Fixed a few warnings

hpvm/docs/components/keras-benchmarks.rst

@@ -90,11 +90,11 @@ The AlexNet source is commented with explanations on how to use the Keras fronte
 
 * 
   ``data_dir``: Directory to dump weights specified specified in
-  `constructor <https://gitlab.engr.illinois.edu/llvm/hpvm/-/blob/approx_hpvm_reorg_keras/hpvm/projects/keras/src/Benchmark.py#L21>`_.
+  `constructor <https://gitlab.engr.illinois.edu/llvm/hpvm/-/blob/approx_hpvm_reorg_keras/hpvm/projects/keras/src/Benchmark.py#L21>`__.
 
 * 
   ``src_dir``: Directory to dump ApproxHPVM sources in HPVM-C (C with HPVM compiler intrinsics) specified in
-  `constructor <https://gitlab.engr.illinois.edu/llvm/hpvm/-/blob/approx_hpvm_reorg_keras/hpvm/projects/keras/src/Benchmark.py#L22>`_.
+  `constructor <https://gitlab.engr.illinois.edu/llvm/hpvm/-/blob/approx_hpvm_reorg_keras/hpvm/projects/keras/src/Benchmark.py#L22>`__.
 
 * 
   ``num_classes``: number of output classes - dependent on the dataset used. For CIFAR10, ``num_classes`` is 10, CIFAR100 has 100 classes,

hpvm/docs/developerdocs/configuration-format.rst

@@ -15,7 +15,7 @@ such as "Convolution Layers" or "Dense Layer" as the API.
 File Format
 --------------
 
-.. code-block:: lang
+.. code-block:: text
 		
    +++++
    ${config_id} ${predicted_speedup} $predicted_energy} ${real_accuracy} ${accuracy_degration}

hpvm/projects/keras/docs/Support.rst

@@ -43,12 +43,12 @@ Example of what is NOT supported:
 
 .. code-block:: python
 
-   Conv2D(num_filters, kernel_size = (3,3), strides = (2,2), padding = `same`)
+   Conv2D(num_filters, kernel_size=(3, 3), strides=(2, 2), padding="same")
 
 Example of what works instead:
 
 .. code-block:: python
 
    # NOTE: Amount of padding varies with kernel sizes and strides
-   ZeroPadding2D(padding=(1,1), data_format = `channels_first`) # only support NCHW
-   Conv2D(num_filters, kernel_size = (3,3), strides = (2,2), padding = `valid`)
+   ZeroPadding2D(padding=(1, 1), data_format="channels_first") # only support NCHW
+   Conv2D(num_filters, kernel_size=(3, 3), strides=(2, 2), padding="valid")