Data Science Asked by Aaron Jones on January 7, 2021
For a project that I’m working on, I have created a simple model in TensorFlow that consists of a dense features layer followed by three dense layers.
def build_model(arguments):
model = tf.keras.Sequential([
tf.keras.layers.DenseFeatures(arguments),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(5, activation='sigmoid')
])
return model
I am unable to go into more detail about the parameter arguments, but the above model function works perfectly fine and can train and save a .h5 file perfectly fine using the code below.
# Create a path for the saving location of the model
model_dir = log_dir + "model.h5"
# Save the model
model.save(model_dir)
However, when I try to load the model back from the .h5 file,
model = tf.keras.models.load_model(model_path)
I get the following error message.
File "sampleModel.py", line 342, in <module>
model = tf.keras.models.load_model(model_path)
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkerassavingsave.py", line 1
82, in load_model
return hdf5_format.load_model_from_hdf5(filepath, custom_objects, c
ompile)
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkerassavinghdf5_format.py",
line 178, in load_model_from_hdf5
custom_objects=custom_objects)
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkerassavingmodel_config.py"
, line 55, in model_from_config
return deserialize(config, custom_objects=custom_objects)
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkeraslayersserialization.py
", line 175, in deserialize
printable_module_name='layer')
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkerasutilsgeneric_utils.py"
, line 358, in deserialize_keras_object
list(custom_objects.items())))
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkerasenginesequential.py",
line 487, in from_config
custom_objects=custom_objects)
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkeraslayersserialization.py
", line 175, in deserialize
printable_module_name='layer')
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkerasutilsgeneric_utils.py"
, line 358, in deserialize_keras_object
list(custom_objects.items())))
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonkerasfeature_columnbase_fea
ture_layer.py", line 141, in from_config
config['feature_columns'], custom_objects=custom_objects)
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonfeature_columnserialization.
py", line 186, in deserialize_feature_columns
for c in configs
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonfeature_columnserialization.
py", line 186, in <listcomp>
for c in configs
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonfeature_columnserialization.
py", line 138, in deserialize_feature_column
columns_by_name=columns_by_name)
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonfeature_columnfeature_column
_v2.py", line 2622, in from_config
config['normalizer_fn'], custom_objects=custom_objects)
File "C:WINDOWSsystem32configsystemprofileAppDataRoamingPython
Python37site-packagestensorflowpythonfeature_columnserialization.
py", line 273, in _deserialize_keras_object
obj = module_objects.get(object_name)
AttributeError: 'NoneType' object has no attribute 'get'
Looking around, I suspect it has something to do with the custom_objects tag for the load_model function, but I am not 100% sure of how to implement it.
After looking around some more and digging through some Github issues, I believe I've figured out away around the issue. For my specific situation, I didn't need to save the entire model, rather than just the weights. For my configuration I'm using Tensorflow 2.3.0 and Keras 2.4.3.
SHORT ANSWER:
Fit your model for at least one epoch, then load in the weights.
LONG ANSWER:
To save the weights, I use the following function appended with my model file path above it.
# Create a path for the saving location of the model
model_dir = dir_path + '/model.h5'
# Save the model
model.save_weights(model_dir)
I first build my model from my question above and store it in a model object
model = build_model(arguments)
I add my loss function and optimizer, then compile my model to make sure it has all relevant features before loading in the weights.
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
#Declare and set the parametors of the optimizer
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, decay=0.001)
#Compile the model
model.compile(loss=loss_object, optimizer=optimizer, metrics=['accuracy'])
I found my answer from this line here, but at the very bottom it says to fit the model for 1 epoch before loading in the weights.
history = model.fit(test_data, batch_size=1, epochs=1)
Afterwards, you should be able to load in the weights just fine using the function below.
model.load_weights(model_path)
Correct answer by Aaron Jones on January 7, 2021
Try this link https://github.com/keras-team/keras/issues/11418
There is an answer by ethanfowler who seems to solve the issue by using custom_objects
Answered by John on January 7, 2021
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