diff --git a/Oiseau.ipynb b/Oiseau.ipynb
index ff56c45..9d859e3 100644
--- a/Oiseau.ipynb
+++ b/Oiseau.ipynb
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,9 +81,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[name: \"/device:CPU:0\"\n",
+      "device_type: \"CPU\"\n",
+      "memory_limit: 268435456\n",
+      "locality {\n",
+      "}\n",
+      "incarnation: 6212331521511793857\n",
+      "]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-12-22 20:34:12.391220: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  SSE4.1 SSE4.2\n",
+      "To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
    "source": [
     "from tensorflow.python.client import device_lib\n",
     "print(device_lib.list_local_devices())"
@@ -91,9 +113,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU\n"
+     ]
+    }
+   ],
    "source": [
     "MODE = \"GPU\" if \"GPU\" in [k.device_type for k in device_lib.list_local_devices()] else \"CPU\"\n",
     "print(MODE)"
@@ -132,25 +162,24 @@
     "\n",
     "```\n",
     "data_dir\n",
-    "└───sample/\n",
-    "│   └───train/\n",
-    "│   │   └───absence/\n",
-    "│   │   │   │   absence_t_0001.jpg\n",
-    "│   │   │   │   absence_t_0002.jpg\n",
-    "│   │   │   │   ...\n",
-    "│   │   └───presence/\n",
-    "│   │   │   │   presence_t_0001.jpg\n",
-    "│   │   │   │   presence_t_0002.jpg\n",
-    "│   │   │   │   ...\n",
-    "│   └───validation/\n",
-    "│   │   └───absence/\n",
-    "│   │   │   │   absence_v_0001.jpg\n",
-    "│   │   │   │   absence_v_0002.jpg\n",
-    "│   │   │   │   ...\n",
-    "│   │   └───presence/\n",
-    "│   │   │   │   presence_v_0001.jpg\n",
-    "│   │   │   │   presence_v_0002.jpg\n",
-    "│   │   │   │   ...\n",
+    "└───train/\n",
+    "│   └───absence/\n",
+    "│   │   │   absence_t_0001.jpg\n",
+    "│   │   │   absence_t_0002.jpg\n",
+    "│   │   │   ...\n",
+    "│   └───presence/\n",
+    "│   │   │   presence_t_0001.jpg\n",
+    "│   │   │   presence_t_0002.jpg\n",
+    "│   │   │   ...\n",
+    "└───validation/\n",
+    "│   └───absence/\n",
+    "│   │   │   absence_v_0001.jpg\n",
+    "│   │   │   absence_v_0002.jpg\n",
+    "│   │   │   ...\n",
+    "│   └───presence/\n",
+    "│   │   │   presence_v_0001.jpg\n",
+    "│   │   │   presence_v_0002.jpg\n",
+    "│   │   │   ...\n",
     "```"
    ]
   },
@@ -158,23 +187,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "###  Parameter"
+    "###  Reading the data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "data_dir = 'data/' # data path\n",
     "\n",
     "# Sample directory path\n",
-    "# That way we can use this algorithm with an other sample if we have mire pictures in the future.\n",
-    "\n",
-    "N_train = 160\n",
-    "N_val = 40\n",
-    "data_dir_sub = data_dir+'sample_%d_Ntrain_%d_Nval' %(N_train, N_val)"
+    "# That way we can use this algorithm with an other sample if we have mire pictures in the future.\n"
    ]
   },
   {
@@ -188,23 +213,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'kpi' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/83/bqq_97cs083fpgkn46bd5k5r0000gn/T/ipykernel_61636/3158407634.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkpi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir_sub\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/train/presence/presence_t_0001.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'kpi' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "img = kpi.load_img(data_dir_sub+'/train/presence/presence_t_0001.jpg')  \n",
+    "img = kpi.load_img(data_dir+'/train/presence/presence_t_0001.jpg')  \n",
     "img"
    ]
   },
@@ -281,15 +294,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "img = kpi.load_img(data_dir_sub+\"/train/presence/presence_t_0001.jpg\")\n",
+    "img = kpi.load_img(data_dir+\"/train/presence/presence_t_0001.jpg\")\n",
     "x = kpi.img_to_array(img)  \n",
     "x_ = np.expand_dims(x, axis=0)\n",
     "\n",
-    "if not(os.path.isdir(data_dir_sub+\"/preprocessing_example\")):\n",
-    "    os.mkdir(data_dir_sub+\"/preprocessing_example\")\n",
+    "if not(os.path.isdir(data_dir+\"/preprocessing_example\")):\n",
+    "    os.mkdir(data_dir+\"/preprocessing_example\")\n",
     "\n",
     "    i = 0\n",
-    "    for batch in datagen.flow(x_, batch_size=1,save_to_dir=data_dir_sub+\"/preprocessing_example\", save_prefix='bird', save_format='jpg'):\n",
+    "    for batch in datagen.flow(x_, batch_size=1,save_to_dir=data_dir+\"/preprocessing_example\", save_prefix='bird', save_format='jpg'):\n",
     "        i += 1\n",
     "        if i > 7:\n",
     "            break  "
@@ -330,8 +343,8 @@
    ],
    "source": [
     "X_list=[]\n",
-    "for f in os.listdir(data_dir_sub+\"/preprocessing_example\"):\n",
-    "    X_list.append(kpi.img_to_array(kpi.load_img(data_dir_sub+\"/preprocessing_example/\"+f)))\n",
+    "for f in os.listdir(data_dir+\"/preprocessing_example\"):\n",
+    "    X_list.append(kpi.img_to_array(kpi.load_img(data_dir+\"/preprocessing_example/\"+f)))\n",
     "\n",
     "\n",
     "fig=plt.figure(figsize=(16,8))\n",
@@ -411,8 +424,8 @@
     }
    ],
    "source": [
-    "x_0 = kpi.img_to_array(kpi.load_img(data_dir_sub+\"/train/presence/presence_t_0001.jpg\"))\n",
-    "x_1 = kpi.img_to_array(kpi.load_img(data_dir_sub+\"/train/presence/presence_t_0002.jpg\"))\n",
+    "x_0 = kpi.img_to_array(kpi.load_img(data_dir+\"/train/presence/presence_t_0001.jpg\"))\n",
+    "x_1 = kpi.img_to_array(kpi.load_img(data_dir+\"/train/presence/presence_t_0002.jpg\"))\n",
     "x_0.shape, x_1.shape"
    ]
   },
@@ -461,14 +474,14 @@
     "# subfolers of 'data/train', and indefinitely generate\n",
     "# batches of augmented image data\n",
     "train_generator = train_datagen.flow_from_directory(\n",
-    "        data_dir_sub+\"/train/\",  # this is the target directory\n",
+    "        data_dir+\"/train/\",  # this is the target directory\n",
     "        target_size=(img_width, img_height),  \n",
     "        batch_size=batch_size,\n",
     "        class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels\n",
     "\n",
     "# this is a similar generator, for validation data\n",
     "validation_generator = valid_datagen.flow_from_directory(\n",
-    "        data_dir_sub+\"/validation/\",\n",
+    "        data_dir+\"/validation/\",\n",
     "        target_size=(img_width, img_height),\n",
     "        batch_size=batch_size,\n",
     "        class_mode='binary')"
@@ -840,7 +853,7 @@
     "datagen = kpi.ImageDataGenerator(rescale=1. / 255)\n",
     "\n",
     "generator = datagen.flow_from_directory(\n",
-    "        data_dir_sub+\"/train\",\n",
+    "        data_dir+\"/train\",\n",
     "        target_size=(img_width, img_height),\n",
     "        batch_size=batch_size,\n",
     "        class_mode=None,  # this means our generator will only yield batches of data, no labels\n",
@@ -849,7 +862,7 @@
     "\n",
     "\n",
     "generator = datagen.flow_from_directory(\n",
-    "    data_dir_sub+\"/validation\",\n",
+    "    data_dir+\"/validation\",\n",
     "        target_size=(img_width, img_height),\n",
     "        batch_size=batch_size,\n",
     "        class_mode=None,\n",
@@ -1044,7 +1057,7 @@
     }
    ],
    "source": [
-    "model_VGG_fcm.save_weights(data_dir_sub+'/weights_model_VGG_fully_connected_model_%d_epochs_%d_batch_size.h5' %(epochs, batch_size))"
+    "model_VGG_fcm.save_weights(data_dir+'/weights_model_VGG_fully_connected_model_%d_epochs_%d_batch_size.h5' %(epochs, batch_size))"
    ]
   },
   {
@@ -1113,7 +1126,7 @@
     "top_model.add(kl.Dropout(0.5))\n",
     "top_model.add(kl.Dense(1, activation='sigmoid'))\n",
     "\n",
-    "top_model.load_weights(data_dir_sub+'/weights_model_VGG_fully_connected_model_%d_epochs_%d_batch_size.h5' %(epochs, batch_size))\n"
+    "top_model.load_weights(data_dir+'/weights_model_VGG_fully_connected_model_%d_epochs_%d_batch_size.h5' %(epochs, batch_size))\n"
    ]
   },
   {
@@ -1247,13 +1260,13 @@
     "test_datagen = kpi.ImageDataGenerator(rescale=1. / 255)\n",
     "\n",
     "train_generator = train_datagen.flow_from_directory(\n",
-    "    data_dir_sub+\"/train/\",\n",
+    "    data_dir+\"/train/\",\n",
     "    target_size=(img_height, img_width),\n",
     "    batch_size=batch_size,\n",
     "    class_mode='binary')\n",
     "\n",
     "validation_generator = test_datagen.flow_from_directory(\n",
-    "    data_dir_sub+\"/validation/\",\n",
+    "    data_dir+\"/validation/\",\n",
     "    target_size=(img_height, img_width),\n",
     "    batch_size=batch_size,\n",
     "    class_mode='binary')\n"
@@ -1390,7 +1403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.7.11"
   },
   "toc": {
    "nav_menu": {},
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