Hello,
Is there an existing flow for running just an evaluation dataset and getting mean_edit_distance/WER
from the frozen_graph provided?
I am attempting to replace the LSTMBlockFusedCell operation with my own custom LSTMCell implementation for an experiment. I would like to not have to implement a gradient calculation for my custom kernel since I am using the pre-trained model provided and only care about the evaluation WER. I am currently using --initialize_from_frozen_model
and some string manipulation to shove in the pre-trained weights into my custom kernel.
However, the in-memory graph still contains all of the training nodes / the optimizer, which is causing errors when the gradients are being calculated for my custom kernel. Ideally I could run my experiment by using tensorflow’s graph_editor
to insert my custom kernel without having any additional gradient calculations.
Command I am currently running:
python DeepSpeech.py --notrain --test --initialize_from_frozen_model=models/output_graph.pb -display_step=1 --epoch 1 --test_files=~/test.csv --dev_files=~/dev.csv --train_files=~/train.csv --limit_test=5 --log_level 0