Does Any one got "AttributeError: 'str' object has no attribute 'decode' " , while Loading a Keras Saved Model

Question

After Training, I saved Both Keras whole Model and Only Weights using

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

Models and Weights were saved successfully and there was no error. I can successfully load the weights simply using model.load_weights and they are good to go, but when i try to load the save model via load_model, i am getting an error.

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' 

I never received this error and i used to load any models successfully. I am using Keras 2.2.4 with tensorflow backend. Python 3.6. My Code for training is :

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

For me the solution was downgrading the h5py package (in my case to 2.10.0), apparently putting back only Keras and Tensorflow to the correct versions was not enough.