diff --git a/for_investigation.ipynb b/for_investigation.ipynb
new file mode 100644
index 0000000..3e3e95e
--- /dev/null
+++ b/for_investigation.ipynb
@@ -0,0 +1,188 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Model\n",
+    "from keras.layers import Input, Dense, Dropout, Activation, BatchNormalization\n",
+    "from keras.optimizers import SGD, Adam\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filename = 'test_set.csv'\n",
+    "raw_data = open(filename, 'rt')\n",
+    "test_data = np.loadtxt(raw_data, delimiter= '\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "number_of_features = 1126\n",
+    "test_set = test_data[:,0:1126]\n",
+    "test_labels = test_data[:,1126] #last column consists of labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Neural network configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "inputs = Input(shape=(number_of_features,))\n",
+    "output_from_1st_layer = Dense(1000, activation='relu')(inputs)\n",
+    "output_from_1st_layer = Dropout(0.5)(output_from_1st_layer)\n",
+    "output_from_1st_layer = BatchNormalization()(output_from_1st_layer)\n",
+    "output_from_2nd_layer = Dense(500, activation='relu')(output_from_1st_layer)\n",
+    "output_from_2nd_layer = Dropout(0.5)(output_from_2nd_layer)\n",
+    "output_from_2nd_layer = BatchNormalization()(output_from_2nd_layer)\n",
+    "output = Dense(1, activation='sigmoid')(output_from_2nd_layer)\n",
+    "\n",
+    "model = Model(inputs, output)\n",
+    "model.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's load weights learnt earlier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model.load_weights(\"weights_2017_05_10.h5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, calculate predictions for test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    " predictions = model.predict(test_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can calculate basic metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:0.7316259444607988\n",
+      "Precision: 0.7378337531486147\n",
+      "Recall: 0.7185752134236091\n",
+      "F1: 0.7280771525154107\n"
+     ]
+    }
+   ],
+   "source": [
+    "    true_positives = 0.0\n",
+    "    false_positives = 0.0\n",
+    "    true_negatives = 0.0\n",
+    "    false_negatives = 0.0\n",
+    "\n",
+    "    for i in range(len(test_set)):\n",
+    "        if (predictions[i]<0.5 and test_labels[i]==0): true_negatives += 1     \n",
+    "        if (predictions[i]<0.5 and test_labels[i]==1): false_negatives += 1\n",
+    "        if (predictions[i]>=0.5 and test_labels[i]==1): true_positives += 1\n",
+    "        if (predictions[i]>=0.5 and test_labels[i]==0): false_positives += 1  \n",
+    "    \n",
+    "    accuracy = (true_positives+true_negatives)/len(test_set)\n",
+    "    precision = true_positives/(true_positives+false_positives)\n",
+    "    recall = true_positives/(true_positives+false_negatives)\n",
+    "    f1 = 2*(precision*recall)/(precision+recall)\n",
+    "\n",
+    "    print ('Accuracy:' + repr(accuracy))\n",
+    "    print ('Precision: ' + repr(precision))\n",
+    "    print ('Recall: ' + repr(recall))\n",
+    "    print ('F1: ' + repr(f1))"
+   ]
+  }
+ ],
+ "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": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}