I have trained a Sagemaker [NTM][1] model which is a neural topic model,
directly on the AWS sagemaker platform. Once training is complete you are able
to download the `mxnet` model files. Once unpacked the files contain:
- params
- symbol.json
- meta.json
I have followed the docs on mxnet to load the model and have the following code:
```python
sym, arg_params, aux_params = mx.model.load_checkpoint('model_algo-1', 0)
module_model = mx.mod.Module(symbol=sym, label_names=None, context=mx.cpu())
module_model.bind(
for_training=False,
data_shapes=[('data', (1, VOCAB_SIZE))]
)
module_model.set_params(arg_params=arg_params, aux_params=aux_params,
allow_missing=True) # must set allow missing true here or receive an error for
a missing n_epoch var
```
I now try and use the model for inference using:
```python
module_model.predict(x) # where x is a numpy array of size (1, VOCAB_SIZE)
```
The code runs, but the result is just a single value, where I expect a
distribution over topics:
```
[11.060672]
<NDArray 1 @cpu(0)>
```
Any help would be great!
[1]:
https://aws.amazon.com/blogs/machine-learning/introduction-to-the-amazon-sagemaker-neural-topic-model/
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