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I made a stackoverflow question about this because it seems people had similar problem in the past https://stackoverflow.com/questions/48691449/typeerror-not-json-serializable-dimension2048.
When I try to save this particular model it gives TypeError: ('Not JSON Serializable:', Dimension(2048)).
I can save other models without a problem and I don't understand why this one doesn't work.
I tried to save it on Windows 10 with python_ver = 3.6, tensorflow_ver = 1.6-rc0 and Ubuntu 16.04 with python_ver = 3.6, tensorflow_ver = 1.3.
I created a model and trained it using code below.
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import InputLayer
from tensorflow.python.keras.layers import Dense
# Declare variables for model.
transfer_len = 2048
num_classes = 3
# Model creation.
model = Sequential()
# Input layer of shape 2048.
model.add(InputLayer(input_shape = (transfer_len,)))
# Fully connected 1024.
model.add(Dense(1024, activation='relu'))
# Output layer.
model.add(Dense(num_classes, activation='softmax'))
from tensorflow.python.keras.optimizers import Adam
optimizer = Adam(lr=1e-3)
model.compile(optimizer = optimizer,
loss = 'categorical_crossentropy',
metrics=['accuracy'])
model.fit(x = transfer_values_train,
y = labels_train,
epochs = 20, batch_size = 100, verbose=0)
When I try to save it it gives following error.
output_path = "model.keras"
model.save(output_path)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-22-6a252d3d7102> in <module>()
----> 1 model.save(output_path)
~\Anaconda3\envs\gpu\lib\site-packages\tensorflow\python\keras\_impl\keras\engine\topology.py in save(self, filepath, overwrite, include_optimizer)
1044 """
1045 from tensorflow.python.keras._impl.keras.models import save_model # pylint: disable=g-import-not-at-top
-> 1046 save_model(self, filepath, overwrite, include_optimizer)
1047
1048 def save_weights(self, filepath, overwrite=True):
~\Anaconda3\envs\gpu\lib\site-packages\tensorflow\python\keras\_impl\keras\models.py in save_model(model, filepath, overwrite, include_optimizer)
131 'config': model.get_config()
132 },
--> 133 default=get_json_type).encode('utf8')
134
135 model_weights_group = f.create_group('model_weights')
~\Anaconda3\envs\gpu\lib\json\__init__.py in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
236 check_circular=check_circular, allow_nan=allow_nan, indent=indent,
237 separators=separators, default=default, sort_keys=sort_keys,
--> 238 **kw).encode(obj)
239
240
~\Anaconda3\envs\gpu\lib\json\encoder.py in encode(self, o)
197 # exceptions aren't as detailed. The list call should be roughly
198 # equivalent to the PySequence_Fast that ''.join() would do.
--> 199 chunks = self.iterencode(o, _one_shot=True)
200 if not isinstance(chunks, (list, tuple)):
201 chunks = list(chunks)
~\Anaconda3\envs\gpu\lib\json\encoder.py in iterencode(self, o, _one_shot)
255 self.key_separator, self.item_separator, self.sort_keys,
256 self.skipkeys, _one_shot)
--> 257 return _iterencode(o, 0)
258
259 def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,
~\Anaconda3\envs\gpu\lib\site-packages\tensorflow\python\keras\_impl\keras\models.py in get_json_type(obj)
113 return obj.__name__
114
--> 115 raise TypeError('Not JSON Serializable:', obj)
116
117 from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
TypeError: ('Not JSON Serializable:', Dimension(2048)
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Offpics commentedon Feb 9, 2018
Tried with tensorflow_ver = 1.5.0, python_ver = 3.5.0 and it gives the same error.
Also changed the model to Functional and raises the same error.
Offpics commentedon Feb 9, 2018
Okay, so the transfer_len variable was a type 'tensorflow.python.framework.tensor_shape.Dimension'.
Changed to int and it saves normally.
Now the question is wheter it is possible to fix it in the save method.
ghost commentedon Apr 24, 2018
Hello!
I was able to fix this by changing
get_json_typehere:tensorflow/python/keras/_impl/keras/engine/saving.py.This is the function that's called from the
\tensorflow\python\keras\_impl\keras\models.pyfile that raised your error.Looks like the original function essentially if/else's the classes to find a JSON-friendly representation and raises a
TypeErrorif it can't.The following lines makes it happy, but I'm sure there's a much better, more principled fix. Anyone know what the best place to put code like this would be? I am more than happy to take a stab at it, just wondering where it would be at.
It seems like the standalone Keras fixed this when it
marshal.dump(...)s the object, not sure what the tf.Keras equivalent is.ArchieMeng commentedon Jun 10, 2018
Since Dimension object can be None like 'Dimension(None)'.A better fix can be:
ialhashim commentedon Sep 3, 2018
I still need to add the hacks suggested by @Clck @ArchieMeng . Why isn't this issue fixed?
gillesdegottex commentedon Sep 19, 2018
@Clck @ArchieMeng definitely works. it seems get_json_type() is in
engine/network.pythough.The question is how to make
from tensorflow.python.framework.tensor_shape import Dimensionbackend-independent. Which might explain why there has been no PR about this.cottrell commentedon Mar 27, 2019
Possibly related: #12473
fix typeerror when saving a model with some tf layers
fix typeerror when saving a model with some tf layers
fix typeerror when saving a model with some tf layers
sandippaul7204 commentedon Sep 15, 2020
I am having the same problem. I have added the code lines in Keras as suggested by you. It returns the same error.
ArchieMeng commentedon Oct 18, 2020
Well. I just went back to keras with Deep learning recently, and I used the model.save method as well. However, it doesn't raise the same error as I got many years ago, and it works pretty well.
So, how did you create your model?