TypeError alma: _thread.RLock nesneleri toplayamıyor

Question

Bir dizi benzer soruyu okuyun, çoğu serileştirilemeyen bir nesneyi serileştirmeye çalışmamanız gerektiğini belirtti. Sorunu anlayamıyorum. Modeli .h5 dosyası olarak kaydedebiliyorum, ancak bu yapmaya çalıştığım şeyin amacına hizmet etmiyor. Lütfen yardım et!

    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

Google Colab kullanarak sorununuzu TF 2.3.0'da tekrarlayabildim

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)

Çıktı:

---------------------------------------------------------------------------
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, daha fazla ayrıntı için github bu konuya önerilen sıcak düzeltme bakın lütfen bu

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)