Train or Fine-tune Your Model
Train your model using your training data and compare the accuracy with the original 32-bit network. The training process varies by model.
To fine-tune an existing model using quantization, load the weights from your trained model into a graph that you use the create_training_graph() function to prepare. The create_training_graph() function is described in the Prerequisites section. When you have loaded the weights, allow the model to continue training for a smaller number of steps.
When the training is complete, compare the accuracy with the original 32-bit network.
For CifarNet, you can use the following commands to download the training data to /tmp/cifar10 and train the network for 100000 steps:
cd models/research/slim/ bash scripts/train_cifarnet_on_cifar10.sh
The fake quantization layers that tf.contrib.quantize.create_training_graph() adds become active after 90000 steps and ensure that the final network is fine-tuned to work with quantized weights. The training process creates a /tmp/cifarnet-model directory that contains the graph and checkpoint weights.
To view the training progress:
-
Run the following command into TensorBoard:
tensorboard --logdir=/tmp/cifarnet-model/
- Open port 6006 on the training server in a browser. If you’re training on your laptop or desktop, this is http://localhost:6006/.
Training takes approximately 40 minutes on a p3.2xlarge EC2 instance.
The final accuracy is approximately 85% for CifarNet.