python - 获取 TypeError:无法腌制 _thread.RLock 对象
问题描述
阅读许多类似的问题,其中大多数提到您不应该尝试序列化不可序列化的对象。我无法理解这个问题。我可以将模型保存为 .h5 文件,但这并不能达到我想要做的目的。请帮忙!
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'))
解决方案
我能够使用 Google Colab 在 TF 2.3.0 中复制您的问题
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)
输出:
---------------------------------------------------------------------------
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,建议在 github 中对此问题进行热修复以获取更多详细信息,请参阅此
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)
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