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Diffstat (limited to 'tensorflow/docs_src/performance/quantization.md')
| -rw-r--r-- | tensorflow/docs_src/performance/quantization.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index 878371f674..86d2b92494 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -82,7 +82,7 @@ them directly is very convenient. ## How Can You Quantize Your Models? -TensorFlow has production-grade support for eight-bit calculations built it. It +TensorFlow has production-grade support for eight-bit calculations built in. It also has a process for converting many models trained in floating-point over to equivalent graphs using quantized calculations for inference. For example, here's how you can translate the latest GoogLeNet model into a version that uses @@ -153,7 +153,7 @@ bit. The min and max operations actually look at the values in the input float tensor, and then feeds them into the Dequantize operation that converts the -tensor into eight-bits. There's more details on how the quantized representation +tensor into eight-bits. There're more details on how the quantized representation works later on. Once the individual operations have been converted, the next stage is to remove |