message: Fix two links in Quantization-aware training page.
diff: @@ -10,7 +10,7 @@ For efficient inference, TensorFlow combines batch normalization with the preced
  convolutional and fully-connected layers prior to quantization by
  [folding batch norm layers](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/quantize/python/fold_batch_norms.py){:.external}.
  -The quantization error is modeled using [fake quantization](../api_guides/python/array_ops.md#Fake_quantization)
  +The quantization error is modeled using [fake quantization](../../api_guides/python/array_ops.md#Fake_quantization)
  nodes to simulate the effect of quantization in the forward and backward passes. The
  forward-pass models quantization, while the backward-pass models quantization as a
  straight-through estimator. Both the forward- and backward-pass simulate the quantization
  @@ -105,7 +105,7 @@ toco \
  --std_value=127.5 --mean_value=127.5
  ```
  -See the documentation for `tf.contrib.quantize` and [TensorFlow Lite](../lite/).
  +See the documentation for `tf.contrib.quantize` and [TensorFlow Lite](../../lite/).
  ## Quantized accuracy results
  