G’day & Namaste

Greatly appreciate the opportunity to submit a query on this forum.

My deepest gratitude to everyone in the Community. Especially the team marshalling the lite effort - your efforts are already giving hope to amputees hoping for low-energy powered protheses in India.

My first attempt at TensorFlow here is to convert a HiddenMarkovModel from core to lite. It’s my first, so a simple cloudy-day rainy-day example was considered. However, the following error is being encountered:

if not signature_keys:

raise ValueError(“Only support at least one signature key.”)

My code is as follows (& hopfully correct!)

```
import numpy as np
import tensorflow.compat.v2 as tf
tf.enable_v2_behavior()
import tensorflow_probability as tfp
from tensorflow_probability import distributions as tfd
from matplotlib import pylab as plt
%matplotlib inline
import scipy.stats
tfd = tfp.distributions
initial_distribution = tfd.Categorical(probs=[0.8, 0.2])
transition_distribution = tfd.Categorical(probs=[[0.7, 0.3],
[0.2, 0.8]])
observation_distribution = tfd.Normal(loc=[0., 15.], scale=[5., 10.])
hmm = tfd.HiddenMarkovModel(
initial_distribution=initial_distribution,
transition_distribution=transition_distribution,
observation_distribution=observation_distribution,
num_steps=7)
# Save the model.
tf.saved_model.save(hmm, '/sample_data/hmm')
# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model('/sample_data/hmm')
tflite_model = converter.convert()
```

Any & all advice would be useful, I am certain.

Regards,

g-sobers