Update Keras README.md

hpvm/projects/keras/README.md

@@ -42,6 +42,8 @@ python setup.py install
 
 Benchmarks under `./src/` 
 
+**NOTE:** Activate conda environment (above) before running benchmarks 
+
 List of benchmarks and the expected accuracies:
 
 | Benchmark       | Accuracy    |
@@ -57,12 +59,11 @@ List of benchmarks and the expected accuracies:
 | VGG16-CIFAR100 | 66.50 |
 | VGG16-ImageNet | 69.46 |
 
-Activate conda environment (above) before running benchmarks 
 
 ### Synopsis
 
 ```
-python src/${BENCH_NAME}.py  [hpvm_reload|keras_reload]  [frontend|keras_dump] 
+python src/${BENCH_NAME}.py  [hpvm_reload|keras_reload]  [frontend] 
 
 ```
 
@@ -72,30 +73,45 @@ python src/${BENCH_NAME}.py  [hpvm_reload|keras_reload]  [frontend|keras_dump]
 
 `keras_reload`: Reloads weights in Keras `.h5` file format 
 
-`frontend`: Invokes the HPVM frontend and dumps weights in directory specified in constructor
-
-`keras_dump`: Dumps keras .h5 format model weights in directory specified in constructor
+`frontend`: Invokes the HPVM frontend and dumps weights (in HPVM `.bin` format) in directory specified
 
 
 
 ### Building New Benchmarks 
 
 All benchmarks inherit from the commom parent `Benchmark` class. 
-Each benchmark overrides virtual functions for building the model, training, inference, 
+Each benchmark overrides virtual functions for building the model, training, 
 and data preprocessing. 
         
     
 `def buildModel(self)`:
-returns a keras model
+Constructs and returns a keras model
 
 `def data_preprocess(self)`:
-returns X_train, y_train, X_test, y_test, X_tuner, and y_tuner data — in that order; this data will be directly used later for training and inference
+returns X_train, y_train, X_test, y_test, X_tuner, and y_tuner data (in that order): 
+These are described here:
+
+* `X_train:` Training data (fp32) in NCHW format
+* `y_train:` Training labels (int32)
+
+* `X_test:` Testing/Evaluation data in NCHW format
+* `y_test:` Testing/Evaluation labels
+
+* `X_tuner:` Data to be used for autotuning 
+* `y_tuner:` Labels corresponding to tuning data
+
 
 `def trainModel(self, model, X_train, y_train, X_test, y_test)`:
-returns a trained keras model
+Trains the Keras model constructed in `buildModel` and is expected to return the 
+trained keras model - training parameters should be tuned here.
 
 
+## Limitations 
 
+* We mosty support convolutional neural networks - RNNs/LSTMS are not supported
+* We currently only support models in NCHW format (NHWC is not supported)
+* Softmax operator should be the last operation in the CNN pipeline 
+* Softmax operation must be a separate operator (not specified as activation to another type of Keras operator)