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.