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-rw-r--r--tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md4
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
index 495014c6fc..7680cdd344 100644
--- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
+++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md
@@ -115,7 +115,7 @@ bazel run --config=opt \
In order to evaluate the possible benefit of generating a quantized graph, TOCO
allows "dummy-quantization" on float graphs. The flags `--default_ranges_min`
-and `--default_ranges_max` accept plausable values for the min-max ranges of the
+and `--default_ranges_max` accept plausible values for the min-max ranges of the
values in all arrays that do not have min-max information. "Dummy-quantization"
will produce lower accuracy but will emulate the performance of a correctly
quantized model.
@@ -338,7 +338,7 @@ below outline the use cases for each.
### Using `--output_format=GRAPHVIZ_DOT`
The first way to get a graphviz rendering is to pass `GRAPHVIZ_DOT` into
-`--output_format`. This results in a plausable visualization of the graph. This
+`--output_format`. This results in a plausible visualization of the graph. This
reduces the requirements that normally exist during conversion between other
input and output formats. For example, this may be useful if conversion from
TENSORFLOW_GRAPHDEF to TFLITE is failing.