是否有人得到“ AttributeError: ‘ str’对象没有属性‘ decode’”，同时加载一个 Kera 保存的模型

经过训练，我保存两个克拉整个模型和只有重量使用

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

成功地保存了模型和权重，没有错误。 我可以简单地使用 model. load _ weight 成功地加载权重，它们都很好，但是当我试图通过 load _ model 加载保存模型时，我得到了一个错误。

File "C:/Users/Rizwan/model_testing/model_performance.py", line 46, in <module>
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 <listcomp>
n.decode('utf8') for n in
AttributeError: 'str' object has no attribute 'decode'

我从来没有收到这个错误，我用来成功加载任何模型。我使用 Kera 2.2.4与张量流后端。Python 3.6. 我的训练守则是:

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()