diff --git a/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb
index 4f73a2b..38b9a9c 100644
--- a/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb
+++ b/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "db0b0a11",
+   "id": "769c9afd",
    "metadata": {},
    "outputs": [
     {
@@ -40,7 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "377355df",
+   "id": "227c58c5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,8 +49,25 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
+   "id": "dc65195f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "def run_time(func):\n",
+    "    def inner(model, image, question):\n",
+    "        back = func(model, image, question)\n",
+    "        print(\"Runned time: {} s\".format(round((time.time() - t)/10, 3)))\n",
+    "        return back\n",
+    "    t = time.time()\n",
+    "    return inner"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 8,
-   "id": "fd4ec8ee",
+   "id": "bcbe551f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,10 +80,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "cc76d61f",
+   "id": "36fcb0d9",
    "metadata": {},
    "outputs": [],
    "source": [
+    "@run_time\n",
     "def inference(model_PATH, img_p):\n",
     "    G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')\n",
     "    G_sample = model.generator(G_Z)\n",
@@ -84,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d93edf59",
+   "id": "b79deb86",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/src/Untitled.ipynb b/src/Untitled.ipynb
index 6966702..972576c 100644
--- a/src/Untitled.ipynb
+++ b/src/Untitled.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "ea42a489",
+   "id": "3c86ef8a",
    "metadata": {},
    "outputs": [
     {
@@ -40,7 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "24a28be9",
+   "id": "87b58a5a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,8 +49,25 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 9,
+   "id": "4e963aed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "def run_time(func):\n",
+    "    def inner(model, image, question):\n",
+    "        back = func(model, image, question)\n",
+    "        print(\"Runned time: {} s\".format(round((time.time() - t)/10, 3)))\n",
+    "        return back\n",
+    "    t = time.time()\n",
+    "    return inner"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 8,
-   "id": "2f04ed3d",
+   "id": "aba8e440",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,10 +80,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "4cc717e4",
+   "id": "bc8c0bf4",
    "metadata": {},
    "outputs": [],
    "source": [
+    "@run_time\n",
     "def inference(model_PATH, img_p):\n",
     "    G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')\n",
     "    G_sample = model.generator(G_Z)\n",
@@ -84,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9ce2fc25",
+   "id": "5c1557b3",
    "metadata": {},
    "outputs": [],
    "source": [