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diff --git a/scripts/tensorflow_data_extractor.py b/scripts/tensorflow_data_extractor.py
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+#!/usr/bin/env python
+"""Extracts trainable parameters from Tensorflow models and stores them in numpy arrays.
+Usage
+    python tensorflow_data_extractor -m path_to_binary_checkpoint_file -n path_to_metagraph_file
+
+Saves each variable to a {variable_name}.npy binary file.
+
+Note that since Tensorflow version 0.11 the binary checkpoint file which contains the values for each parameter has the format of:
+    {model_name}.data-{step}-of-{max_step}
+instead of:
+    {model_name}.ckpt
+When dealing with binary files with version >= 0.11, only pass {model_name} to -m option;
+when dealing with binary files with version < 0.11, pass the whole file name {model_name}.ckpt to -m option.
+
+Also note that this script relies on the parameters to be extracted being in the
+'trainable_variables' tensor collection. By default all variables are automatically added to this collection unless
+specified otherwise by the user. Thus should a user alter this default behavior and/or want to extract parameters from other
+collections, tf.GraphKeys.TRAINABLE_VARIABLES should be replaced accordingly.
+
+Tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3.4.3.
+"""
+import argparse
+import numpy as np
+import os
+import tensorflow as tf
+
+
+if __name__ == "__main__":
+    # Parse arguments
+    parser = argparse.ArgumentParser('Extract Tensorflow net parameters')
+    parser.add_argument('-m', dest='modelFile', type=str, required=True, help='Path to Tensorflow checkpoint binary\
+            file. For Tensorflow version >= 0.11, only include model name; for Tensorflow version < 0.11, include\
+            model name with ".ckpt" extension')
+    parser.add_argument('-n', dest='netFile', type=str, required=True, help='Path to Tensorflow MetaGraph file')
+    args = parser.parse_args()
+
+    # Load Tensorflow Net
+    saver = tf.train.import_meta_graph(args.netFile)
+    with tf.Session() as sess:
+        # Restore session
+        saver.restore(sess, args.modelFile)
+        print('Model restored.')
+        # Save trainable variables to numpy arrays
+        for t in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
+            varname = t.name
+            if os.path.sep in t.name:
+                varname = varname.replace(os.path.sep, '_')
+                print("Renaming variable {0} to {1}".format(t.name, varname))
+            print("Saving variable {0} with shape {1} ...".format(varname, t.shape))
+            # Dump as binary
+            np.save(varname, sess.run(t))