diff --git a/ddsm_train/Example_model_test.ipynb b/ddsm_train/Example_model_test.ipynb
new file mode 100644
index 0000000..8ada5b9
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+++ b/ddsm_train/Example_model_test.ipynb
@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import keras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Resnet & VGG16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from keras.models import load_model\n",
+    "res_mod = load_model('1152x896_s10_prt_addtopf2_1.h5', compile=False)\n",
+    "vgg_mod = load_model('1152x896_s10_prt_addvgg20.h5', compile=False)\n",
+    "hybrid_mod = load_model('1152x896_s10_prt_addvgg53.h5', compile=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 376 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dm_image import DMImageDataGenerator\n",
+    "test_imgen = DMImageDataGenerator(featurewise_center=True)\n",
+    "test_imgen.mean = 52.18\n",
+    "test_generator = test_imgen.flow_from_directory(\n",
+    "    './full_test_1152x896', target_size=(1152, 896), target_scale=None,\n",
+    "    rescale_factor=0.003891,\n",
+    "    equalize_hist=False, dup_3_channels=True, \n",
+    "    classes=['neg', 'pos'], class_mode='categorical', batch_size=4, \n",
+    "    shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.86367945  0.86367945]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dm_keras_ext import DMAucModelCheckpoint\n",
+    "res_auc, res_y_true, res_y_pred = DMAucModelCheckpoint.calc_test_auc(\n",
+    "    test_generator, res_mod, test_samples=test_generator.nb_sample, return_y_res=True)\n",
+    "print res_auc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.82536263  0.82536263]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dm_keras_ext import DMAucModelCheckpoint\n",
+    "vgg_auc, vgg_y_true, vgg_y_pred = DMAucModelCheckpoint.calc_test_auc(\n",
+    "    test_generator, vgg_mod, test_samples=test_generator.nb_sample, return_y_res=True)\n",
+    "print vgg_auc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.84914548  0.84914548]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dm_keras_ext import DMAucModelCheckpoint\n",
+    "hybrid_auc, hybrid_y_true, hybrid_y_pred = DMAucModelCheckpoint.calc_test_auc(\n",
+    "    test_generator, hybrid_mod, test_samples=test_generator.nb_sample, return_y_res=True)\n",
+    "print hybrid_auc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print np.array_equal(res_y_true, vgg_y_true)\n",
+    "print np.array_equal(res_y_true, hybrid_y_true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.88507827086\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_auc_score\n",
+    "all_mod_y_pred_avg = (res_y_pred[:,1] + vgg_y_pred[:,1] + hybrid_y_pred[:,1])/3\n",
+    "print roc_auc_score(res_y_true[:,1], all_mod_y_pred_avg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.87832831  0.87832831]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dm_keras_ext import DMAucModelCheckpoint\n",
+    "res_auc_aug, res_y_true_aug, res_y_pred_aug = DMAucModelCheckpoint.calc_test_auc(\n",
+    "    test_generator, res_mod, test_samples=test_generator.nb_sample, return_y_res=True, test_augment=True)\n",
+    "print res_auc_aug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.85919862  0.85919862]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dm_keras_ext import DMAucModelCheckpoint\n",
+    "vgg_auc_aug, vgg_y_true_aug, vgg_y_pred_aug = DMAucModelCheckpoint.calc_test_auc(\n",
+    "    test_generator, vgg_mod, test_samples=test_generator.nb_sample, return_y_res=True, test_augment=True)\n",
+    "print vgg_auc_aug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.88237829  0.88237829]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dm_keras_ext import DMAucModelCheckpoint\n",
+    "hybrid_auc_aug, hybrid_y_true_aug, hybrid_y_pred_aug = DMAucModelCheckpoint.calc_test_auc(\n",
+    "    test_generator, hybrid_mod, test_samples=test_generator.nb_sample, return_y_res=True, test_augment=True)\n",
+    "print hybrid_auc_aug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.907827086026\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_auc_score\n",
+    "all_mod_y_pred_avg_aug = (res_y_pred_aug[:,1] + vgg_y_pred_aug[:,1] + hybrid_y_pred_aug[:,1])/3\n",
+    "print roc_auc_score(res_y_true_aug[:,1], all_mod_y_pred_avg_aug)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Done."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}