Quantization
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Quantization-aware model training ensures that the forward pass matches precision for both training and inference. There are two aspects to this:
- Operator fusion at inference time are accurately modeled at training time.
- Quantization effects at inference are modeled at training time.
For efficient inference, TensorFlow combines batch normalization with the preceding convolutional and fully-connected layers prior to quantization by folding batch norm layers.
