I have trained a Sagemaker NTM 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:
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:
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)>
EDIT:
I have tried to load it using the Symbol API, but still no luck:
import warnings
with warnings.catch_warnings():
warnings.simplefilter('ignore')
deserialized_net = gluon.nn.SymbolBlock.imports('model_algo-1-symbol.json', ['data'], 'model_algo-1-0000.params', ctx=mx.cpu())
Error:
AssertionError: Parameter 'n_epoch' is missing in file: model_algo-1-0000.params, which contains parameters: 'logsigma_bias', 'enc_0_bias', 'projection_bias', ..., 'enc_1_weight', 'enc_0_weight', 'mean_bias', 'logsigma_weight'. Please make sure source and target networks have the same prefix.
Any help would be great!
