diff --git a/cnn/CNN_application.ipynb b/cnn/CNN_application.ipynb
index 0a806b0..b299574 100644
--- a/cnn/CNN_application.ipynb
+++ b/cnn/CNN_application.ipynb
@@ -52,16 +52,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'1.13.1'"
+       "'1.2.0-rc0'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -400,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,18 +449,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From /mnt/c/Users/Allah/Documents/courses/main/ml_course/lib/python3.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use keras.layers.flatten instead.\n",
-      "Z3 = [[ 1.4416984  -0.24909666  5.450499   -0.2618962  -0.20669907  1.3654671 ]\n",
-      " [ 1.4070846  -0.02573211  5.08928    -0.48669922 -0.40940708  1.2624859 ]]\n"
+      "Z3 = [[-0.44670227 -1.5720876  -1.5304923  -2.3101304  -1.2910438   0.46852064]\n",
+      " [-0.17601591 -1.5797201  -1.4737016  -2.616721   -1.0081065   0.5747785 ]]\n"
      ]
     }
    ],
@@ -511,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -538,22 +535,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From <ipython-input-11-a4a3e74fb158>:16: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "\n",
-      "Future major versions of TensorFlow will allow gradients to flow\n",
-      "into the labels input on backprop by default.\n",
-      "\n",
-      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
-      "\n",
-      "cost = 4.6648693\n"
+      "cost = 2.9103396\n"
      ]
     }
    ],
@@ -614,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -742,38 +731,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cost after epoch 0: 1.921332\n",
-      "Cost after epoch 5: 1.904156\n",
-      "Cost after epoch 10: 1.904309\n",
-      "Cost after epoch 15: 1.904477\n",
-      "Cost after epoch 20: 1.901876\n",
-      "Cost after epoch 25: 1.784077\n",
-      "Cost after epoch 30: 1.681052\n",
-      "Cost after epoch 35: 1.618207\n",
-      "Cost after epoch 40: 1.597972\n",
-      "Cost after epoch 45: 1.566707\n",
-      "Cost after epoch 50: 1.554486\n",
-      "Cost after epoch 55: 1.502187\n",
-      "Cost after epoch 60: 1.461035\n",
-      "Cost after epoch 65: 1.304477\n",
-      "Cost after epoch 70: 1.201501\n",
-      "Cost after epoch 75: 1.144230\n",
-      "Cost after epoch 80: 1.098368\n",
-      "Cost after epoch 85: 1.077411\n",
-      "Cost after epoch 90: 1.043173\n",
-      "Cost after epoch 95: 1.022620\n"
+      "Cost after epoch 0: 1.917929\n",
+      "Cost after epoch 5: 1.506757\n",
+      "Cost after epoch 10: 0.955359\n",
+      "Cost after epoch 15: 0.845802\n",
+      "Cost after epoch 20: 0.701174\n",
+      "Cost after epoch 25: 0.571977\n",
+      "Cost after epoch 30: 0.518435\n",
+      "Cost after epoch 35: 0.495806\n",
+      "Cost after epoch 40: 0.429827\n",
+      "Cost after epoch 45: 0.407291\n",
+      "Cost after epoch 50: 0.366394\n",
+      "Cost after epoch 55: 0.376922\n",
+      "Cost after epoch 60: 0.299491\n",
+      "Cost after epoch 65: 0.338870\n",
+      "Cost after epoch 70: 0.316400\n",
+      "Cost after epoch 75: 0.310413\n",
+      "Cost after epoch 80: 0.249549\n",
+      "Cost after epoch 85: 0.243457\n",
+      "Cost after epoch 90: 0.200031\n",
+      "Cost after epoch 95: 0.175452\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -788,8 +777,8 @@
      "output_type": "stream",
      "text": [
       "Tensor(\"Mean_1:0\", shape=(), dtype=float32)\n",
-      "Train Accuracy: 0.6638889\n",
-      "Test Accuracy: 0.55\n"
+      "Train Accuracy: 0.94074076\n",
+      "Test Accuracy: 0.78333336\n"
      ]
     }
    ],
@@ -887,7 +876,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,