«AttributeError: объект 'str' не имеет атрибута 'decode'» при загрузке сохраненной модели Keras ⇐ Python

[Anonymous]

После обучения я сохранил как всю модель Keras, так и только веса, используя

model.save_weights(MODEL_WEIGHTS) and model.save(MODEL_NAME)


Модели и веса были успешно сохранены, ошибок не было.
Я могу успешно загрузить веса, просто используя model.load_weights, и все готово, но когда я пытаюсь загрузить модель сохранения через load_model, я получаю сообщение об ошибке.

File "C:/Users/Rizwan/model_testing/model_performance.py", line 46, in
Model2 = load_model('nasnet_RS2.h5',custom_objects={'euc_dist_keras': euc_dist_keras})
File "C:\Users\Rizwan\AppData\Roaming\Python\Python36\site-packages\keras\engine\saving.py", line 419, in load_model
model = _deserialize_model(f, custom_objects, compile)
File "C:\Users\Rizwan\AppData\Roaming\Python\Python36\site-packages\keras\engine\saving.py", line 321, in _deserialize_model
optimizer_weights_group['weight_names']]
File "C:\Users\Rizwan\AppData\Roaming\Python\Python36\site-packages\keras\engine\saving.py", line 320, in
n.decode('utf8') for n in
AttributeError: 'str' object has no attribute 'decode'


Я никогда не получал этой ошибки и успешно загружал любые модели. Я использую Keras 2.2.4 с бэкэндом tensorflow. Python 3.6.
Мой код для обучения:

from keras_preprocessing.image import ImageDataGenerator
from keras import backend as K
from keras.models import load_model
from keras.callbacks import ReduceLROnPlateau, TensorBoard,
ModelCheckpoint,EarlyStopping
import pandas as pd

MODEL_NAME = "nasnet_RS2.h5"
MODEL_WEIGHTS = "nasnet_RS2_weights.h5"
def euc_dist_keras(y_true, y_pred):
return K.sqrt(K.sum(K.square(y_true - y_pred), axis=-1, keepdims=True))
def main():

# Here, we initialize the "NASNetMobile" model type and customize the final
#feature regressor layer.
# NASNet is a neural network architecture developed by Google.
# This architecture is specialized for transfer learning, and was discovered via Neural Architecture Search.
# NASNetMobile is a smaller version of NASNet.
model = NASNetMobile()
model = Model(model.input, Dense(1, activation='linear', kernel_initializer='normal')(model.layers[-2].output))

# model = load_model('current_best.hdf5', custom_objects={'euc_dist_keras': euc_dist_keras})

# This model will use the "Adam" optimizer.
model.compile("adam", euc_dist_keras)
lr_callback = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.003)
# This callback will log model stats to Tensorboard.
tb_callback = TensorBoard()
# This callback will checkpoint the best model at every epoch.
mc_callback = ModelCheckpoint(filepath='current_best_mem3.h5', verbose=1, save_best_only=True)
es_callback=EarlyStopping(monitor='val_loss', min_delta=0, patience=4, verbose=0, mode='auto', baseline=None, restore_best_weights=True)

# This is the train DataSequence.
# These are the callbacks.
#callbacks = [lr_callback, tb_callback,mc_callback]
callbacks = [lr_callback, tb_callback,es_callback]

train_pd = pd.read_csv("./train3.txt", delimiter=" ", names=["id", "label"], index_col=None)
test_pd = pd.read_csv("./val3.txt", delimiter=" ", names=["id", "label"], index_col=None)

# train_pd = pd.read_csv("./train2.txt",delimiter=" ",header=None,index_col=None)
# test_pd = pd.read_csv("./val2.txt",delimiter=" ",header=None,index_col=None)
#model.summary()
batch_size=32
datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = datagen.flow_from_dataframe(dataframe=train_pd,
directory="./images", x_col="id", y_col="label",
has_ext=True,
class_mode="other", target_size=(224, 224),
batch_size=batch_size)
valid_generator = datagen.flow_from_dataframe(dataframe=test_pd, directory="./images", x_col="id", y_col="label",
has_ext=True, class_mode="other", target_size=(224, 224),
batch_size=batch_size)

STEP_SIZE_TRAIN = train_generator.n // train_generator.batch_size
STEP_SIZE_VALID = valid_generator.n // valid_generator.batch_size
model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=valid_generator,
validation_steps=STEP_SIZE_VALID,
callbacks=callbacks,
epochs=20)

# we save the model.
model.save_weights(MODEL_WEIGHTS)
model.save(MODEL_NAME)
if __name__ == '__main__':
# freeze_support() here if program needs to be frozen
main()


Подробнее здесь: https://stackoverflow.com/questions/537 ... ng-a-keras