small fixes

install.sh

@@ -1,9 +1,12 @@
-conda create --copy --name hls4ml-env python=3.6
+conda create -y --copy --name hls4ml-env python=3.6
 
 source activate hls4ml-env
 
 ## CPU version of pytorch for now
 #conda install pytorch torchvision -c pytorch
-conda install pytorch-cpu torchvision-cpu -c pytorch
+conda install -y pytorch-cpu torchvision-cpu -c pytorch
 
-conda install -c anaconda scikit-learn h5py pyyaml
+conda install -y  -c anaconda scikit-learn h5py pyyaml
+
+# for plotting ROC curves, etc.
+pip install matplotlib

install_miniconda3.sh

@@ -1,3 +1,3 @@
-wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
-bash Miniconda3-latest-Linux-x86_64.sh
+wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O Miniconda3-latest-Linux-x86_64.sh
+bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda3
 export PATH="$CONDA_PREFIX/miniconda3/bin:$PATH"

keras-to-hls/extract_roc.py

@@ -52,18 +52,23 @@ def extract_roc(yamlConfig,opts):
         ## Expected AUC from keras
         efpr, etpr, ethreshold = roc_curve(truth_df[:,i],predict_df[:,i])
         eauc = auc(efpr, etpr)
+        print('Output %i: Keras auc = %.1f%%'%(i, eauc * 100))
         plt.plot(etpr,efpr,label='Keras auc = %.1f%%'%(eauc * 100))
 
         ## Expected AUC from HLS
         dfpr, dtpr, dthreshold = roc_curve(truth_df[:,i],output_df[:,i])
         dauc = auc(dfpr, dtpr)
+        print('Output %i: HLS auc = %.1f%%'%(i, dauc * 100))
+        print('Output {}: Ratio HLS/Keras = {:.2f}'.format(i, dauc / eauc))
         plt.plot(dtpr,dfpr,label='HLS auc = %.1f%%'%(dauc * 100))
         plt.plot([], [], ' ', label="Ratio HLS/Keras = {:.2f}".format(dauc / eauc))
 
         ## Expected AUC from FPGA run
         if opts.useFPGA!="":
          dfpr, dtpr, dthreshold = roc_curve(truth_df[:,i],fpga_output_df[:,i])
          fauc = auc(dfpr, dtpr)
+         print('Output %i: FPGA auc = %.1f%%'%(i, fauc * 100))
+         print('Output {}: Ratio FPGA/HLS = {:.2f}'.format(i, fauc / dauc))
          plt.plot(dtpr,dfpr,label='FPGA auc = %.1f%%'%(fauc * 100))
          plt.plot([], [], ' ', label="Ratio FPGA/HLS = {:.2f}".format(fauc / dauc))