Running Model Analysis - 2.0 English

Now that you have prepared a trained checkpoint and a GraphDef file, you can start the pruning process. Run the following shell scripts to call the vai_p_tensorflow functions.

WORKSPACE=./models
BASELINE_GRAPH=${WORKSPACE}/mnist.pbtxt
BASELINE_CKPT=${WORKSPACE}/train/model.ckpt
INPUT_NODES="image"
OUTPUT_NODES="logits/add"
action=ana

vai_p_tensorflow \
  --action=${action} \
  --input_graph=${BASELINE_GRAPH} \
  --input_ckpt=${BASELINE_CKPT} \
  --eval_fn_path=low_level_cnn.py \
  --target="accuracy" \
  --max_num_batches=100 \
  --workspace=${WORKSPACE} \
  --input_nodes="${INPUT_NODES}" \
  --input_node_shapes="1,28,28,1" \
  --output_nodes=\"${OUTPUT_NODES}\"

The output log is as shown below:

INFO:tensorflow:Starting evaluation at 2019-01-09-08:43:15
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from ./models/train/model.ckpt
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [80/100]
INFO:tensorflow:Evaluation [90/100]
INFO:tensorflow:Evaluation [100/100]
INFO:tensorflow:Finished evaluation at 2019-01-09-08:43:21