Fix graph_transform documentation:

  - remove \ from within strings
  - remove :0 from inputs and outputs, so fold_constants works
  - make sure fold_(old_)batch_norms runs before quantize_weigths
    and round_weights.
Change: 153728959

1 files changed, 54 insertions, 42 deletions

diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md
index df9191fb96..c9f0a9b222 100644
--- a/tensorflow/tools/graph_transforms/README.md
+++ b/tensorflow/tools/graph_transforms/README.md
@@ -136,15 +136,14 @@ bazel build tensorflow/tools/graph_transforms:transform_graph
 bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
 --in_graph=tensorflow_inception_graph.pb \
 --out_graph=optimized_inception_graph.pb \
---inputs='Mul:0' \
---outputs='softmax:0' \
---transforms='\
-strip_unused_nodes(type=float, shape="1,299,299,3") \
-remove_nodes(op=Identity, op=CheckNumerics) \
-fold_constants(ignore_errors=true) \
-fold_batch_norms \
-fold_old_batch_norms\
-'
+--inputs='Mul' \
+--outputs='softmax' \
+--transforms='
+  strip_unused_nodes(type=float, shape="1,299,299,3")
+  remove_nodes(op=Identity, op=CheckNumerics)
+  fold_constants(ignore_errors=true)
+  fold_batch_norms
+  fold_old_batch_norms'
 ```
 
 The batch norm folding is included twice because there are two different flavors
@@ -177,14 +176,13 @@ bazel build tensorflow/tools/graph_transforms:transform_graph
 bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
 --in_graph=tensorflow_inception_graph.pb \
 --out_graph=optimized_inception_graph.pb \
---inputs='Mul:0' \
---outputs='softmax:0' \
---transforms='\
-strip_unused_nodes(type=float, shape="1,299,299,3") \
-fold_constants(ignore_errors=true) \
-fold_batch_norms \
-fold_old_batch_norms\
-'
+--inputs='Mul' \
+--outputs='softmax' \
+--transforms='
+  strip_unused_nodes(type=float, shape="1,299,299,3")
+  fold_constants(ignore_errors=true)
+  fold_batch_norms
+  fold_old_batch_norms'
 ```
 
 ### Shrinking File Size
@@ -212,11 +210,14 @@ bazel build tensorflow/tools/graph_transforms:transform_graph
 bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
 --in_graph=tensorflow_inception_graph.pb \
 --out_graph=optimized_inception_graph.pb \
---inputs='Mul:0' \
---outputs='softmax:0' \
+--inputs='Mul' \
+--outputs='softmax' \
 --transforms='\
-round_weights(num_steps=256) \
-'
+  strip_unused_nodes(type=float, shape="1,299,299,3")
+  fold_constants(ignore_errors=true)
+  fold_batch_norms
+  fold_old_batch_norms
+  round_weights(num_steps=256)'
 ```
 
 You should see that the `optimized_inception_graph.pb` output file is the same
@@ -236,11 +237,14 @@ bazel build tensorflow/tools/graph_transforms:transform_graph
 bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
 --in_graph=tensorflow_inception_graph.pb \
 --out_graph=optimized_inception_graph.pb \
---inputs='Mul:0' \
---outputs='softmax:0' \
---transforms='\
-quantize_weights \
-'
+--inputs='Mul' \
+--outputs='softmax' \
+--transforms='
+  strip_unused_nodes(type=float, shape="1,299,299,3")
+  fold_constants(ignore_errors=true)
+  fold_batch_norms
+  fold_old_batch_norms
+  quantize_weights'
 ```
 
 You should see that the size of the output graph is about a quarter of the
@@ -263,9 +267,8 @@ bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
 --out_graph=optimized_inception_graph.pb \
 --inputs='Mul:0' \
 --outputs='softmax:0' \
---transforms='\
-obfuscate_names \
-'
+--transforms='
+  obfuscate_names'
 ```
 
 ### Eight-bit Calculations
@@ -280,17 +283,19 @@ bazel build tensorflow/tools/graph_transforms:transform_graph
 bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
 --in_graph=tensorflow_inception_graph.pb \
 --out_graph=optimized_inception_graph.pb \
---inputs='Mul:0' \
---outputs='softmax:0' \
+--inputs='Mul' \
+--outputs='softmax' \
 --transforms='
- add_default_attributes
- strip_unused_nodes(type=float, shape="1,299,299,3")
- remove_nodes(op=Identity, op=CheckNumerics)
- fold_old_batch_norms
- quantize_weights
- quantize_nodes
- strip_unused_nodes
- sort_by_execution_order'
+  add_default_attributes
+  strip_unused_nodes(type=float, shape="1,299,299,3")
+  remove_nodes(op=Identity, op=CheckNumerics)
+  fold_constants(ignore_errors=true)
+  fold_batch_norms
+  fold_old_batch_norms
+  quantize_weights
+  quantize_nodes
+  strip_unused_nodes
+  sort_by_execution_order'
 ```
 
 This process converts all the operations in the graph that have eight-bit
@@ -446,7 +451,7 @@ bazel-bin/tensorflow/examples/label_image/label_image \
 --input_layer=Mul \
 --output_layer=softmax \
 --graph=/tmp/logged_quantized_inception.pb \
---labels=learning/brain/models/image/inception_v3/imagenet_comp_graph_label_strings.txt \
+--labels=${HOME}/Downloads/imagenet_comp_graph_label_strings.txt \
 --logtostderr \
 2>/tmp/min_max_log_small.txt
 ```
@@ -580,7 +585,10 @@ Converts any large (more than 15 element) float Const op into an eight-bit
 equivalent, followed by a float conversion op so that the result is usable by
 subsequent nodes. This is mostly useful for [shrinking file
 sizes](#shrinking-file-size), but also helps with the more advanced
-[quantize_nodes](#quantize_nodes) transform.
+[quantize_nodes](#quantize_nodes) transform. Even though there are no
+prerequesites, it is advisable to run [fold_batch_norms](#fold_batch_norms) or
+[fold_old_batch_norms](#fold_old_batch_norms), because rounding variances down
+to zero may cause significant loss of precision.
 
 ### remove_attribute
 
@@ -665,7 +673,11 @@ Rounds all float values in large Const ops (more than 15 elements) to the given
 number of steps. The unique values are chosen per buffer by linearly allocating
 between the largest and smallest values present. This is useful when you'll be
 deploying on mobile, and you want a model that will compress effectively. See
-[shrinking file size](#shrinking-file-size) for more details.
+[shrinking file size](#shrinking-file-size) for more details. Even though there
+are no prerequesites, it is advisable to run
+[fold_batch_norms](#fold_batch_norms) or
+[fold_old_batch_norms](#fold_old_batch_norms), because rounding variances down
+to zero may cause significant loss of precision.
 
 ### sparsify_gather