Merge pull request #479 from onnx/prasanthpul-patch-1

Update README.md

Author: guschmue

README.md

@@ -1,14 +1,14 @@
 tf2onnx - convert TensorFlow models to ONNX models.
 ========
 
-![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test?branchName=master)
+[![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test?branchName=master)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build?definitionId=16&branchName=master)
 
 # Supported ONNX version
-tensorflow-onnx will use the onnx version installed on your system and installs the latest onnx version if none is found.
+tensorflow-onnx will use the ONNX version installed on your system and installs the latest ONNX version if none is found.
 
-By default we use opset 7 for the resulting onnx graph since most runtimes will support opset 7. Opset 7 was introduced in onnx-1.2.
+By default we use opset 7 for the resulting ONNX graph since most runtimes will support opset 7. Opset 7 was introduced in onnx-1.2.
 
-With the release of onnx-1.3 there is now opset 8 - to create an onnx graph for opset 8 use in the command line ```--opset 8```.
+Newer releases of ONNX support higher opsets. For example, to create an ONNX graph for opset 8 use in the command line ```--opset 8```.
 
 # Status
 We support many TensorFlow models. Support for Fully Connected and Convolutional networks is mature. Dynamic LSTM/GRU/Attention networks should work but the code for this is evolving. 
@@ -73,17 +73,16 @@ python -m tf2onnx.convert
     --graphdef SOURCE_GRAPHDEF_PB
     --checkpoint SOURCE_CHECKPOINT
     --saved-model SOURCE_SAVED_MODEL
+    [--output TARGET_ONNX_MODEL]
     [--inputs GRAPH_INPUTS]
     [--outputs GRAPH_OUTPUS]
     [--inputs-as-nchw inputs_provided_as_nchw]
+    [--opset OPSET]
     [--target TARGET]
-    [--output TARGET_ONNX_GRAPH]
-    [--target TARGET]
-    [--continue_on_error]
-    [--verbose]
     [--custom-ops list-of-custom-ops]
-    [--opset OPSET]
     [--fold_const]
+    [--continue_on_error]
+    [--verbose]
 ```
 
 ## Parameters
@@ -100,10 +99,10 @@ the target onnx file path.
 Tensorflow model's input/output names, which can be found with [summarize graph tool](#summarize_graph). Those names typically end on ```:0```, for example ```--inputs input0:0,input1:0```. inputs and outputs are ***not*** needed for models in saved-model format.
 ### --inputs-as-nchw
 By default we preserve the image format of inputs (nchw or nhwc) as given in the TensorFlow model. If your hosts (for example windows) native format nchw and the model is written for nhwc, ```--inputs-as-nchw``` tensorflow-onnx will transpose the input. Doing so is convinient for the application and the converter in many cases can optimize the transpose away. For example ```--inputs input0:0,input1:0 --inputs-as-nchw input0:0``` assumes that images are passed into ```input0:0``` as nchw while the TensorFlow model given uses nhwc.
-### --target 
-Some runtimes need workarounds, for example they don't support all types given in the onnx spec. We'll workaround it in some cases by generating a different graph. Those workarounds are activated with ```--target TARGET```. 
 ### --opset
-by default we uses the newest opset 7 to generate the graph. By specifieing ```--opset``` the user can override the default to generate a graph with the desired opset. For example ```--opset 5``` would create a onnx graph that uses only ops available in opset 5. Because older opsets have in most cases fewer ops, some models might not convert on a older opset.
+By default we use the opset 7 to generate the graph. By specifying ```--opset``` the user can override the default to generate a graph with the desired opset. For example ```--opset 5``` would create a onnx graph that uses only ops available in opset 5. Because older opsets have in most cases fewer ops, some models might not convert on a older opset.
+### --target 
+Some models require special handling to run on some runtimes. In particular, the model may use unsupported data types. Workarounds are activated with ```--target TARGET```. Currently supported values are listed on this [wiki](https://github.com/onnx/tensorflow-onnx/wiki/target). If your model will be run on Windows ML, you should specify the appropriate target value.
 ### --custom-ops
 the runtime may support custom ops that are not defined in onnx. A user can asked the converter to map to custom ops by listing them with the --custom-ops option. Tensorflow ops listed here will be mapped to a custom op with the same name as the tensorflow op but in the onnx domain ai.onnx.converters.tensorflow. For example: ```--custom-ops Print``` will insert a op ```Print``` in the onnx domain ```ai.onnx.converters.tensorflow``` into the graph. We also support a python api for custom ops documented later in this readme. 
 ### --fold_const
@@ -163,7 +162,7 @@ optional arguments:
   --config           yaml config file
   --verbose          verbose output, option is additive
   --opset OPSET      target opset to use
-  --perf csv-file    capture performance numbers or tensorflow and onnx runtime
+  --perf csv-file    capture performance numbers for tensorflow and onnx runtime
   --debug            dump generated graph with shape info
   --fold_const when set, TensorFlow fold_constants transformation will be applied before conversion. This will benefit features including Transpose optimization (e.g. Transpose operations introduced during tf-graph-to-onnx-graph conversion will be removed), and RNN unit conversion (for example LSTM).
 ```