Moving example to examples/ directory and renaming driver filese

tf/edgeml/bonsai/bonsaiTrainer.py

@@ -328,19 +328,24 @@ class BonsaiTrainer:
             self.bonsaiObj.sigmaI = oldSigmaI
             sys.stdout.flush()
 
-        # sigmaI has to be set to infinity to ensure only a single path is used in inference
+        # sigmaI has to be set to infinity to ensure
+        # only a single path is used in inference
         self.bonsaiObj.sigmaI = 1e9
-        print("Maximum Test accuracy at compressed model size(including early stopping): " +
+        print("Maximum Test accuracy at compressed" +
+              " model size(including early stopping): " +
               str(maxTestAcc) + " at Epoch: " +
-              str(maxTestAccEpoch + 1) + "\nFinal Test Accuracy: " + str(testAcc))
+              str(maxTestAccEpoch + 1) + "\nFinal Test" +
+              " Accuracy: " + str(testAcc))
         print("\nNon-Zeros: " + str(self.getModelSize()[1]) + " Model Size: " +
               str(float(self.getModelSize()[1]) / 1024.0) + " KB hasSparse: " +
               str(self.getModelSize()[2]) + "\n")
 
         resultFile.write("MaxTestAcc: " + str(maxTestAcc) +
-                         " at Epoch(totalEpochs): " + str(maxTestAccEpoch + 1) +
+                         " at Epoch(totalEpochs): " +
+                         str(maxTestAccEpoch + 1) +
                          "(" + str(totalEpochs) + ")" + " ModelSize: " +
                          str(float(self.getModelSize()[1]) / 1024.0) +
                          " KB hasSparse: " + str(self.getModelSize()[2]) +
-                         " Param Directory: " + str(os.path.abspath(currDir)) + "\n")
+                         " Param Directory: " +
+                         str(os.path.abspath(currDir)) + "\n")
         resultFile.close()

tf/edgeml/examples/bonsai_example.py

@@ -1,4 +1,4 @@
-import bonsaiPreProcess
+import bonsaipreprocess
 import tensorflow as tf
 import numpy as np
 from bonsaiTrainer import BonsaiTrainer
@@ -10,7 +10,7 @@ tf.set_random_seed(42)
 np.random.seed(42)
 
 # Hyper Param pre-processing
-args = bonsaiPreProcess.getArgs()
+args = bonsaipreprocess.getArgs()
 
 sigma = args.sigma
 depth = args.depth
@@ -28,7 +28,7 @@ learningRate = args.learningRate
 data_dir = args.data_dir
 
 (dataDimension, numClasses,
-    Xtrain, Ytrain, Xtest, Ytest) = bonsaiPreProcess.preProcessData(data_dir)
+    Xtrain, Ytrain, Xtest, Ytest) = bonsaipreprocess.preProcessData(data_dir)
 
 sparZ = args.sZ
 
@@ -61,7 +61,7 @@ if numClasses == 2:
 X = tf.placeholder("float32", [None, dataDimension])
 Y = tf.placeholder("float32", [None, numClasses])
 
-currDir = bonsaiPreProcess.createDir(data_dir)
+currDir = bonsaipreprocess.createDir(data_dir)
 
 # numClasses = 1 for binary case
 bonsaiObj = Bonsai(numClasses, dataDimension,

tf/edgeml/examples/bonsaipreprocess.py

@@ -1,10 +1,12 @@
+'''
+ Functions to check sanity of input arguments
+ for the example script.
+'''
 import argparse
 import numpy as np
 import datetime
 import os
 
-# Functions to check sanity of input arguments
-
 
 def checkIntPos(value):
     ivalue = int(value)
@@ -45,7 +47,8 @@ def getArgs():
     parser = argparse.ArgumentParser(
         description='HyperParams for Bonsai Algorithm')
     parser.add_argument('-dir', '--data_dir', required=True,
-                        help='Data directory containing train.npy and test.npy')
+                        help='Data directory containing' +
+                        'train.npy and test.npy')
 
     parser.add_argument('-d', '--depth', type=checkIntNneg, default=2,
                         help='Depth of Bonsai Tree (default: 2 try: [0, 1, 3])')