“属性错误：”str“对象没有属性“解码”； ”加载 Keras 保存的模型时

Question

训练后，我保存了 Keras 整个模型，并且仅使用

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

模型和权重保存了成功，并且没有错误。 我可以简单地使用 model.load_weights 成功加载权重，并且它们很好用，但是当我尝试通过 load_model 加载保存模型时，我收到错误。

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'

我从未收到此错误，并且我曾经成功加载任何模型。我正在使用带有张量流后端的 Keras 2.2.4。 Python 3.6。 我的培训代码是：

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()

Answer 1

对我来说，解决方案是将 h5py 包降级（在我的例子中降级为 2.10.0），显然仅将 Keras 和 Tensorflow 恢复到正确的版本是不够的。

Answer 2

我使用以下命令降级了我的 h5py 软件包，

pip install 'h5py==2.10.0' --force-reinstall

重新启动了我的 ipython 内核并且它工作了。

Answer 3

对我来说，这是优于我之前版本的 h5py 版本。
通过设置为 2.10.0 修复了该问题。

Answer 4

使用以下命令降级 h5py 包来解决问题，

pip install h5py==2.10.0 --force-reinstall

Answer 5

使用 TF 格式文件而不是 h5py 保存：save_format='tf'。就我而言：

model.save_weights("NMT_model_weight.tf",save_format='tf')

Answer 6

我遇到了同样的问题，解决了将 compile=False 放入 load_model 中：

model_ = load_model('path to your model.h5',custom_objects={'Scale': Scale()}, compile=False)
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model_.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])

Answer 7

这可能是由于从不同版本的 keras 保存的模型造成的。从 keras 2.2.6 加载由tensorflow.keras（我认为与 tf 1.12 的 keras 2.1.6 类似）生成的模型时，我遇到了同样的问题。

您可以使用 model.load_weights 加载权重，并从您要使用的 keras 版本重新保存完整模型。

Answer 8

对我有用的解决方案是：

pip3 uninstall keras
pip3 uninstall tensorflow
pip3 install --upgrade pip3
pip3 install tensorflow
pip3 install keras

Answer 9

在我的环境中使用tensorflow==2.4.1、h5py==2.1.0和python 3.8后，我仍然遇到此错误。
解决这个问题的方法是将 python 版本降级到 3.6.9

Answer 10

降级 python、tensorflow、keras 和 h5py 解决了该问题。

python -> 3.6.2

pip install tensorflow==1.3.0
pip install keras==2.1.2
pip install 'h5py==2.10.0' --force-reinstall