diff --git a/converters/pb2nnet.py b/converters/pb2nnet.py
index 47510ee..92b780b 100644
--- a/converters/pb2nnet.py
+++ b/converters/pb2nnet.py
@@ -21,7 +21,7 @@ def processGraph(op,input_op, foundInputFlag, weights, biases):
         (bool): Updated foundInputFlag
     '''
     
-    if op.node_def.op=='Const':
+    if op.node_def.op=='Const' and op.outputs[0].consumers()[0].type == 'Identity' :
         # If constant, extract values and add to weight or bias list depending on shape
         param = tensor_util.MakeNdarray(op.node_def.attr['value'].tensor)
         if len(param.shape)>1:
@@ -40,9 +40,8 @@ def processGraph(op,input_op, foundInputFlag, weights, biases):
         else:
             foundInputFlag = True
     return foundInputFlag
-
         
-def pb2nnet(pbFile, inputMins=None, inputMaxes=None, means=None, ranges=None, nnetFile="", inputName="", outputName="", savedModel=False, savedModelTags=[]):
+def pb2nnet(pbFile, inputMins=None, inputMaxes=None, means=None, ranges=None, nnetFile="", inputName="", outputName="", savedModel=False, savedModelTags=[], order="xW"):
     '''
     Write a .nnet file from a frozen Tensorflow protobuf or SavedModel
 
@@ -63,6 +62,11 @@ def pb2nnet(pbFile, inputMins=None, inputMaxes=None, means=None, ranges=None, nn
     if nnetFile=="":
         nnetFile = pbFile[:-2] + 'nnet'
 
+    sess = pb2sess(pbFile,inputName="", outputName="", savedModel=False, savedModelTags=[])
+
+    FFTF2nnet(sess, inputMins, inputMaxes, means, ranges, order, nnetFile, inputName, outputName)
+
+def pb2sess(pbFile,inputName="", outputName="", savedModel=False, savedModelTags=[]):
     if savedModel:
         ### Read SavedModel ###
         sess = tf.Session()
@@ -84,7 +88,18 @@ def pb2nnet(pbFile, inputMins=None, inputMaxes=None, means=None, ranges=None, nn
             tf.import_graph_def(graph_def, name="")
         sess = tf.Session(graph=graph)
         ### END reading protobuf ###
+    return sess
 
+def FFTF2nnet(sess, inputMins, inputMaxes, means, ranges, order, nnetFile="", inputName="", outputName=""):
+    weights, biases, inputSize = FFTF2W(sess, inputName, outputName)
+    # Default values for input bounds and normalization constants
+    if inputMins is None: inputMins = inputSize*[np.finfo(np.float32).min]
+    if inputMaxes is None: inputMaxes = inputSize*[np.finfo(np.float32).max]
+    if means is None: means = (inputSize+1)*[0.0]
+    if ranges is None: ranges = (inputSize+1)*[1.0]
+    writeNNet(weights,biases,inputMins,inputMaxes,means,ranges,order, nnetFile)
+
+def FFTF2W(sess, inputName="", outputName=""):
     ### Find operations corresponding to input and output ###
     if inputName:
         inputOp = sess.graph.get_operation_by_name(inputName)
@@ -111,18 +126,32 @@ def pb2nnet(pbFile, inputMins=None, inputMaxes=None, means=None, ranges=None, nn
     assert(len(inputShape)==2)
     inputSize = inputShape[1]
     if foundInputFlag:
-        
-        # Default values for input bounds and normalization constants
-        if inputMins is None: inputMins = inputSize*[np.finfo(np.float32).min]
-        if inputMaxes is None: inputMaxes = inputSize*[np.finfo(np.float32).max]
-        if means is None: means = (inputSize+1)*[0.0]
-        if ranges is None: ranges = (inputSize+1)*[1.0]
-            
-        # Write NNet file
-        writeNNet(weights,biases,inputMins,inputMaxes,means,ranges,nnetFile)
+        return weights, biases, inputSize
     else:
         print("Could not find the given input in graph: %s"%inputOp.name)
 
+
+def pb2W(pbFile, inputName="", outputName="", savedModel=False, savedModelTags=[]):
+    sess = pb2sess(pbFile,inputName, outputName, savedModel, savedModelTags)
+    weights, biases, inputSize = FFTF2W(sess, inputName, outputName)
+    return weights, biases
+
+def test():
+    ## Script showing how to run pb2nnet
+    # Min and max values used to bound the inputs
+    inputMins  = [0.0,-3.141593,-3.141593,100.0,0.0]
+    inputMaxes = [60760.0,3.141593,3.141593,1200.0,1200.0]
+
+    # Mean and range values for normalizing the inputs and outputs. All outputs are normalized with the same value
+    means  = [1.9791091e+04,0.0,0.0,650.0,600.0,7.5188840201005975]
+    ranges = [60261.0,6.28318530718,6.28318530718,1100.0,1200.0,373.94992]
+
+    # Tensorflow pb file to convert to .nnet file
+    pbFile = 'NNet/nnet/TestNetwork2.pb'
+
+    # Convert the file
+    pb2nnet(pbFile, inputMins, inputMaxes, means, ranges, order="Wx", nnetFile="NNet/nnet/TestNetwork2_converted.nnet")
+
 if __name__ == '__main__':
     # Read user inputs and run pb2nnet function
     # If non-default values of input bounds and normalization constants are needed, this function should be run from a script
diff --git a/utils/writeNNet.py b/utils/writeNNet.py
index fbf1412..069bf1a 100644
--- a/utils/writeNNet.py
+++ b/utils/writeNNet.py
@@ -1,6 +1,6 @@
 import numpy as np
 
-def writeNNet(weights,biases,inputMins,inputMaxes,means,ranges,fileName):
+def writeNNet(weights,biases,inputMins,inputMaxes,means,ranges,order,fileName):
     '''
     Write network data to the .nnet file format
 
@@ -40,7 +40,12 @@ def writeNNet(weights,biases,inputMins,inputMaxes,means,ranges,fileName):
 
         #Extract the necessary information and write the header information
         numLayers = len(weights)
-        inputSize = weights[0].shape[1]
+        if order == 'xW':
+            inputSize = weights[0].shape[0]
+        elif order == 'Wx':
+            inputSize = weights[0].shape[1]
+        else:
+            raise NotImplementedError
         outputSize = len(biases[-1])
         maxLayerSize = inputSize
         
@@ -58,6 +63,7 @@ def writeNNet(weights,biases,inputMins,inputMaxes,means,ranges,fileName):
         f2.write("0,\n") #Unused Flag
 
         # Write Min, Max, Mean, and Range of each of the inputs and outputs for normalization
+        import pdb; pdb.set_trace()
         f2.write(','.join(str(inputMins[i])  for i in range(inputSize)) + ',\n') #Minimum Input Values
         f2.write(','.join(str(inputMaxes[i]) for i in range(inputSize)) + ',\n') #Maximum Input Values                
         f2.write(','.join(str(means[i])      for i in range(inputSize+1)) + ',\n') #Means for normalizations
@@ -70,11 +76,24 @@ def writeNNet(weights,biases,inputMins,inputMaxes,means,ranges,fileName):
         # The pattern is repeated by next writing the weights from the first hidden layer to the second hidden layer,
         # followed by the biases of the second hidden layer.
         ##################
-        for w,b in zip(weights,biases):
-            for i in range(w.shape[0]):
+        if order == 'xW':
+            for w,b in zip(weights,biases):
                 for j in range(w.shape[1]):
-                    f2.write("%.5e," % w[i][j]) #Five digits written. More can be used, but that requires more more space.
-                f2.write("\n")
-                
-            for i in range(len(b)):
-                f2.write("%.5e,\n" % b[i]) #Five digits written. More can be used, but that requires more more space.
+                    for i in range(w.shape[0]):
+                        f2.write("%.5e," % w[i][j]) #Five digits written. More can be used, but that requires more more space.
+                    f2.write("\n")
+                    
+                for i in range(len(b)):
+                    f2.write("%.5e,\n" % b[i]) #Five digits written. More can be used, but that requires more more space.
+        elif order == 'Wx':
+            for w,b in zip(weights,biases):
+                for i in range(w.shape[0]):
+                    for j in range(w.shape[1]):
+                        f2.write("%.5e," % w[i][j])
+                    f2.write("\n")
+                for i in range(len(b)):
+                    f2.write("%.5e,\n" % b[i])
+        else: 
+            raise NotImplementedError
+
+