

TypeError: can't pickle _thread.RLock objects

@adriangb, mengusulkan perbaikan terbaru untuk masalah ini di github untuk detail lebih lanjut, silakan lihat ini

import pickle

from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense
from tensorflow.python.keras.layers import deserialize, serialize
from tensorflow.python.keras.saving import saving_utils


def unpack(model, training_config, weights):
    restored_model = deserialize(model)
    if training_config is not None:
        restored_model.compile(
            **saving_utils.compile_args_from_training_config(
                training_config
            )
        )
    restored_model.set_weights(weights)
    return restored_model

# Hotfix function
def make_keras_picklable():

    def __reduce__(self):
        model_metadata = saving_utils.model_metadata(self)
        training_config = model_metadata.get("training_config", None)
        model = serialize(self)
        weights = self.get_weights()
        return (unpack, (model, training_config, weights))

    cls = Model
    cls.__reduce__ = __reduce__

# Run the function
make_keras_picklable()

# Create the model
model = Sequential()
model.add(Dense(1, input_dim=42, activation='sigmoid'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Save
with open('model.pkl', 'wb') as f:
    pickle.dump(model, f)