Keras ValueError trying to load model - tensorflow

Question

I am using Anaconda Navigator, Jupyter to be precised.
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
>>> 1.14.0
This is my model
def create_model():
model = tf.keras.Sequential([
keras.layers.Dense(86, activation='relu', kernel_regularizer=keras.regularizers.l2(0.0001),input_shape=(129,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(142, activation='relu', kernel_regularizer=keras.regularizers.l2(0.0001)),
keras.layers.Dropout(0.2),
keras.layers.Dense(4, activation='softmax')
])
return model
model = create_model()
# Display the model's architecture
model.summary()
After training,predicting and evaluating my model, I decided to save it using
model.save('/Users/Jennifer/myproject/my_model.h5')
I checked the directory and folder with the h5py file. And I decided to load it using
new_model1 = tf.keras.models.load_model('/Users/Jennifer/myproject/my_model.h5')
I got an Error
ValueError: Unknown entries in loss dictionary: ['class_name', 'config']. Only expected following keys: ['dense_17']
Please help me. What should I do? I have almost spent the whole day trying to solve this issue. Thanks

Answer 1

Here is a bit of a work around that just loads the weights:
#!/usr/bin/env python3
from tensorflow import keras
import os
def create_model():
model = keras.Sequential([
keras.layers.Dense(86, activation='relu', kernel_regularizer=keras.regularizers.l2(0.0001),input_shape=(129,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(142, activation='relu', kernel_regularizer=keras.regularizers.l2(0.0001)),
keras.layers.Dropout(0.2),
keras.layers.Dense(4, activation='softmax')
])
return model
if os.path.exists("junk.h5"):
model = create_model()
model.load_weights("junk.h5")
else:
model = create_model()
model.compile(optimizer=keras.optimizers.Adam(0.0001), loss=keras.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
model.save("junk.h5")
Another workaround would be to save the model without the optimizer
model.save("junk.h5", include_optimizer=False)
It looks like the loss function you're using creates a dictionary that has invalid keys. This sounds like a bug in keras/tensorflow. That is why the colab one probably worked because it was using a newer version.