diff --git a/GTSRB/01-Preparation-of-data.ipynb b/GTSRB/01-Preparation-of-data.ipynb
index 6ff294fc9f16bc6e5c267fc0fa71e516f94fde7a..ec29ed9d9d70484ac11313508ce4959824ea6d08 100644
--- a/GTSRB/01-Preparation-of-data.ipynb
+++ b/GTSRB/01-Preparation-of-data.ipynb
@@ -8,7 +8,6 @@
     "=================================================\n",
     "---\n",
     "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  \n",
-    "Version: 1.12\n",
     "\n",
     "## Episode 1 : Preparation of data\n",
     "\n",
@@ -42,7 +41,7 @@
     "from skimage.filters import rank\n",
     "from skimage import io, color, exposure, transform\n",
     "\n",
-    "import idle.pwk as ooo\n",
+    "import fidle.pwk as ooo\n",
     "from importlib import reload\n",
     "\n",
     "ooo.init()"
diff --git a/GTSRB/02-First-convolutions.ipynb b/GTSRB/02-First-convolutions.ipynb
index 64831435737f221cfa7073fafb9a865796e5cd44..36c83251ee8d6739b751d99095affeaef18b9f28 100644
--- a/GTSRB/02-First-convolutions.ipynb
+++ b/GTSRB/02-First-convolutions.ipynb
@@ -8,7 +8,6 @@
     "=================================================\n",
     "---\n",
     "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  \n",
-    "Vesion : 1.2.1\n",
     "\n",
     "## Episode 2 : First Convolutions\n",
     "\n",
@@ -23,22 +22,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IDLE 2020 - Practical Work Module\n",
-      "  Version            : 0.1.4\n",
-      "  Run time           : Thursday 16 January 2020, 16:26:01\n",
-      "  Matplotlib style   : idle/talk.mplstyle\n",
-      "  TensorFlow version : 2.0.0\n",
-      "  Keras version      : 2.2.4-tf\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
@@ -49,7 +35,7 @@
     "import h5py\n",
     "import os,time\n",
     "\n",
-    "import idle.pwk as ooo\n",
+    "import fidle.pwk as ooo\n",
     "from importlib import reload\n",
     "\n",
     "ooo.init()"
@@ -66,20 +52,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset \"set-24x24-L\" is loaded. (228.8 Mo)\n",
-      "\n",
-      "CPU times: user 0 ns, sys: 344 ms, total: 344 ms\n",
-      "Wall time: 463 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -113,40 +88,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train :  (39209, 24, 24, 1)\n",
-      "y_train :  (39209,)\n",
-      "x_test  :  (12630, 24, 24, 1)\n",
-      "y_test  :  (12630,)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x338.4 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x291.6 with 36 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"x_train : \", x_train.shape)\n",
     "print(\"y_train : \", y_train.shape)\n",
@@ -169,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,17 +203,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Images of the dataset have this folowing shape :  (24, 24, 1)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "(n,lx,ly,lz) = x_train.shape\n",
     "print(\"Images of the dataset have this folowing shape : \",(lx,ly,lz))"
@@ -284,44 +220,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_2 (Conv2D)            (None, 22, 22, 96)        960       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 11, 11, 96)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 11, 11, 96)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 9, 9, 192)         166080    \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 192)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 4, 4, 192)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 3072)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 1500)              4609500   \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 1500)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 43)                64543     \n",
-      "=================================================================\n",
-      "Total params: 4,841,083\n",
-      "Trainable params: 4,841,083\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = get_model_v1(lx,ly,lz)\n",
     "\n",
@@ -341,29 +242,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 2000 samples, validate on 12630 samples\n",
-      "Epoch 1/5\n",
-      "2000/2000 [==============================] - 3s 2ms/sample - loss: 3.5544 - accuracy: 0.0640 - val_loss: 3.4370 - val_accuracy: 0.0924\n",
-      "Epoch 2/5\n",
-      "2000/2000 [==============================] - 2s 900us/sample - loss: 3.3254 - accuracy: 0.1140 - val_loss: 3.1923 - val_accuracy: 0.1568\n",
-      "Epoch 3/5\n",
-      "2000/2000 [==============================] - 2s 903us/sample - loss: 2.8077 - accuracy: 0.2880 - val_loss: 2.5395 - val_accuracy: 0.3308\n",
-      "Epoch 4/5\n",
-      "2000/2000 [==============================] - 2s 880us/sample - loss: 2.1190 - accuracy: 0.4425 - val_loss: 1.8869 - val_accuracy: 0.5202\n",
-      "Epoch 5/5\n",
-      "2000/2000 [==============================] - 2s 909us/sample - loss: 1.5545 - accuracy: 0.5655 - val_loss: 1.5202 - val_accuracy: 0.5954\n",
-      "CPU times: user 2min 27s, sys: 14.4 s, total: 2min 41s\n",
-      "Wall time: 10.9 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -374,7 +255,7 @@
     "x_train,y_train=ooo.shuffle_np_dataset(x_train,y_train)\n",
     "\n",
     "# ---- Train\n",
-    "history = model.fit(  x_train[:2000], y_train[:2000],\n",
+    "history = model.fit(  x_train, y_train,\n",
     "                      batch_size=batch_size,\n",
     "                      epochs=epochs,\n",
     "                      verbose=1,\n",
@@ -390,17 +271,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max validation accuracy is : 0.5954\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "max_val_accuracy = max(history.history[\"val_accuracy\"])\n",
     "print(\"Max validation accuracy is : {:.4f}\".format(max_val_accuracy))"
@@ -408,174 +281,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test loss      : 1.5202\n",
-      "Test accuracy  : 0.5954\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "score = model.evaluate(x_test, y_test, verbose=0)\n",
     "\n",
     "print('Test loss      : {:5.4f}'.format(score[0]))\n",
     "print('Test accuracy  : {:5.4f}'.format(score[1]))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6/ Multiple datasets, multiple models ;-)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "\n",
-    "batch_size = 64\n",
-    "epochs     = 16\n",
-    "\n",
-    "datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
-    "models   = {'v1':get_model_v1, 'v2':get_model_v2, 'v3':get_model_v3}\n",
-    "# datasets = ['set-24x24-L', 'set-24x24-RGB']\n",
-    "# models   = {'v1':get_model_v1, 'v3':get_model_v3}\n",
-    "\n",
-    "report=[]\n",
-    "head=['Datasets', 'Size']\n",
-    "head.extend( [ 'Model : {} '.format(i) for i in models.keys()] )\n",
-    "report.append('|'+'|'.join(head)+'|')\n",
-    "report.append('|:-----:'*len(head)+'|')\n",
-    "\n",
-    "for dname in datasets:\n",
-    "    print()\n",
-    "    # ---- Read dataset\n",
-    "    x_train,y_train,x_test,y_test = read_dataset(dname)\n",
-    "    dsize=os.path.getsize('./data/'+dname+'.h5')/(1024*1024)\n",
-    "    \n",
-    "    # ---- Get the shape\n",
-    "    (n,lx,ly,lz) = x_train.shape\n",
-    "\n",
-    "    # ---- For each model\n",
-    "    accuracy={}\n",
-    "    duration={}\n",
-    "    for kmodel,fmodel in models.items():\n",
-    "        print(\"    Run model : {}  : \".format(kmodel), end='')\n",
-    "        # ---- get model\n",
-    "        try:\n",
-    "            model=fmodel(lx,ly,lz)\n",
-    "            # ---- Compile it\n",
-    "            model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
-    "            # ---- Train\n",
-    "            start_time = time.time()\n",
-    "            history = model.fit(  x_train, y_train,\n",
-    "                                batch_size=batch_size,\n",
-    "                                epochs=epochs,\n",
-    "                                verbose=0,\n",
-    "                                validation_data=(x_test, y_test))\n",
-    "            # ---- Result\n",
-    "            end_time= time.time()\n",
-    "            accuracy[kmodel] = '{:6.2f}%'.format(100*max(history.history[\"val_accuracy\"]))\n",
-    "            duration[kmodel] = '{:6.2f}s'.format( (end_time-start_time) )\n",
-    "            print(\"Done with : {} in {}\".format(accuracy[kmodel],duration[kmodel]))\n",
-    "        except:\n",
-    "            accuracy[kmodel] = '  -   '\n",
-    "            duration[kmodel] = '  -   '\n",
-    "            print('Cannot be use..')\n",
-    "    # ---- Output\n",
-    "    results='|'.join( [ ' {} {} '.format(accuracy[i], duration[i]) for i in accuracy.keys()] )\n",
-    "    report.append( '| {:24s} | {:12.0f} Mo | {} |'.format(dname, dsize, results ) )\n",
-    "\n",
-    "print('\\n\\nFinal report is :\\n')\n",
-    "print('\\n'.join(report))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "\n",
-    "\n",
-    "### Some results :  \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "|         Datasets         |       Size      |     Model : v1     |    Model : v2      |     Model : v3     |\n",
-    "|:------------------------:|:---------------:|:------------------:|:------------------:|:------------------:|\n",
-    "| set-24x24-L              |          229 Mo |   95.91%  75.04s   |   96.86% 102.28s   |   -      -         |\n",
-    "| set-24x24-RGB            |          684 Mo |   96.60%  77.24s   |   97.32% 103.93s   |   -      -         |\n",
-    "| set-48x48-L              |          914 Mo | **96.71%** 123.94s |   97.68% 149.57s   |  97.60%  91.53s    |\n",
-    "| set-48x48-RGB            |         2736 Mo |   96.36% 117.74s   | **98.20%** 142.63s |  97.28%  91.29s    |\n",
-    "| set-24x24-L-LHE          |          229 Mo |   95.95%  66.12s   |   96.75%  89.45s   |   -      -         |\n",
-    "| set-24x24-RGB-HE         |          684 Mo |   95.30%  68.89s   |   96.28%  92.15s   |   -      -         |\n",
-    "| set-48x48-L-LHE          |          914 Mo |   96.69% 109.28s   |   97.94% 135.17s   | **97.97%** 83.80s  |\n",
-    "| set-48x48-RGB-HE         |         2736 Mo |   95.29% 117.70s   | **98.13%** 141.56s |  97.00%  89.38s    |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         Dataset  Size  v1 Accuracy  v1 Duration\n",
-      "0    set-24x24-L   229        95.91        75.04\n",
-      "1  set-24x24-RGB   684        96.60        77.24\n",
-      "2    set-48x48-L   914        96.71       123.94\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = {'Dataset':  ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L'],\n",
-    "        'Size': [229,684,914],\n",
-    "        'v1 Accuracy': [95.91, 96.60, 96.71],\n",
-    "        'v1 Duration': [75.04, 77.24, 123.94]\n",
-    "        }\n",
-    "df = pd.DataFrame (data)\n",
-    "print(df)\n",
-    "\n",
-    "# A faire : pretty print, ajout multi_run dans pwk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/GTSRB/03-Tracking-and-visualizing.ipynb b/GTSRB/03-Tracking-and-visualizing.ipynb
index b85943a8f3e5b3afcae3c45e74d2cce2390372a2..b185c9c21e9b1ae8f39e1f6c4a0c2f0a4514acf5 100644
--- a/GTSRB/03-Tracking-and-visualizing.ipynb
+++ b/GTSRB/03-Tracking-and-visualizing.ipynb
@@ -7,7 +7,7 @@
     "German Traffic Sign Recognition Benchmark (GTSRB)\n",
     "=================================================\n",
     "---\n",
-    "Introduction au Deep Learning  (IDLE) - S. Aria, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020\n",
+    "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020\n",
     "\n",
     "## Episode 3 : Tracking, visualizing and save models\n",
     "\n",
@@ -22,22 +22,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IDLE 2020 - Practical Work Module\n",
-      "  Version            : 0.1.4\n",
-      "  Run time           : Wednesday 15 January 2020, 11:19:11\n",
-      "  Matplotlib style   : idle/talk.mplstyle\n",
-      "  TensorFlow version : 2.0.0\n",
-      "  Keras version      : 2.2.4-tf\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
@@ -52,7 +39,7 @@
     "import seaborn as sn\n",
     "import os, time, random\n",
     "\n",
-    "import idle.pwk as ooo\n",
+    "import fidle.pwk as ooo\n",
     "from importlib import reload\n",
     "\n",
     "ooo.init()"
@@ -70,20 +57,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset \"set-24x24-L\" is loaded. (228.8 Mo)\n",
-      "\n",
-      "CPU times: user 20 ms, sys: 296 ms, total: 316 ms\n",
-      "Wall time: 2.07 s\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -119,40 +95,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train :  (39209, 24, 24, 1)\n",
-      "y_train :  (39209,)\n",
-      "x_test  :  (12630, 24, 24, 1)\n",
-      "y_test  :  (12630,)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x338.4 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x291.6 with 36 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"x_train : \", x_train.shape)\n",
     "print(\"y_train : \", y_train.shape)\n",
@@ -175,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,29 +164,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "total 0\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%bash\n",
     "# To clean old logs and saved model, run this cell\n",
     "#\n",
-    "/bin/rm -r ./run/logs 2>/dev/null\n",
+    "/bin/rm -r ./run/logs   2>/dev/null\n",
     "/bin/rm -r ./run/models 2>/dev/null\n",
-    "/bin/ls -l ./run  2>/dev/null"
+    "/bin/mkdir -p -m 755 ./run/logs\n",
+    "/bin/mkdir -p -m 755 ./run/models\n",
+    "echo -e \"Reset directories : ./run/logs and ./run/models .\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,17 +210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Images of the dataset have this folowing shape :  (24, 24, 1)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "(n,lx,ly,lz) = x_train.shape\n",
     "print(\"Images of the dataset have this folowing shape : \",(lx,ly,lz))"
@@ -296,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,85 +245,15 @@
    "metadata": {},
    "source": [
     "**Train it :**  \n",
-    "Note : La courbe d'apprentissage est visible en temps réel avec Tensorboard :    \n",
+    "Note: The training curve is visible in real time with Tensorboard :     \n",
     "`#tensorboard --logdir ./run/logs`  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 5000 samples, validate on 200 samples\n",
-      "Epoch 1/30\n",
-      "5000/5000 [==============================] - 3s 669us/sample - loss: 3.3748 - accuracy: 0.1088 - val_loss: 2.9594 - val_accuracy: 0.2850\n",
-      "Epoch 2/30\n",
-      "5000/5000 [==============================] - 2s 490us/sample - loss: 2.0842 - accuracy: 0.4414 - val_loss: 1.5636 - val_accuracy: 0.5550\n",
-      "Epoch 3/30\n",
-      "5000/5000 [==============================] - 2s 470us/sample - loss: 1.1964 - accuracy: 0.6546 - val_loss: 1.1344 - val_accuracy: 0.6700\n",
-      "Epoch 4/30\n",
-      "5000/5000 [==============================] - 3s 515us/sample - loss: 0.8034 - accuracy: 0.7570 - val_loss: 0.9471 - val_accuracy: 0.7450\n",
-      "Epoch 5/30\n",
-      "5000/5000 [==============================] - 2s 471us/sample - loss: 0.5804 - accuracy: 0.8266 - val_loss: 0.7523 - val_accuracy: 0.8000\n",
-      "Epoch 6/30\n",
-      "5000/5000 [==============================] - 2s 495us/sample - loss: 0.4359 - accuracy: 0.8646 - val_loss: 0.6976 - val_accuracy: 0.8050\n",
-      "Epoch 7/30\n",
-      "5000/5000 [==============================] - 2s 460us/sample - loss: 0.3446 - accuracy: 0.8938 - val_loss: 0.6042 - val_accuracy: 0.7950\n",
-      "Epoch 8/30\n",
-      "5000/5000 [==============================] - 3s 504us/sample - loss: 0.2875 - accuracy: 0.9200 - val_loss: 0.6150 - val_accuracy: 0.8200\n",
-      "Epoch 9/30\n",
-      "5000/5000 [==============================] - 2s 481us/sample - loss: 0.2202 - accuracy: 0.9300 - val_loss: 0.5156 - val_accuracy: 0.8400\n",
-      "Epoch 10/30\n",
-      "5000/5000 [==============================] - 3s 531us/sample - loss: 0.2028 - accuracy: 0.9406 - val_loss: 0.5839 - val_accuracy: 0.8550\n",
-      "Epoch 11/30\n",
-      "5000/5000 [==============================] - 2s 441us/sample - loss: 0.1693 - accuracy: 0.9506 - val_loss: 0.4834 - val_accuracy: 0.8700\n",
-      "Epoch 12/30\n",
-      "5000/5000 [==============================] - 2s 493us/sample - loss: 0.1508 - accuracy: 0.9552 - val_loss: 0.5246 - val_accuracy: 0.8750\n",
-      "Epoch 13/30\n",
-      "5000/5000 [==============================] - 2s 460us/sample - loss: 0.1233 - accuracy: 0.9646 - val_loss: 0.4841 - val_accuracy: 0.8750\n",
-      "Epoch 14/30\n",
-      "5000/5000 [==============================] - 3s 505us/sample - loss: 0.1214 - accuracy: 0.9654 - val_loss: 0.4631 - val_accuracy: 0.8550\n",
-      "Epoch 15/30\n",
-      "5000/5000 [==============================] - 2s 457us/sample - loss: 0.1056 - accuracy: 0.9708 - val_loss: 0.4590 - val_accuracy: 0.8750\n",
-      "Epoch 16/30\n",
-      "5000/5000 [==============================] - 2s 469us/sample - loss: 0.0990 - accuracy: 0.9708 - val_loss: 0.5055 - val_accuracy: 0.8600\n",
-      "Epoch 17/30\n",
-      "5000/5000 [==============================] - 2s 450us/sample - loss: 0.0883 - accuracy: 0.9764 - val_loss: 0.4340 - val_accuracy: 0.8650\n",
-      "Epoch 18/30\n",
-      "5000/5000 [==============================] - 2s 488us/sample - loss: 0.0663 - accuracy: 0.9808 - val_loss: 0.4305 - val_accuracy: 0.8650\n",
-      "Epoch 19/30\n",
-      "5000/5000 [==============================] - 2s 459us/sample - loss: 0.0691 - accuracy: 0.9798 - val_loss: 0.4639 - val_accuracy: 0.8850\n",
-      "Epoch 20/30\n",
-      "5000/5000 [==============================] - 2s 495us/sample - loss: 0.0641 - accuracy: 0.9840 - val_loss: 0.4365 - val_accuracy: 0.8750\n",
-      "Epoch 21/30\n",
-      "5000/5000 [==============================] - 2s 430us/sample - loss: 0.0631 - accuracy: 0.9800 - val_loss: 0.4468 - val_accuracy: 0.8900\n",
-      "Epoch 22/30\n",
-      "5000/5000 [==============================] - 3s 505us/sample - loss: 0.0502 - accuracy: 0.9850 - val_loss: 0.4335 - val_accuracy: 0.8900\n",
-      "Epoch 23/30\n",
-      "5000/5000 [==============================] - 2s 461us/sample - loss: 0.0471 - accuracy: 0.9868 - val_loss: 0.4634 - val_accuracy: 0.8950\n",
-      "Epoch 24/30\n",
-      "5000/5000 [==============================] - 2s 471us/sample - loss: 0.0497 - accuracy: 0.9846 - val_loss: 0.5547 - val_accuracy: 0.8700\n",
-      "Epoch 25/30\n",
-      "5000/5000 [==============================] - 2s 412us/sample - loss: 0.0521 - accuracy: 0.9854 - val_loss: 0.4100 - val_accuracy: 0.8850\n",
-      "Epoch 26/30\n",
-      "5000/5000 [==============================] - 2s 468us/sample - loss: 0.0479 - accuracy: 0.9856 - val_loss: 0.4185 - val_accuracy: 0.9000\n",
-      "Epoch 27/30\n",
-      "5000/5000 [==============================] - 2s 421us/sample - loss: 0.0479 - accuracy: 0.9852 - val_loss: 0.4890 - val_accuracy: 0.8750\n",
-      "Epoch 28/30\n",
-      "5000/5000 [==============================] - 2s 484us/sample - loss: 0.0379 - accuracy: 0.9888 - val_loss: 0.3553 - val_accuracy: 0.8900\n",
-      "Epoch 29/30\n",
-      "5000/5000 [==============================] - 2s 465us/sample - loss: 0.0389 - accuracy: 0.9894 - val_loss: 0.4577 - val_accuracy: 0.8800\n",
-      "Epoch 30/30\n",
-      "5000/5000 [==============================] - 2s 472us/sample - loss: 0.0374 - accuracy: 0.9886 - val_loss: 0.4341 - val_accuracy: 0.8850\n",
-      "CPU times: user 22min 12s, sys: 2min 20s, total: 24min 33s\n",
-      "Wall time: 1min 12s\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -405,7 +266,7 @@
     "# ---- Train\n",
     "# Note: To be faster in our example, we can take only 2000 values\n",
     "#\n",
-    "history = model.fit(  x_train[:5000], y_train[:5000],\n",
+    "history = model.fit(  x_train, y_train,\n",
     "                      batch_size=batch_size,\n",
     "                      epochs=epochs,\n",
     "                      verbose=1,\n",
@@ -424,17 +285,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max validation accuracy is : 0.9000\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "max_val_accuracy = max(history.history[\"val_accuracy\"])\n",
     "print(\"Max validation accuracy is : {:.4f}\".format(max_val_accuracy))"
@@ -442,18 +295,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test loss      : 0.4549\n",
-      "Test accuracy  : 0.9183\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "score = model.evaluate(x_test, y_test, verbose=0)\n",
     "\n",
@@ -471,34 +315,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ooo.plot_history(history)"
    ]
@@ -512,22 +331,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "y_pred   = model.predict_classes(x_test)\n",
     "conf_mat = confusion_matrix(y_test,y_pred, normalize=\"true\", labels=range(43))\n",
@@ -545,34 +351,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "./run/models/\n",
-      "./run/models/best-model.h5\n",
-      "./run/models/model-0002.h5\n",
-      "./run/models/model-0004.h5\n",
-      "./run/models/model-0006.h5\n",
-      "./run/models/model-0008.h5\n",
-      "./run/models/model-0010.h5\n",
-      "./run/models/model-0012.h5\n",
-      "./run/models/model-0014.h5\n",
-      "./run/models/model-0016.h5\n",
-      "./run/models/model-0018.h5\n",
-      "./run/models/model-0020.h5\n",
-      "./run/models/model-0022.h5\n",
-      "./run/models/model-0024.h5\n",
-      "./run/models/model-0026.h5\n",
-      "./run/models/model-0028.h5\n",
-      "./run/models/model-0030.h5\n",
-      "./run/models/last-model.h5\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!find ./run/models/"
    ]
@@ -586,17 +367,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "loaded_model = tf.keras.models.load_model('./run/models/best-model.h5')\n",
     "# best_model.summary()\n",
@@ -612,18 +385,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test loss      : 0.5084\n",
-      "Test accuracy  : 0.9177\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
     "\n",
@@ -640,63 +404,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Output layer from model is (x100) :\n",
-      "\n",
-      "[[ 0.    0.01  0.07  1.31  0.01  0.    0.    0.    0.    1.47  0.02  0.    0.01  0.17 37.19\n",
-      "   0.75  0.    0.23  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
-      "   0.    0.    0.05  0.05  0.7   0.55  0.05  0.   57.34  0.    0.    0.    0.  ]]\n",
-      "\n",
-      "Graphically :\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAADECAYAAADTYuRHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de9ztU53A8c+XcFxyiwgVkYiKUkkml6Tp3hTShRilrEi6zKRp5FKmmSJFq0aGIiYqku4XUZGQdNdNJCJmzHE5Dqdz1vyxftvZZ7f38+y9n9s+z/N5v177tff+rfX77e+zn3XO891rr0uUUpAkSZI0s1aY6QAkSZIkmZhLkiRJI8HEXJIkSRoBJuaSJEnSCDAxlyRJkkaAibkkSZI0AkzMJc0JEXFARFwZEfdERGluO0TEGm3P15vpOMcSEds2cd4z07HMJRFxR6u9dBw/tDn+pWmMZblpr5IG95CZDkDS8isiht0I4dJSyq6TGctYIuINwMebpw8AtzWPF01XDJ0i4nnAgcDTgA2AxcCtwE3AZcAlwCWllL/OVIyaXhGxJ7ATcGUp5SszHY+k6WdiLmkibutxfF1gJWAhML9L+f9OWUTdvaW5fx9wVCllSasgIlYDft08nfIkOCJWAj4FvLLt8F+BBcCmwObArsC/AI8FftdW735qrPdOdZzqy53U38dNk3S9PYG3AR8FeiXmS5jG9ippepmYSxpaKWXDbscj4hJgF+DcUsoB0xlTl1hWBB7XPD2tPSkHKKUsALaaxpCOpiblS4CPABn4fSllSUSsAmwHvBA4oPPEUspvmd5YNYZSytnA2dP8mtPdXiVNIxNzSbPdPCCaxzM6NjsiHgKk5ukHSinvbC8vpdwP/BD4YUQcjfOAJGlO8T99STOmczJjRDwrIi6MiFsjYnFEvLet7nYRcUxEXBYRN0XE/c2kvG9HxP4REd2uzbLJ+O1tE+dOaeqNO5kuItaMiCMj4oqIuDMi7ouI30XE+RGxT9Mr349HAms3j8ecMFhKWVxKWWYMfD+TPyPi5RHxvYi4KyLmN+/Xvk3Z1c35e3Wcs8wkxrZrzI+Iu5tr/EOfP2NnPJ9rrv32iFg9Io6PiN9GxMLm93xmRGzW49zOuA6MiO9HxP82x/foqL9WRLwnIq5pfv77IuK6iDgxIh4xTpwHRsRVEXFvRPxPRHyj8/rjxdejzhMj4rSmvSxoYv9JE9OTmjqttvq25rQ3tbXJZdpmn+11q4g4PSJuaN7n/4mI73T7d9J2zoNto3mN93X8ns6KiE3Hej8kTZw95pJGQkQcCJxG7TD4P+pQj3ZXAKs0jxdTE+6HAbs3txdFxD6llNaE1EXUMfABPLw59hegVX5Xn3FtT02iN2q77gLqWPDNgX8A1gfu6Od6bTYesP64IuL9wD83Twv1fdwR2CkithnwGkuAu4E1qBMSz4+Ig0spnxgyvNWpk1qfRB0r/wB10ut+wEsi4jmllCvHiOt06mTZJdR5C6Wj/EnAV4FWAv7X5jUe19xeExHPK6X8qOO8AE5n6dChxc15zwH2iIjXD/nzEhHvBI5n2W9sVgWe2Ny2pA5barXVhwKrUdvX3R2X6/z30Os196YOr1mpOTQfWJM6b2FXYK/m38nCHpdYl/qtzeOpc0SWUH9PrwGeExE7lFL+1E8skgZnj7mkUTCPOtb6HOCRpZR1qAlKexL4bWry9EhglVLK2tRE5iDgf4C9gDe2KpdSft2Mgd+87RrblFI2bG7vGi+oiNiQmuxtBPwGeAGwevPaawG7AZ+mz6QJuBG4vXl8XERM2ljhiHgRS5PyDDy8lLIusB5wIksnk47lmdRe23cA6zQ/58bAl5vyEyJijSFDPALYDNiX+h6uSV2R5hfUxPFzY1x7F2B/6s+3TvNzPQz4MUBErM/SpPwz1KR3XillderP/Fnqh6cLok72bXcwS5Py44B1m/a3CfA54BTqh5OBNB80/42alH8aeFwppZV4b0L9kPEzWKatfqw5/Yy2dtq6jTthuvnwdRY1Kf86sEXzO1wTeDP1w8qLgPePcZl/b85/NvXD1EOBPagfHDYAju3/XZA0sFKKN2/evE3qjbrUXwE+OU69bZt6hZpIxJCv97zmGj/rUrZG22usN0g5NcEtwM3A+pP03hzW9npLgCuBD1EnhG7W5/t1T5eyHzVl5/c498y2192ro+zQtrLDu5y7Jkt7qV824M/7ubZrv6RL+UbUby8K8JYx4nrXGK9xUlPn7B7lK7S1yTe2HV+x+d0W4JQu561I7eVvxbBDj/i+1HF8NeoHsAKcOsB79cFesfTZXs9tjv+c+uG189y3NuWLgI07yq5uyu6ifjjuPPe1Tfn8Yf+devPmbfybPeaSRsUHSynDrov+TerwiG0iYq3JCKYZN/7q5unxpZTbx6rfr1LKydSE7k5qb+pTqcs5ngNc34xF/qeImDdArI8Gntw8/fce1cbqJW2Zz9Je2/aY7wIubp5u229cHX5ZSrmwy7VvAc5onu7VWd64Hzi5W0EzFGW/5umJ3eqUuhLPZ5qnz2kr2on6waDQ5X0rpSymv/et0/Op31TcDxw5xPkDi7oM54ubpx8odSJxp1Oo3y49hDoEq5tPl1K6Lf/4heZ+Teq3VpKmgIm5pFHxg7EKo3pVRFwUEX9qJqWVZtLcIur482DpGOOJ2pqahEDvNaWHUkr5KDW52Zc6XOcnLN3saHNqkviDiFi3z0tu39zfT+357Paav6QmZWP5SSnlgR5lNzf36/QZU6dLxii7tLl/co/yn5dSOsdct2xFHRcN8JVmouLf3KjDSmDZpLL1etf3SEYBvjtG3L3s2Nz/sJQy3ns+WR5PHRIG8J1uFZrf7febp73e66t6nDufpevnD9sGJI3DyZ+SRsHCUspYK42sDHwReG77OdQJl4ub5w+nJuarT1JMG7Q9nqwNZB5USrmXOvTgXKirbVB7c98F7EBdz/zDLO0NHktrdY6/NL28vfyZOja7l17JL9T3G5ZOKhzUzX2UrRoRqzfvTbuxvq1o/yD28J61lmofY75+c39Lr8qllPlRV8EZZJx5q+38cYBzJmr9tsdjvdetiZvr9ygfrw2szvBtQNI47DGXNArGSiYBDqcm5XcDh1DHx65aSlm/NJPjWLrDaNfl4IYwWdfpSynlnlLKBdTe1subw6+IiH4+aExrrFNgvPjHah+tv2NLgJVKKTHObZihOIO+vzP9++h7GJSk0WJiLml5sHdz/65SysebcckPiohVWTrsZLLc2vb4UZN87Z6aHu9PNk9XAjbt47RWj/L6Mfaa6pM1zGcYG41R1orrvi695eO5rblfgcF3xGy9bz1ji4g1GfxbmFbbefSA501E+7cKY7XXTbrUlzQiTMwlLQ9aycSPe5TvxuT/f/Yrlq51/vxJvvZ42pPTXmO+27Xel3nUYTB/IyK2ZuxhLFNtlz7Krhniur9k6e/pZQOe23q9x0REr3XlnzVETFc090+LiEHe89aym8P0uP+SpcONdutWoRkStnPzdJj3WtIUMzGXtDxoDVN5QmdBk2wcM9kv2PRcn908PbJZK3tCou58+Yw+qr6quf8/4IbxKpdSbmRpcv72HtX+qY/XnUrbNmutL6NZK/6A5ulnB71o83s6q3n61oh4TK+6EbFC0wPecjl1PHbQ5f2JiBVYujb8IL5Mnf+wCksnnfaj9QFj7TFrdVHqLrFfbJ6+LSJW6VLtUOqHs78CFwz6GpKmnom5pOXBN5v790bE3zcJExHxBOBr1BVUui0PN1HHUb/y3wj4fkQ8LyIe0rz2mhGxR0R8foDVUx4KXB51u/uDI2KLVkFErBwRO0bEhdSNjAA+1iRc/Wh9ONkrIk5u9dRGxNoR8R/Udaj72u10iswHzoqIvVvDbSLiKcA3qJs13QT815DXPoY6qXEt6u/pVe0bCUXEphFxCHX1mz1bx5uk/rjm6Zsj4uiIeGhzzkbUD2Y7MGDbKqXcB7yzefr6iDgzIh7bXDciYuOISBHxgY5Tf9Hc7xYRmw7ymo1jm1gfA3yh9SElIuZFxGEsXRLylFLKWBNEJc0QV2WRtDx4H/BS6lJ3XwUeiIj7qYnuIup24ZnaQzlpSil/jojnA1+ibp/+FWBRRNzLsr2ab+jzkoupwxV2bm5ExAPUrdrXYdkhDOcA7xkg1gsj4oPUHvNDgRQR86nJ6grA0dR1rp/M1HyIGc+HqGtnnwcsjIhF1N8f1Em9e421Ms9YSim3R8RzqT3Gm1MT6sUR8X/U8eHtkyE718o/lTrh9gDq+/3uiLiLpb/f11MT2oHaVinlv5rk/hjqyjr7RcTd1HkDrXi+3HHa16kfMDahrml/O3BfU7Z9KeXOcV7zFxGxP3Wn0b8Hft/2HrRWUvkS07S2uqTB2WMuaeSVUm4Dng6cRl3yL6jJ7OeAZ5ZSzpvC176a2iN/NHW4yEJqkvY74PPUianjbpfeXOt26hb3B1OTx59Tk+S1qePKf0Wd+Ll7KeXVA/SWt67/DmAf6hCNBdSdK38A7F1KOYaapEMdIjPd7qV+GHk/NflcmTpx8yzgSaWUKydy8Wad9idQV/D5LvVnXJs6Rv9a6geDZwLnd5xXSikHAgdRd09tjen/NrBnKWXYXnxKKccBT6P+jH+ktpsF1J77E+hIkEspC6jjw/+b2s7XpU4gfTT1d9nPa54HPInajm6iLg95L3Wt+AOou68u7HW+pJkVw2+0N0kBRBxJ7cF5CrAZcGMpZdMhrrM/cAR1Vv5dwEXAkZO1W58kLc+a4Ta3Uztk1i+l3DFNr/s54OXAO0opH5yO15Sk5dUo9JgfD+wO/J66RfXAIuII4FPUMYyHA/9J3VHvkj7XAJak2e5t1P/zr52upFySNJhRGGO+eSnleoCI+DmD7a5GRKwHvJe6jfCzW7veRcRV1PGGh1OTf0ma1SLiFOpSfd8opfylObYx9f/B1mot9lpL0oia8cS8lZRPwEupY+hObt+KupRyUURcT50UZmIuaS7YGXgTQETcRx0vvVZb+cdLKWd3O1GSNPNGYSjLRD21uf9Bl7IrgK0iYqBeeElaTr2HOtHwOuqk0tWou1B+EXhxKeWQGYxNkjSOGe8xnwStrZS7rcna2jhiI+A3vS6QUioAOedhdluTpJFQSrkQuHCm42hXStlrpmOQpOXFbEjMW5tIdFuXd2FHnWVExMHAwYcc8mAn0swuUSNJkqTZrmdH8GwYyrKgue+2+cO8jjrLKKWcWkrZYUqikiRJkgYwGxLzW5r7jbuUbUztBb+lS5kkSZI0MmZDYn5Vc/+MLmVPB3497DbPkiRJ0nQZODFPKa00FYH0IyIeFRFbRUR7DBcC9wGHRsSKbXVfBGxO3fZakiRJGmnDTP68OaV0BvCJnPPvJhpAROwHPLp5uj6wckS8u3l+YynlrLbqZwK7AJsBNwCUUm6PiH+lbprxrYj4b+oQlrdRlww7aaIxSpIkSVNtmMR8BeAdwNtTShcDHwcuzDn/dcgYDqIm2+2Oa+4vpa7JO6ZSygkR8T/AEcBHgLuA84B3OoxFkiRJy4NhEvONgL2Ag4FnA7sDf0kpnQ6clnP+wyAXK6XsOhl1SymfBD45yGtLkiRJoyJKGX7p7pTSlsAbgP2BhwFLgG8A/wlclHNeMhlBTrW2DYZmOhRJkiTNbj3XMZ9QYt6SUlqZ2ov+euqwlAL8GTgNODXnPNLLFZqYS5IkaZpM7QZDOecHgC8DF1DXDA/qkJejgD+klE5KKXXbAEiSJEkSw40xX0ZKaUfqcJZ9qDtt3kWdgHk68GTgrcBh1J05D5no60mSJEmz0VCJeUrpocB+1IR8W2oP+TXAx4Bzcs73NVV/mlI6C/gadaiLibkkSZLUxcCJeUrpNOAVwGrA/dTlDHPO+cpu9XPOi1NKl1BXb5EkSZLUxTA95v8I/J66fvkZOef/7eOcS4Bjh3gtSZIkaU4YJjF/Xs7564OckHO+DLhsiNeSJEmS5oRhVmXZIKX0xLEqpJS2TSntP2RMkiRJ0pwzTGL+SeCl49R5CXDGENeWJEmS5qRJWce8ixWpmwxJkiRJ6sNUJeZbAndO0bUlSZKkWaevyZ8ppdM7Dr00pbRpl6orAo8C/o66E6gkSZKkPvS7KssBbY8LsF1z66YAPwSOGD4sSZIkaW7pNzHfrLkP4HrgJODDXeotBu7MOd87CbFJkiRJc0ZfiXnO+cbW45TSMcB32o9JkiRJmpiBNxjKOR8zFYFIkiRJc9m4iXlK6VHNw5tzzovbno8r5/zHoSOTJEmS5pB+esxvoE7o3Br4Tdvz8ZQ+ry9JkiTNef0kzmdSk+z5Hc8lSZIkTZIoxRw7pVQAcs4zHYokSZJmt+hVMFU7f0qSJEkagIm5JEmSNAL6WZXl9CGvXXLOBw15riRJkjSn9DP584Ahr10AE3NJkiSpD/0k5ptNeRSSJEnSHDduYp5zvnE6ApEkSZLmMid/SpIkSSOgn8mfj2oe3pxzXtz2fFw55z8OHZkkSZI0h/QzxvwG6kTOrYHftD0fT+nz+pIkSdKc10/ifCY1yZ7f8VySJEnSJIlSzLFTSgUg5zzToUiSJGl2i14FTv6UJEmSRsCExoCnlB4JbA+sRR3q8uOc802TEZgkSZI0lwyVmKeUHgtkYPcuZRcDb8o5/2aCsUmSJElzxsBDWVJKWwCXA88GrqdOBv2P5v765vj3m3qSJEmS+jBMj/m/AQ8DDgc+mnNe0ipIKa0AHAZ8CDge2GcygpQkSZJmu2ES82cDX8k5n9xZ0CTpH04p7QnsMdHgJEmSpLlimMR8ZeDacepcCzxriGtLkiRpCpx1af/T//bbZcspjES9DLNc4k+A8caPbwH8dIhrS5IkSXPSMIn58cDLUkrP61aYUnoB8A/A+yYSmCRJkjSXjDuUJaW0f5fDXwW+lFL6NvBd4DZgA2AX6hKKFwHr9RNARKxAnUj6BmBT4HbgPOCoUsq9fZzfa+vSe0spa/QTgyRJkjTT+hlj/kmgM/ltbSW6B90neb4YeBF1CcXxfAh4M3ABcAKwdfN8+4jYo5SyZKyTG98DTu04tqiP8yRJkqSR0E9ifuBUvXhEbENdXvH8UsrL247/AfgIsC9wTh+Xur6U8umpiVKSJEmaeuMm5jnnT03h67+S2vt+UsfxTwDvB15Df4k5EbEysHIp5Z5JjVCSJEmaBsNM/pxMTwWWAFe2HyylLKQuufjUPq+zF7AAuDsi/hIRJ0fEWpMaqSRJkjSFZjox3wi4o5Ryf5eym4H1mp7wsVwJHE1Nzl8LXAwcCnwvIsac/BkRB0fE1QNHLUmSJE2yYTYYIqW0OpCA5wIbA6t0qVZyzpuPc6nVgG5JOcDCtjoP9LpAKeXpHYfOjIifUpdrPJwxlm0spZwKnJpS6rWyiyRJkjQtBu4xTymtDfwQ+HdgB+BxwDrU5RI3bW4r93ntBXRP6gHmtdUZ1AeoyfwLhjhXkiRJmnbDDGV5N/B44CBqQg51ycM1gJ2Aa4DfU5c9HM8t1OEq3ZLzjanDXHr2lvdSSlnUuvag50qSJEkzYZjE/MXAd3POZ+ScHxwCknMuOecrgOcDWwH/0se1rmpieFr7wYiYB2wHDDX+uzl/E+rGR5IkSdLIGyYxfyS1V7xlCW3DUXLOf6HuDLpvH9c6l7p50Vs6jr+eOrb87NaBiNg8IrZqrxQRD+tx3eOo4+cv6iMGSZIkacYNM/lzAbC47fl8YMOOOrdRh6KMqZTys4j4KHBoRJwPfIWlO39eyrJrmH8beDRLdx0FeHdE7Ah8B/gjdTjN84HdqOPgT+7/x5IkSZJmzjA95jdRe81bfgk8K6W0YtuxnYFb+7zeW4C3A9sAH6X2tJ8MvLCUsmSccy8B7qIuk3gScAywLnUYza6llPv6jEGSJEmaUcP0mF8K7JNSimaM+bnAR4Avp5QuAnYFdgQ+1s/FSimLgROa21j1Nu1y7ELgwkGClyRJkkbRMIn5p6jLIW5C7T3/OLA78FJgz6bOZdTVWyRJkiT1YeDEPOd8DXBI2/O/Ai9LKT0F2AK4Abgq5zzeMBRJkiRJjaF2/uwm5/wj4EeTdT1JkiRpLplQYp5SWom6ispa1NVZfpVzXjQZgUmSJElzyVCJeUrpYcD7gVcB89qKFqaUzgGOzDnfMQnxSZIkSXPCwIl5SmkD6uTOx1B7ya+kLo24IXW3zoOA3VJKz8w5u/OmpKGcdelvBqq/3y5bTlEkkiRNj2F6zI+nJuUnAUfnnO9qFaSU1qSuJX448D7gdZMRpCRJkjTbDZOYvxD4Xs75rZ0FTZJ+REppB+BFEw1OkiRJmiuG2fnzocD3x6nzPWCNIa4tSZIkzUnDJObXAY8Yp84jgF8PcW1JkiRpThomMf8w8IqU0hO7FaaUtgP2oY5BlyRJktSHcceYp5Se1XHoD8A3gStTSmcC3wVuAzYAdgH2A75K3QFUkiRJUh/6mfx5CVC6HA/qqisHdRwDeAnwYmDFiQQnSZIkzRX9JObH0j0xlyRJkjRJxk3Mc85HT0MckiRJ0pw2zORPSZIkSZNsmA2GHpRS2hnYHlgbmA9ck3Meb41zSZIkSR2GSsxTSk8GPg08rjkUNOPQU0q/BvbPOV89KRFKkiRJc8DAQ1lSSlsAFwNbAZcBxwGHNPffb45/M6X02EmMU5IkSZrVhukx/1dgDeAVOefPdpQdnVLaC/gM8G7gtROMT5IkSZoThpn8uQfwhS5JOQA5588BFzb1JEmSJPVhmMR8PeC6cepc19STJEmS1IdhEvPbgcePU2cr4I4hri1JkiTNScMk5hcDL04p7dutMKX0cuAlwLcmEpgkSZI0lwwz+fNYauJ9dkrpTcB3gD8DGwK7AjsDdwPvnaQYJUmSpFlv4B7znPPvqBM7fwM8k7r6yinU1Vr+rjm+Z875t5MYpyRJkjSrDbXBUM75KmDrlNJOwJOBtag7f/4453zZJMYnSZIkzQkDJ+YppWcBd+Wcr805Xw5cPvlhSZIkSXPLMJM/vwMcPNmBSJIkSXPZMIn5HcB9kx2IJEmSNJcNk5hfAuw0yXFIkiRJc9owifm7gcellI5LKa002QFJkiRJc9Ewq7IcCfwceBdwUErpJ8CtQOmoV3LOB00wPkmSJGlOGCYxP6Dt8YbNrZsCmJhLkiRJfRgmMd9s0qOQJEmS5riBE/Oc841TEYgkSZI0lw2UmKeUHgU8lTpM5aqc801TEpUkSZI0x/S9KktK6YPA9cB5wGeBP6SUPjBVgUmSJElzSV+JeUrpVcBbgQCuA37dPH5rSumVUxeeJEmSNDf022N+EPBXYI+c8zY558cDzwWW4MorkiRJ0oT1m5g/EfhCzvk7rQM5528BFwLbTTSIiFghIo6IiOsiYmFE3BQRJ0TE6tNxviRJkjTT+k3M16EOX+l0HbD2JMTxIeBE4JfAYdQx7G8GLoqIfmKc6PmSJEnSjOp3VZYVgEVdji+ijjUfWkRsQ02mzy+lvLzt+B+AjwD7AudM1fmSJEnSKBikN7lMUQyvpCb3J3Uc/wSwAHjNFJ8vSZIkzbhB1jE/OqV0dLeClNLiLodLzrmf6z+VOon0ymVOLmVhRFzblE/l+erTWZf+pu+6++2y5RRGMnsM8p6C76skSbPZIIn5oENW+q2/EXBHKeX+LmU3AztFxMqllAem6PwHpZT6DFnj+cG5Mx3B7OT72pvvjSRNHv9PnVIl59w1T+4rMc85T+UEytWAbkk1wMK2Or0S66HPj4iDgYMPOeSQ/iKVJEmSpsggPeZTZQHw8B5l89rqTPr5pZRTgVPHC3AmRMTVpZQdZjoOLV9sNxqG7UbDsu1oGLab3kZhKcFbgPUiYpUuZRtTh6mMNQxloudLkiRJM24UEvOrqHE8rf1gRMyjbl509RSfL0mSJM24UUjMz6UuxfiWjuOvp44NP7t1ICI2j4ithj1/OTOSQ2w08mw3GobtRsOy7WgYtpseopSpWp58gCAiTgYOBS4AvgJsTd258zJg91LKkqbeDcCjSykxzPmSJEnSqBqVxHxFao/3wcCmwB3UnvCjSin3tNW7ge6JeV/nS5IkSaNqJBJzSZIkaa4bhTHmAiJihYg4IiKui4iFEXFTRJwQEavPdGwaDRFxZER8NiKuj4jSfIM0Vv2nR8S3IuLuiLgrIr4WEdtNU7gaARGxZUQcGxFXRMTtTVu4NiL+pdv/LRHxuIj4QkTcGRH3RsT3ImL3mYhdM6dpB2dHxK8iYn5ELGj+Np0YEY/oUd92o78REatFxB+av1mndCm37XQYhXXMVX2IOi7+AuAElo6T3z4i9nCcvIDjgf8FrgHWHqtiROwIXELd/fao5vChwPciYqdSys+mME6Njn8E3gR8kToRfhGwG/BeYJ+I2LGUch/UyfXA5cBfgf8A5lMn0X89Ip5XSvnWDMSvmbEJ8Ajq36M/UdvEE6jDRfeNiO1KKX8B243GdSywXrcC2053DmUZARGxDfAz4IJSysvbjh8GfAR4dSnlnJmKT6MhIh5TSrm+efxzYI1SyqY96l4JbAVsXUq5uTm2MfAr4IpSyp7TE7VmUkTsAPy2lDK/4/h7gX8BDiulnNIcOw94OfCUUsq1zbE1gF9Qd1HeqvgHY06LiL2B84B/LqX8R3PMdqOuIuLJwJXAP1E7HD9aSjm0rdy204VDWUbDK4EATuo4/gnqrqWvmfaINHJaSfl4ImIL4KnAZ1tJeXP+zcBngT0iYsOpiVKjpJRydWdS3ji3ud8WoBnW8mLgktYfyOb8e4DTgC2pbUpz243N/Tpgu1FvzaIcnwC+Bpzfpdy204OJ+Wh4KrCE+snyQaWUhcC1zNHGqaG12ssPupRdQf0Q+JTpC0cjaJPm/rbm/onAKvRuM+D/Q3NORMyLiPUiYpOI2BP4z6boK8297Ua9HEH91vbQHuW2nR5MzEfDRsAdpZT7u5TdDKwXEStPc0xafm3U3N/cpax1bONpikUjpunJOoo6rrM1RM42o25eB9wO3AR8nTq35TWllO815bYb/Y2I2Aw4Bji2lHJDj2q2nR6c/DkaVgO6JeVQx1m16jwwPeFoObdac9+tTS3sqKO55yRgR+BdpcXiPOIAAAcMSURBVJRfN8dsM+rmC8B1wBrA9tShB+u3ldtu1M3HgD8AJ45Rx7bTg4n5aFgAPLxH2by2OlI/Wm1llS5ltqc5LCKOo361fGop5d/aimwz+hullD9RV2UB+EJEfB64KiJWbdqP7UbLiIjXAHsCzyqlLBqjqm2nB4eyjIZbqMNVujXQjanDXOwtV79uae67fQ3YOtbt60PNYhFxNPBu4AzgjR3FthmNq5TyU+DHQGoO2W70oCaHOZE6B+HWiNiiWYzg0U2VtZpja2Pb6cnEfDRcRf1dPK39YETMA7YDrp6JoLTcuqq5f0aXsh2BAvxo+sLRTIuI9wDvAc4EXtdlCbKfUb9S7tVmwP+HVK0KrNs8tt2o3arUoU4vAH7bdrukKX9N8/x12HZ6MjEfDedSk6W3dBx/PXWM1dnTHpGWW6WU31H/Q9s7IloTbGge7w1cXEq5dabi0/SKiKOAo4GzgAO7bVbWLFF2EbBrRDyp7dw1qH9Ef0vHqlGavXotpxoRu1GX2LwCbDf6G/dS/8Z03lrfsHytef5F205vbjA0IiLiZOrYzwuoXwO1dv68DNjdnT8VEfux9CvBw4CVqZs2ANxYSjmrre5OwHeo40NPbjtnA+CZpZSfTEvQmlER8SbgFOCPwL9Sl2Vtd1sp5ZtN3S2ofwgXUXcivovaOfAE4AWllK9PV9yaWRFxAXXnz4upa5fPoy6xui913O+ubRvC2G40pojYlDoZtHODIdtOFybmI6JZwuwt1C2PNwXuoPakH9V8stQcFxGXALv0KL60lLJrR/1nULdefzr1G5nLgSNLKddMYZgaIRHxSeC1Y1RZpt1ExNbA+6ntbGXgGuDoubo19lwVEftQ280TqUMTCjVB/ybwgVLKHzvq227UU6/EvCmz7XQwMZckSZJGgGPMJUmSpBFgYi5JkiSNABNzSZIkaQSYmEuSJEkjwMRckiRJGgEm5pIkSdIIMDGXJEmSRsBDZjoASdJoSCltSt0I5FM55wNmNhpJmntMzCVplkspbQW8CdgNeCSwKnV34R8D5wNn55wXzlyEkiQwMZekWS2ldBTwHurQxSuATwH3ABsAuwKnAYcAO8xQiJKkhom5JM1SKaV3AccANwF755x/2KXOC4G3TXdskqS/ZWIuSbNQM178aGAR8Pyc88+71cs5fyml9M1xrrUl8I/AHsCjgTWBW4GvA8fmnP/UUT+A/YE3AI8FHgrcDvwSOD3nfG5b3ScCRwLPAB4B3EX9IPFd4B0550WD/NyStDxzVRZJmp0OBFYCPt8rKW/JOd8/zrVeBryRmjD/N3AyNcl+HXBVSmnjjvrvAz4JbAicB5wIfAvYGNi7ValJyn8IvIQ6zObEpv7tQAJWGScuSZpV7DGXpNlp5+b+25NwrbOAD3Um8CmlPYGvAu+mjlNveQNwM7BtznlBxznrtT19LTAPeGnO+cKOeusAy5wrSbOdibkkzU6PaO7/NGatPuScb+5x/BsppV8Az+1SvAhY3OWcO7rUva9LvTsHjVOSlncm5pI0O0VzXyZ6oWbM+KuBA4AnAesAK7ZVeaDjlLOBw4BfpJQ+C1wK/CDnPL+j3rnA4cAXUkqfow53uSzn/PuJxixJyyPHmEvS7HRLc7/JJFzrROpwlsdTJ3yeQF3t5RjgRmDljvpHAG8B7gXeSR3uckdK6cKU0hatSjnnK4G/Ay4G9qIu5fi7lNJ1KaVXTkLckrRcscdckman7wO7A88G/mvYi6SUHg68Gfg5sFPO+e6O8r9JoHPOi4EPAx9uzt8Z2Jc68XOblNI2rfHqOecfAC9MKa0CPAX4e2pv+zkppdtzzt8aNnZJWt7YYy5Js9MZ1HHeL08pPX6sik1S3MtjqH8rvtElKd+kKe8p5/yXnPP5Oed9qD3jmwPbdql3f8758pzzUdQPAlBXa5GkOcPEXJJmoZzzDdR1zFcGvpxS6rqzZ0rp76lDTXq5obnfOaX04LjylNIawCfo+OY1pbRKSunZzbj09uMrAes2Txc0x/4upbRWl9fcoL2eJM0VDmWRpFkq53x8SukhwHuo641fDlwN3ENNfp9F3QDo6jGucWtK6TPUoSjXppS+AawFPAdYCFwLbNd2yqrUSZw3pJR+SB2DPq+pvzXwxZzzr5q6bwP2TCldAlzfxLUN8DzgTuDUib4HkrQ8scdckmaxnPOx1KEjp1AT6gOBdwAvAH5P3SRo554XqA4Cjqcm3W+iLo/4JWAnoHOllXuBfwaua8oPB15F3dHzENo2GAIydWWWTYH9qGPLt2yOb+/qLJLmmihlwitpSZIkSZoge8wlSZKkEWBiLkmSJI0AE3NJkiRpBJiYS5IkSSPAxFySJEkaASbmkiRJ0ggwMZckSZJGgIm5JEmSNAJMzCVJkqQRYGIuSZIkjYD/B4Q9y6gLf4x9AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 864x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Prediction on the left, real stuff on the right :\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 648x169.2 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "oups, that's wrong ;-(\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# ---- Get a random image\n",
     "#\n",
diff --git a/GTSRB/04-Data-augmentation.ipynb b/GTSRB/04-Data-augmentation.ipynb
index 870955df5a7f8f2201be5899a0cae56af82e3a69..6a8bfc9781e8297a48643cf9d790ee87fc41196e 100644
--- a/GTSRB/04-Data-augmentation.ipynb
+++ b/GTSRB/04-Data-augmentation.ipynb
@@ -7,7 +7,7 @@
     "German Traffic Sign Recognition Benchmark (GTSRB)\n",
     "=================================================\n",
     "---\n",
-    "Introduction au Deep Learning  (IDLE) - S. Aria, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020\n",
+    "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020\n",
     "\n",
     "## Episode 4 : Data augmentation\n",
     "\n",
@@ -19,22 +19,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IDLE 2020 - Practical Work Module\n",
-      "  Version            : 0.1.4\n",
-      "  Run time           : Monday 13 January 2020, 21:15:42\n",
-      "  Matplotlib style   : idle/talk.mplstyle\n",
-      "  TensorFlow version : 2.0.0\n",
-      "  Keras version      : 2.2.4-tf\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
@@ -49,7 +36,7 @@
     "import seaborn as sn\n",
     "import os, time, random\n",
     "\n",
-    "import idle.pwk as ooo\n",
+    "import fidle.pwk as ooo\n",
     "from importlib import reload\n",
     "\n",
     "ooo.init()"
@@ -67,20 +54,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset \"set-24x24-L-LHE\" is loaded. (228.8 Mo)\n",
-      "\n",
-      "CPU times: user 8 ms, sys: 132 ms, total: 140 ms\n",
-      "Wall time: 1.32 s\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -114,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,59 +113,6 @@
     "    model.add( keras.layers.Dropout(0.5))\n",
     "\n",
     "    model.add( keras.layers.Dense(43, activation='softmax'))\n",
-    "    return model\n",
-    "\n",
-    "# A more sophisticated model\n",
-    "#\n",
-    "def get_model_v2(lx,ly,lz):\n",
-    "    model = keras.models.Sequential()\n",
-    "\n",
-    "    model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))\n",
-    "    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
-    "    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
-    "    model.add( keras.layers.Dropout(0.2))\n",
-    "\n",
-    "    model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
-    "    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
-    "    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
-    "    model.add( keras.layers.Dropout(0.2))\n",
-    "\n",
-    "    model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))\n",
-    "    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
-    "    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
-    "    model.add( keras.layers.Dropout(0.2))\n",
-    "\n",
-    "    model.add( keras.layers.Flatten())\n",
-    "    model.add( keras.layers.Dense(512, activation='relu'))\n",
-    "    model.add( keras.layers.Dropout(0.5))\n",
-    "    model.add( keras.layers.Dense(43, activation='softmax'))\n",
-    "    return model\n",
-    "\n",
-    "# My sphisticated model, but small and fast\n",
-    "#\n",
-    "def get_model_v3(lx,ly,lz):\n",
-    "    model = keras.models.Sequential()\n",
-    "    model.add( keras.layers.Conv2D(32, (3,3),   activation='relu', input_shape=(lx,ly,lz)))\n",
-    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
-    "    model.add( keras.layers.Dropout(0.5))\n",
-    "\n",
-    "    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
-    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
-    "    model.add( keras.layers.Dropout(0.5))\n",
-    "\n",
-    "    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
-    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
-    "    model.add( keras.layers.Dropout(0.5))\n",
-    "\n",
-    "    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
-    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
-    "    model.add( keras.layers.Dropout(0.5))\n",
-    "\n",
-    "    model.add( keras.layers.Flatten()) \n",
-    "    model.add( keras.layers.Dense(1152, activation='relu'))\n",
-    "    model.add( keras.layers.Dropout(0.5))\n",
-    "\n",
-    "    model.add( keras.layers.Dense(43, activation='softmax'))\n",
     "    return model"
    ]
   },
@@ -203,37 +126,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "total 8\n",
-      "drwxr-x--- 3 paroutyj l-simap 4096 Jan 13 21:28 logs\n",
-      "drwxr-x--- 2 paroutyj l-simap 4096 Jan 13 21:33 models\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%bash\n",
     "# To clean old logs and saved model, run this cell\n",
     "#\n",
-    "#/bin/rm -r ./run/logs 2>/dev/null\n",
-    "#/bin/rm -r ./run/models 2>/dev/null\n",
-    "/bin/ls -l ./run  2>/dev/null"
+    "/bin/rm -r ./run/logs   2>/dev/null\n",
+    "/bin/rm -r ./run/models 2>/dev/null\n",
+    "/bin/mkdir -p -m 755 ./run/logs\n",
+    "/bin/mkdir -p -m 755 ./run/models\n",
+    "echo -e \"Reset directories : ./run/logs and ./run/models .\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ooo.mkdir('./run/models')\n",
-    "ooo.mkdir('./run/logs')\n",
-    "\n",
     "# ---- Callback tensorboard\n",
     "log_dir = \"./run/logs/tb_\" + ooo.tag_now()\n",
     "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
@@ -257,18 +169,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset \"set-48x48-L-LHE\" is loaded. (913.9 Mo)\n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "x_train,y_train,x_test,y_test = read_dataset('set-48x48-L-LHE')"
    ]
@@ -282,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,17 +209,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Images of the dataset have this folowing shape :  (48, 48, 1)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "(n,lx,ly,lz) = x_train.shape\n",
     "print(\"Images of the dataset have this folowing shape : \",(lx,ly,lz))"
@@ -331,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,79 +250,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train for 613 steps, validate on 12630 samples\n",
-      "Epoch 1/30\n",
-      "613/613 [==============================] - 14s 23ms/step - loss: 2.6682 - accuracy: 0.2347 - val_loss: 1.0431 - val_accuracy: 0.6518\n",
-      "Epoch 2/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 1.1481 - accuracy: 0.6272 - val_loss: 0.3927 - val_accuracy: 0.8698\n",
-      "Epoch 3/30\n",
-      "613/613 [==============================] - 13s 22ms/step - loss: 0.7645 - accuracy: 0.7480 - val_loss: 0.2486 - val_accuracy: 0.9294\n",
-      "Epoch 4/30\n",
-      "613/613 [==============================] - 13s 21ms/step - loss: 0.6177 - accuracy: 0.7999 - val_loss: 0.1862 - val_accuracy: 0.9454\n",
-      "Epoch 5/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.5352 - accuracy: 0.8278 - val_loss: 0.1689 - val_accuracy: 0.9519\n",
-      "Epoch 6/30\n",
-      "613/613 [==============================] - 14s 23ms/step - loss: 0.4701 - accuracy: 0.8480 - val_loss: 0.1500 - val_accuracy: 0.9527\n",
-      "Epoch 7/30\n",
-      "613/613 [==============================] - 13s 21ms/step - loss: 0.4300 - accuracy: 0.8629 - val_loss: 0.1231 - val_accuracy: 0.9670\n",
-      "Epoch 8/30\n",
-      "613/613 [==============================] - 13s 21ms/step - loss: 0.4069 - accuracy: 0.8700 - val_loss: 0.1172 - val_accuracy: 0.9670\n",
-      "Epoch 9/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.3778 - accuracy: 0.8783 - val_loss: 0.1282 - val_accuracy: 0.9640\n",
-      "Epoch 10/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.3633 - accuracy: 0.8856 - val_loss: 0.1058 - val_accuracy: 0.9709\n",
-      "Epoch 11/30\n",
-      "613/613 [==============================] - 14s 23ms/step - loss: 0.3473 - accuracy: 0.8882 - val_loss: 0.1081 - val_accuracy: 0.9687\n",
-      "Epoch 12/30\n",
-      "613/613 [==============================] - 13s 22ms/step - loss: 0.3371 - accuracy: 0.8936 - val_loss: 0.1014 - val_accuracy: 0.9710\n",
-      "Epoch 13/30\n",
-      "613/613 [==============================] - 13s 22ms/step - loss: 0.3326 - accuracy: 0.8968 - val_loss: 0.0883 - val_accuracy: 0.9753\n",
-      "Epoch 14/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.3214 - accuracy: 0.9001 - val_loss: 0.0966 - val_accuracy: 0.9714\n",
-      "Epoch 15/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.3164 - accuracy: 0.9026 - val_loss: 0.0987 - val_accuracy: 0.9703\n",
-      "Epoch 16/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.3052 - accuracy: 0.9056 - val_loss: 0.1026 - val_accuracy: 0.9689\n",
-      "Epoch 17/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.3035 - accuracy: 0.9072 - val_loss: 0.0888 - val_accuracy: 0.9751\n",
-      "Epoch 18/30\n",
-      "613/613 [==============================] - 13s 21ms/step - loss: 0.2957 - accuracy: 0.9090 - val_loss: 0.0850 - val_accuracy: 0.9764\n",
-      "Epoch 19/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.2931 - accuracy: 0.9098 - val_loss: 0.0853 - val_accuracy: 0.9747\n",
-      "Epoch 20/30\n",
-      "613/613 [==============================] - 13s 21ms/step - loss: 0.2868 - accuracy: 0.9118 - val_loss: 0.0862 - val_accuracy: 0.9760\n",
-      "Epoch 21/30\n",
-      "613/613 [==============================] - 13s 21ms/step - loss: 0.2874 - accuracy: 0.9126 - val_loss: 0.0872 - val_accuracy: 0.9754\n",
-      "Epoch 22/30\n",
-      "613/613 [==============================] - 13s 20ms/step - loss: 0.2823 - accuracy: 0.9129 - val_loss: 0.0858 - val_accuracy: 0.9762\n",
-      "Epoch 23/30\n",
-      "613/613 [==============================] - 13s 21ms/step - loss: 0.2825 - accuracy: 0.9137 - val_loss: 0.0796 - val_accuracy: 0.9770\n",
-      "Epoch 24/30\n",
-      "613/613 [==============================] - 13s 21ms/step - loss: 0.2802 - accuracy: 0.9144 - val_loss: 0.0805 - val_accuracy: 0.9752\n",
-      "Epoch 25/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.2767 - accuracy: 0.9172 - val_loss: 0.0764 - val_accuracy: 0.9762\n",
-      "Epoch 26/30\n",
-      "613/613 [==============================] - 12s 19ms/step - loss: 0.2770 - accuracy: 0.9166 - val_loss: 0.0842 - val_accuracy: 0.9760\n",
-      "Epoch 27/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.2802 - accuracy: 0.9164 - val_loss: 0.0920 - val_accuracy: 0.9724\n",
-      "Epoch 28/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.2729 - accuracy: 0.9189 - val_loss: 0.0774 - val_accuracy: 0.9783\n",
-      "Epoch 29/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.2769 - accuracy: 0.9181 - val_loss: 0.0838 - val_accuracy: 0.9759\n",
-      "Epoch 30/30\n",
-      "613/613 [==============================] - 12s 20ms/step - loss: 0.2724 - accuracy: 0.9187 - val_loss: 0.0812 - val_accuracy: 0.9768\n",
-      "CPU times: user 6min 33s, sys: 11.1 s, total: 6min 45s\n",
-      "Wall time: 6min 30s\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%time\n",
     "\n",
@@ -457,17 +282,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max validation accuracy is : 0.9783\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "max_val_accuracy = max(history.history[\"val_accuracy\"])\n",
     "print(\"Max validation accuracy is : {:.4f}\".format(max_val_accuracy))"
@@ -475,18 +292,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test loss      : 0.0812\n",
-      "Test accuracy  : 0.9768\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "score = model.evaluate(x_test, y_test, verbose=0)\n",
     "\n",
@@ -504,38 +312,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ooo.plot_history(history)"
    ]
@@ -544,29 +323,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 8/ Restore and evaluate best model"
+    "## 8/ Evaluate best model"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 8.1/ Restore model :"
+    "### 8.1/ Restore best model :"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "loaded_model = tf.keras.models.load_model('./run/models/best-model.h5')\n",
     "# best_model.summary()\n",
@@ -582,18 +353,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test loss      : 0.0774\n",
-      "Test accuracy  : 0.9783\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
     "\n",
@@ -610,22 +372,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAAIOCAYAAAAFqVF0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzde5wlVXXo8d8aXuPwVEEJGMDgm6iogEC8KoT4vMYnRhPim0FLEIhIUCMv8XE1IIKUOpj4fqAGvOSKGlFAIxIZEUEjosCgMooSERiGGRxn3T/qtBya0+fR06erTtfv+/mcz6Gr9qlem+qZXrN27b0jM5EkSVJzLao7AEmSJPVnwiZJktRwJmySJEkNZ8ImSZLUcCZskiRJDWfCJkmS1HAmbJIkSQ1nwiZJktRHRLwxIj4XEddGREbEille5yUR8b2IuCMiboyID0XEdkN9tmkL50bExsASYHVmrqs7HkmS1G4RkcBvgcuAxwG3ZuYuI17jSOAU4CLgU8ADgH8Argf2yszb+36+CQlbRLwIOAjYE9i269RNwKXAJzPz03XEJkmS2i0i/iwzr+389w+ALUZJ2CJiW6rE7IfAPpn5h87xZwHnAm/OzLf3vUadCVtELKEKdH9gNXA5cAOwBlgM7AjsDtwLuBB4VmauriVYSZLUerNM2F4FnAm8JDM/Pu3cNcDazHxEv2tsPItY59KJwBOB1wFnZuba6Q0iYjNgKXAycALwhnmNUJIkacPs2Xn/do9zlwAvjogtMnPVTBeoe9LBC4HTMvN9vZI1gMxcm5mnA6cDL5rX6CRJkjbcDp33G3qcuwGIrjY91V1h2w740ZBt/5u7P9/WU0QsparI8cEPfvBxhxxyyMALN+E5PkmSGixqD6B68H8cDqGTN3Qsy8xlc/w9lnTeexWn1kxr01Pdz7D9CPhBZh44RNt/Ax6RmQ8f4VtkxOCfMRM2SZL6WrAJW2aO1LdZPsP278D/BpZk5h3Tzr2L6nGvh2bm1TNdo+4h0WXA8yPisxGxb0Rs0n0yIjaJiL+IiM8Bz+m0lyRJLRNjes2TlZ33HXuc2xHIrjY91T0keiqwC3Ao8HxgfUTcRFUy3IxqCHQqqTyj016SJGmSXEo17LoP8NNp5x4P/LjfhANozjpsjwD+FtiD6qG7JVTLfKyk6uRZmfmDWVy6/s5JkjT5ah8S3XhMQ6Lr5nhINCJ2ospjrsnM33eObUe1DtuVwL491mF7S2ae1Pf7NiFhG6MF3TlJkuZJqxO2iPh7YOfOl4cBm1ItNwZwfffaahFxIfAk4IGZuaLr+OuBf6ZaV/bTVEOhrwd+Duw5ERW2MRqpc5sNMUFh7bj/f60fYjeuRRsP13ZR3SPekqQFovaEbZMxJWy/Hy5hu5AqCevlosx8co+2d0vYOudeBhwJPBS4Ffh/wDGZ+euBMZiw3cWETZKknmpP2DYbU8K2dsQh0brUPUtUkiRJA1iCkSRJjdf2ClPb+y9JktR4VtgkSVLjTcSDZmNkwiZJkhqv7UOCbe+/JElS41lhkyRJjeeQqP5o7GusDWOUtdNcZ02SpFbwN74kSWo8K2yalY2H2BVhXd27IliBkyQtEG1/6L7t/ZckSWo8SzCSJKnx2l5hanv/JUmSGs8KmyRJary2TzqwwiZJktRwVtgkSVLjtb3CZMImSZIazyFRSZIkNZoVtlka+6K4w3BhXElSS7S9wtT2/kuSJDWeJZp5sNUQ21jdOpuK3e9W9D+/zS6jX1OSpAZq+zNsJmySJKnx2p6wOSQqSZLUcFbYJElS47W9wtT2/kuSJDWeFTZJktR4bX+GzYRNkiQ1XtuHBNvef0mSpMazwiZJkhqv7RUmE7Z5MKtFcYfhwriSJLWCCZskSWo8Jx2oUbYcYhur25qw8TzAnasGt9l0i/HHIUmTaN2awW02Xjz+OCZE24dE295/SZKkxrPCJkmSGq/tQ6JW2CRJkhrOCpskSWo8K2ySJElqNCtskiSp8dpeYTJhkyRJjdf2hK3t/ZckSWo8K2wN05hFcYfhoriSNHsuijsSJx1IkiSp0aywzYcxbeG02YBtrNZOUrVOkqQ+2l5hamTCFhFLgMOBZwLbAjcC5wLvy8y1dcYmSZLmn0OiNYuIWyPiwK6vtwIuBt4G7AFsCuwDvAv4ekRsWkugkiRJNak9YQO2ADbp+vok4FHAm4EtMvPPgC2BU6gStyPnPUJJklSrRWN6TYomxvo3wGcy8x2ZuQ4gM9dm5huACzrnZxQRSyNieUQsX7Zs2TyEK0mSNF6NeoYtIjYHtgPOm6HJecDx/a6RmcuAqUzNp+4lSVoA2v4MW6MSNuBO4A/ArTOcXwVsNH/hSJKkJjBha4alEXFA57/vAHadod1OwP/MT0iSJEnN0JSE7Ymd15QXAu/p0W4/4AfzEpEkSWqMJj50P59qT9gyc6h7EBH3AS4BLhxrQOMwpi2cXBhXkqR2iFzYv/QXdOckSZontT9CtmfEWH6nX5pZe9+GUXuFTeO354AtrAAuXdiJu6T5cP4xg9sc8M7xx6HhrV83uM0iU4Um8C5IkqTG8xk2SZKkhmt7wtb2/kuSJDWeFTZJktR4EzEzYIyssEmSJDWcFTZJktR4ba8wmbBJkqTGc0hUkiRJjeZOB5IkaZDaC1xPHNNOB9+YkJ0OrLBJkiQ1nM+wTbJ1a/qf33jxyJe81xDbWN0xVZUd9P1nGYMkSdO1vcJkwiZJkhqv7Qlb2/svSZLUeFbYJElS403EzIAxssImSZLUcFbYJElS47W9wtT2/kuSJDWeFTZJktR4bX+GzYRtko1hjbM7Rtn5wjXWJEnzpO1Dgm3vvyRJUl8RsSgijoyIqyJiTUT8PCJOjojNh/z8FhHxpoi4MiJui4ibIuLiiHhZxBAr1mOFbX6s+tXgNltsP/p116/rf37ReG/vlkP8jN22sPeqlSTNk5qHRN8DvA44BzgZeHjn68dExAGZuX6mD0bEIuBLwL7AR4HTgSXAi4EPd671j4MCMGGTJEmaQUTsBhwGnJ2Zz+86fh1wGvAi4FN9LvF44AnAqZl5ZNfnS+Aq4BCGSNgcEpUkSY23aEyvIbyYqsB36rTjZwKrgYMGfH6rzvvK7oOZeSdwE3D7MEFYYZMkSY1X45DonsB64DvdBzNzTURc3jnfz3eA3wFHR8QK4L+AewEvAx4HvHqYIEzYJEmSZrYDcFNmru1x7gZg34jYtFMxu4fMvDki/hr4EPDZrlO3Ac/PzC8ME4RDopIkqfFiXK+IpRGxvOu1dNq3XgL0StYA1nS16WcV8APgn4HnAa8Cfgp8KiL+amDnscImSZJaLDOXAcv6NFkN3G+Gc4u72vQUEY8ELgaOzMwPdB3/NFUSd2ZE7JqZf+gXpxU2SZLUeDVOOlgJbBsRm/U4tyPVcGnP4dCOI6kSu891H8zM1cAXgZ2BXQYFYcImSZIar8aE7dJO0726D0bEYmB3YPmAz+/Yed+ox7mNp73PyCHR+TCbRXGHMeaFcQdxUVxJUgucBbwJOAL4Ztfxg6meXfvk1IGI2BXYJDOv6mr338BTqGaFvqur7TbAs4GbgWsGBRG5sH/pLujOSZI0T2rfe/15EWP5nX525sC+RcTpwKFUOx2cx107HXwL2H9qp4POsh07Z9c1I2Jn4DLg3lTJ3beA+1AlfLsAr83MclAMVtg0L4bZKm2B/+NBkjS5jgBWAEuBZ1IteHs6cGy/bakAMvP6iNgLOBb4S6qdEe4ALgden5lnDxOAFTbNCxM2SZpotVfYXjCmCtvnh6iwNYGTDiRJkhrOIVFJktR4E1EGGyMTNkmS1HhtHxJse/8lSZIazwqbJElqvLYPiVphkyRJajgrbJIkqfHaXmEyYdO8cI01SdKGcEhUkiRJjWaFbZKtX9f/fM2bw9/NnasGt9l0CwDuNcSuCHdYsZOkVml7hant/ZckSWq8BpVgICLuBzwO2BFYAqwGbgC+m5m/rjM2SZJUn7Y/w9aIhC0idgNOBg6guifd9yWBjIivUe1q/4MaQpQkSTVq+5Bg7QlbRDwa+CZVYvYR4BKqqtoaYDFVtW0f4AXAxRHxvzLz+/VEK0mSNP+akLC+E7gReHhmviozP5SZX8rMCzrvH8rMVwIPB34NvKPfxSJiaUQsj4jly5Ytm4fwJUnSuMWYXpOi9gobsC9wbGau7NcoM1dGxPuAEwa0WwZMZWpOJZQkSROvCQlbMHxilUxWQixJkuZAE4YE69SE/n8HOCwitu/XqHP+MOC/5iUqSZKkhmhChe3NwIXAVRHxWe6adLAW2Iy7Jh0c2Pn67+oJs4GatDDuIJ1FcYfhoriSpOnaPrwWTdjjMSL2Bk4D9ugc6g5q6h4tBw7PzG+PcOn6OydJ0uSrPV86OGIsv9PPzKy9b8NoRIkmMy8B9oqIXYE9gR24a+HclcClmXlNjSGqoXYYYhurlQ34R4nUKCsuHNxmlyePOwpJI2hEwjalk5SZmEmSpLtpwkP3dWp7/yVJkhpvohK2iDgoIr5edxySJGl+LRrTa1I0akh0CDsDT6o7CEmSNL8mYmbAGE1ScilJktRKtVfYIuLaEZpvPbZAJElSY7W9wlR7wgbsAtxMtXzHIEvGG4okSVLzNCFhuw74aWY+dVDDiPgnBmz+LkmSFp62P8PWhITtu8B+Q7Z1BVTdjYviSrPgoriaQG0fEm1C/78H3Dcidhmi7fXAN8YajSRJUsM0Yi/RMVrQnZso69YMbrPx4rGGsNkQ21itHeefh/XrBrdZ1ISityTdQ+0jkkeMaS/RUydkL9EmVNgkSZLUh/+clyRJjdf2ClPb+y9JktR4VtgkSVLjTcSDZmNkwiZJkhqv7UOCbe+/JElS41lhkyRJjdf2IVErbJIkSQ1nhU3zY8yL4g5jrIviDsNFcSVp1tpeYfI3iCRJary2D4masEk9LBqwjdX6uqt1kqRWMWGTJEmN1/Yh0bb3X5IkqfGssEmSpMbzGTZJkqSGa3vC5pCoJElSw1lhkyRJjdf2ClPb+y9JktR4VtgkSVLjtb3CZMIm9eDCuJKkJjFhkyRJjdf2WaImbNIGiAFbWAGk1TpJ2mBtHxJte/8lSZIazwqbJElqvLYPiVphkyRJajgrbJIkqfHaXmEzYZMkSY3X9iHBtvdfkiSp8aywSZKkxmt7hant/ZckSWo8K2zSBnBRXEmaH046kCRJari2DwmasE2yNb/rf37xNvMTxzDWrRncZuPF1ftPvzy47YOetmHxDDIo3qlYR/DIIbaxutKKnTTZ1q8b3GaRv3o1ukb/1ETEg4BtgRsz87q645EkSfVo+5Bo7RXGiHheRDxg2rH9I+LHwI+BbwE/jYgrI2KfWoKUJEmqUe0JG/A54IlTX0TEXsCXgB2BjwInAJ8GHgz8R0Q8pI4gJUlSfRaN6TUpmhDr9CrnScAqYPfMfEVmnpiZBwH7UA3hHtP3YhFLI2J5RCxftmzZeCKWJEnzKsb0mhSNeoYtIoKq2va2zPxp97nM/F5EfBh4Rr9rZOYyYCpT8wluSZI08ZpQYeu2BbAp8P0Zzl8O/Mn8hSNJkprAIdFmSIDMvA24Ddh8hnabA3fMV1CSJElN0JQh0U9ExCe6vn4c1USD6R4MrJyfkCZAk9ZZG2SUdcvGvcbaMGaxztogrrEmtYBrrI3NJD1vNg5N+Mn6aI9jd04/EBGbAc8Bvjr2iCRJkjoiYhFwOHAIsAvwG+CzwLGZefuQ17gP8CaqXOYBVCOKP+hc45uDPl97wpaZLx+y6UbA04FfjTEcqRFiiF0R3MdUUpvUXGF7D/A64BzgZODhna8fExEHZOb6fh+OiJ2BC6me1f8X4Gpga+BRVMuYDTRjwlYUxRXDXKCHLMvy0bP87MwXzVzNzJMRJEnSAlbXQ/cRsRtwGHB2Zj6/6/h1wGnAi4BPDbjMJ6hyrkdl5i9nE0e/CtsOuCyGJElqtxdTFfhOnXb8TOCdwEH0Sdgi4onAE4DXZeYvI2ITYJNOIWpoMyZsZVluO8qF5kNEHAS8IjP3rzsWSZI0f2pc1mJPYD3wne6DmbkmIi7vnO9nav3Yn0XEv1M93rVRRPwEODEzPzHzR+/SlGU9hrUz8KS6g5AkSa2xA3BTZq7tce4GYNuI2LTP5x/aeT8TuA/wUuCVVBMsPx4RQz3LP+tJB0VRbAJsUZblzbO9hiRJ0jDGNekgIpYCS7sOLevsmjRlCdArWQNY09XmHitcdGzZeb8N2C8z7+x833OAa4G3R8RHB01cGClhK4piMfBG4O+AB1I947Zx59yewBuAk8qyHHrCQkRcO0IIW4/QVpIkLRDjGhKctqVlL6uB+81wbnFXm5lMLfj/6alkrfN9b46Ic4GXUFXhftQvzqETtqIoNqeakvo44KfANcCuXU1+BDyTKlscZYbpLsDNDLcg7pIRritJkrShVgKPiIjNegyL7kg1XDpTdQ3gF533XsuSTc0YvfegIEZJWN9ElawdWpblQ5g2I6Isy1XARcABI1wT4DpgeWY+ctALeN+I15YkSQtAjOk1hEup8qW97hZPxGJgd2D5gM9PTVZ4QI9zU8d+PSiIURK2A4Gvl2VZdr7uteTHihkC6ue7wGOHbOsyI2qFzBz4kiTNi7Oo8o8jph0/mGrk75NTByJi14h42LR2X6B6fu2giNiiq+2fUO168JPM/OmgIEZ5hm0nqhV++7kVGHWDy+8BL4iIXTJzxYC21wPfGPH6kiRpwtW1rEVmXhkRZwCHRsTZwHnctdPBRdx9xPFrVCtaRNfnb46Io4APApdExL8CmwKv6bwfOkwcoyRstwPbDWjzQOC3I1yTzHwH8I4h236CarVgqRnWrxvcZsybQW88xDZW66zISZpwNW9NdQTVKOJSquf1bwJOp9oHtO/sTqgmNkTETcDRwFup1nX7NvC3mfmtYQIYJWH9LvD0oih6PvhfFMV2wNOAi0e4piRJUqNl5h8y8+TMfGhmbpaZO2bmP2TmqmntdsnMnrllZp6dmXtn5uaZuWVmPmXYZA1GS9jeB9wf+EJRFDt1n+h8/WmqTU1PH+GakiRJAy0a02tSxCgPLxdF8S7gKKqH724HNgd+DvwpVbXyrWVZHjeGOGfLcSCNl0Oiktqh5hFJ+FjEWP4ie8kMFbGmGSm5LMvyaOCvga9z14zY+1NNBHh2w5I1SZK0QNS4rEcjjFRhm64oik3Lsuy3WFzdLCtovKywSWqH2nObj4+pwvb3E1Jh26CEbQIs6M6pAUzYJLVD7UnNJ8eUsP3dhCRsI/8mKYpie+DFwGOo9va8hWottU+XZdlr2wVJkqQNMhFZ1RiNOungEOAUqs1Ou//fJdWO9f9QluUH5zTCDWNZQeNlhU1SO9SeL31qTBW2v52QCtvQCVtRFM8F/o1qdugHqDaC/xWwPbAfcAjVFg3PL8vyC+MIdhb8LSVJ0oarPan5zJgSthdNSMI2yj/9j6HaemrPsix/Mu3cF4uiOJNqg9NjqPbNkiRJ0hwYJWF7JPCJHskaAGVZ/rgois8CfzcnkUmac/cZYvj0t00ZPl23ZnCbjRePP45h3blqcJtNO/s+jzKUPqjtmIfcpaaYiDLYGI26l+hNA9rcBAzxt5YkSdLwJmlXgnEYpf9fA/5yQJu/BM6ffTiSJEmabpQK29HAtzvPqr25LMtfT50oiuJ+wNuBHYDnz22IkiSp7RwSnUFRFOf2OPwL4BXAQUVR/Bi4kWprqocCmwLLqTaJf/bchypJktRO/Sps/7vPuc2AR/U4vicupSFJkuaYFbaZbTlvUUiSJPXR9kkH7iUqtYjLeoyRy3poYau9wHXOmBbOfe4CXDhXkiSpFm2vsM0qYSuK4t5UM0I363W+LMvLNiQoSePRmOrZMJpUPRvGVPVsGKNUxaygSWLEhK0oiicAJwN7DGi60awjkiRJmmYixi3HaOiErSiKx1Atinsb8BHg5cC3geuAfYFdgPOAH811kJLmXwzxvNsCfwZWkhpjlCHhNwN/APYqy/KVnWNfKcvyIOAhwCnAXwDL5jZESZLUdovG9JoUo8T6BODcsiyv6zoWAGVZrgPeAFwPvHXuwpMkSaoSjnG8JsUoCdu9qYY/p/we2Hzqi7IsE7gI2G9uQpMkSRKMNungJmDrrq9/DTxwWptFdCVxkiRJc2GSqmHjMEqF7SfAn3V9fSnwV0VR7AxQFMV9gecB18xdeJIkSRolYfsy8OSiKKaqbKdTbV91eVEUF1DNDt2eavN3SZKkOeOkg+Eto9oQfmqiwQXAS4FbgCcBa4E3lGV55lwHKUmS2q3tCduc7CVaFMVGZVn+YQ7imWsuEiXNkuuwSepS+yNk/zGmvUSfMiF7ibr5uyRJGqT2pOarY0rY/mpCErZJqgZKkiS10ozLehRFccUsr5llWT56lp+VNIEcPpU0bm2vMPVbh20H5nlIMSL2APYEdgSWAKuBG4DlmXnpfMYiSZKaYyLGLceoEc+wRcT+wBlUe5L2uicJXA0clpnnj3Dp+jsntYAVNmnBqz1f+vqYnmHbf0KeYRtlp4OxiIj9qNZ4WwkcB1xCVVVbAyymqrbtA7wKOC8inpqZF9QUriRJqkHbh0Rrr7BFxDeotrN6Ymbe3qfdFsA3gVsz80l92i0FlgJ88IMffNzSpUvnOGJJ01lhkxa82qtQF46pwvbkCamwNSFhWwUclZkfGKLta4B3Z+YWQ17e3xDSPDBhkxa82pOaixaNJ2F70vrJSNiaUGG8E9hqyLZbddpLkiS1Ru3PsAHfAI6MiC9l5pUzNYqIRwJHAhfNW2SSJKkRFjWhxFSjJiRsRwPfBi6LiAu4a9LBWmAz7pp08GSqfUuPqSdMSZJUlzBhq1dmXh0RewLvBJ4FHNCj2VrgHOCNmXnNfMYnaTCfT5Ok8ao9YQPIzGuBF0bEEuBRVIv2Ti2cuxK4st8MUkmStLBZYRtRURQPAl4EPBzYvCzL53SOP4Aq2frPsixvnU0wmbmaakhU0gI1aEap1TpJuqeREraiKI4GTur6XPffrPcC/h04FHj/KNeNiM2AVwJ/DvwK+HRm/qRHuwOAN2Xm/qNcX5IkTba2TzoYuvtFUTyX6jmzi4EnACd3ny/L8ifA94BnjxJAZxj0EuB04NXA8cAPIuLoHs3vD8y4aK4kSVqYFi0az2tSjBLqkcAK4GllWV4MrOrR5ofAQ0eM4Qjg0cDbqYZUnwlcBrwjIsoRryVJkrTgjDIkujvw8bIs1/Rps5KqCjaKA4GzMvMtna9/EBFfAd4LvDYiNsnMg0e8piRJWkCcdDC8jRi8y8C2Q7SZblemPfOWmeuBwyLid8CbI2KjzHzFiNeVJElaEEZJ2K4B9p7pZFEUAewL/GjEGNYAm/Q6kZlviYg/AMdGNbXs6yNeW5IkLQCT9LzZOIzS/c8DexVF8eoZzh8BPAw4a8QY+iaCmXk8cALwUuDEEa8tSZIWgFg0ntekGKXCdjLwN8AZRVEcSKcqVhTF8cD/oto66nJg1IkCXwUOj4gtM/O2Xg0y84SISKoZpC7SJEmSWiVGWaSyKIptgQ8AzwWmr355DnBwWZa/HSmAiAcDrwA+l5mXDWj7WmCPzHz5kJc3uZMkacP1X/F6Hnxv2xjL7/TH3JS1920YIyVsU4qi2JFqGPO+VBuyX1KW5fVzHNtcMGGTJGnD1Z7UmLAt7G1gFnTnpIVs0BZW4DZW0jyqPam5/H7jSdh2//VkJGwT9LidJElSOw096aAoitOGbJplWR4+y3gkSZLuoe3LeowyS/TQAeeTqmSagAmbJEmaM5O0BMc4jJKwPXKG49sAewLHABcAJ21oUJIkSbrL0AlbWZY/7HP6W0VRnAt8H/h/VJvAS5IkzYm2D4nOWffLsrwW+L/A6+fqmpIkSRptSHQYvwSeN8fXlCRJLeczbHOks/n7E4FVc3VNSZIkcEh0lGU9HtvnGn8KvBLYA/joHMQlqeVcFFeS7jJKhW05/XcOiE6bN2xQRJIkSdM4JDq8U+idsK0Hbga+A1xQlqX/LJY0r9zGStJC516ikiaeCZs0drXvt/mTB41nL9EH/3Qy9hIddWuqH5Vl+f4xxiNJknQPbR8SHaX7hwA7jysQSZIk9TbKM2w/A+47rkAkSZJmYoVteGcBTy2KYstxBSNJkqR7GiVhOwm4GvhqURRPLopi8zHFJEmSdDeLFo3nNSlGGRL9NVWCtwT4GkBRFKu550zMLMty67kJT5IkSaMkbFfjMhmSGsglO6SFb5KqYePgOmySJGmQ2tcq+9mjxrMO205XDF6HLSIWAYdTrZixC/Ab4LPAsZl5+yjfLyKWAD/sXOeMzDx0mM/1rbAVRfES4PKyLK8YJRhJaqp7DbHI7h3j/ofsujWD22y8eLi2U+0kjdN7gNcB5wAnAw/vfP2YiDggM9ePcK0TgW1HDWDQkOhHgOMBEzZJklSbuoZEI2I34DDg7Mx8ftfx64DTgBcBnxryWo8FjgCOpkr8htbyEWFJkqS+Xkw1JHzqtONnAquBg4a5SERs1PnMl4GzRw1ilEkHkiRJtahx4dw9gfXAd7oPZuaaiLi8c34YRwIPA54/qGEvVtgkSVLjxaLxvIawA3BTZq7tce4GYNuI2LRv7BEPBE4ATszMFSN2HRiuwrZNURQ7jXLRsix/NptgJEmS5lNELAWWdh1alpnLur5eAvRK1gDWdLW5s8+3eT9wHXDKbOMcJmE7vPMaVg55XUmSpKGMa9JBJzlb1qfJauB+M5xb3NWmp4g4CHgK8MTM/P2sgmS4xOpW4Hez/QaSJEkTbCXwiIjYrMew6I5Uw6U9q2sRsRlVVe084FcR8aCuzwFs3Tl2U2b2zbWGSdjeU5bliUO0kyRJGosadzq4lKpCthfwzamDEbEY2B34Rp/P3gvYDnhm5zXdQZ3XG4B/7heEQ5eSWmXsi+IOY5TFbl0YVwJqnSV6FvAmqvXTvtl1/GCqZ9c+OXUgInYFNsnMqzqHbgcO7HHN7YCSaomPf2GI9W5N2CRJkmaQmVdGxBnAoRFxNtXw5tROBxdx90VzvwbsTGcrr84za5+ffs2I2KXzn9dk5j3O92LCJkkzGGkbqztXDb7gpltU7+vXDQ6ldTQAACAASURBVG67yL+epW41b/5+BLCCajbpM4GbgNOp9hIdZVuqWfNvBEmSpD4y8w9UW0n13U4qM3cZ8nor6FThhhXZhOc5eujsZr8v1QapNwIXz7BoXT/N7JykiWCFTfqjkZKLcfjtE2Msv9Pv842svW/DqH2ng4j4h4h46LRjLwF+DnyF6mG+84GfRcTzaghRkiSpVk34J9y7gV8BPwaIiKcBH6aqqp1ANWb8EOA1wGciYp/M/G49oUqSpDrU/Axb7ZrQ/emlyLdQLVK3W2aemJkfy8x/olrr5Hbg6L4Xi1gaEcsjYvmyZf0WLpYkSZOixr1EG6EJFbY/ioiNqBamOyYzb+4+l5k/j4h/AV7c7xrTtpjwGTZJkjTxGpWwUS1AtxGd4dEerqKahCBJklqk7UOiTUnY7hsRO1ENj94G3HuGdvcGhpiKJUmStHA0JWE7tfOCKmnbF/h4j3YPB34xX0FJareRtrGaWrJjGC7ZIY1skp43G4cm/K1xQo9j99ixPiK2Ap5Djy0eJEnSwmbCVrPM7JWw9XI78EDgjjGGI0l3GWGB20VDLLK7vqELlUtqvtoTtmF1toW4pe44JEnS/Gv7pINGdD8iNouIIiLKiDguIh48Q7sDIuLr8x2fJElSnWqvsHX2DP0W8CjuWkT3TRHxlsx817Tm9weeNJ/xSZKk+llhq98RwKOBt1Mlbc8ELgPeERFlnYFJkqRmcKeD+h0InJWZb+l8/YOI+ArwXuC1EbFJZh5cX3iSJEn1akLCtivw/u4DmbkeOCwifge8OSI2ysxX1BKdJEmqXduHRJuQsK0BNul1IjPfEhF/AI6NiACccCBJklqnCQnbNcDewBm9Tmbm8RGRwHHAk+cxLkmS1BCT9LzZODQhYfsqcHhEbJmZt/VqkJkndJK24wFXnpQ0P0bYQspFcSWNUxMSto9TDYk+mGp2aE+ZeWJE/A+wx3wFJkmSmqHtz7BFLux/FS7ozklqkDX32AL5nhZvA8BmQ2xjtXZh/92syTP4h3bcXhjj+UPx2ay/b0Noeb4qSZLUfE0YEpUkSeqv5SWmlndfkiSp+aywSZKk5mt5icmETZIkNV/LE7aWd1+SJKn5rLBJkqTma3mJqeXdlyRJaj4rbJI0FzqL4g7DRXGlWWh5icmETZIkNZ8JmySpqWLANlYLfHtBSR0mbJIkqflaXmFrefclSZKazwqbJElqvpaXmEzYJElS87U8YWt59yVJkprPCpskSWq+lpeYWt59SZKk5rPCJkmSmq/lJSYTNklqMBfGlQQmbJIkaRJYYZOkCbduzeA2Gy8efxw12njAFlYA66zWaZK1PGFrefclSZKazwqbJElqvpaXmFrefUmSpOazwiZJkpqv5SUmEzZJktR8LU/YWt59SZKk5rPCJkmSmq/lJaaWd1+SJKn5rLBJmnwLfFHcYbgorha8lpeYTNgkSVLzmbBpwVu/bnCbRf4otMK4fhau+sLgNg97zujXnSSrfjW4zRbbj37dm67qf37bh1XvI9zbvxxiG6uvTVXs/PtDaoRG/SmLiD2APYEdgSXAauAGYHlmXlpnbJIkqUZW2OoXEfsDZwAPAXr90y8j4mrgsMw8f16DkyRJqlntCVtE7Ad8GVgJHAdcQlVVWwMspqq27QO8CjgvIp6amRfUFK4kSapDyytsTej+CcCVwG6ZeVJmnp+ZP8rM6zrv52fmW4HdgB8Cx/e7WEQsjYjlEbF82bJl449ekiRpzGqvsAGPBY7KzNv7NcrMVRGxDHj3gHbLgKlMzXnukiQtBIs2qjuCWjUhYbsT2GrItlt12kuSpDZp+WzkJgyJfgM4MiIe2a9R5/yRwEXzEpUkSVJDNCFdPRr4NnBZRFzAXZMO1gKbcdekgycDtwDH1BOmJEmqTcsrbJEN2M4kIv4MeCfwLKokbbq1wL8Db8zMa0a4dP2dkyRp8g1ebXnc3r7FeH6nv2lV/X0bQiPS1cy8FnhhRCwBHgXswF0L564Erhw0KUGSJC1gLa+wNar3mbmaakhU0iQZ17ZMqt2SIbaxWt2AkRq1QMsTtiZMOiAito2IoyLibRHxF13H/zEiro+I2yLiPyLiUXXGKUmSVIfa09WI2B5YTjUMCnBMRBwEbEe1qO73gJuB/YELIuLRmfmLWoKVJEn1sMJWuzcAWwMvBPYGvg+cBLwU2DMz98nM3YG/ArYAjqorUEmSpDo0IV19BvCvmfl5gIg4hmpv0RMy88qpRpl5QUR8BnhKPWFKkqTatLzC1oTe70S1l+iUH3bev9ej7XeBF4w9IkmS1CwtT9iaMCT6e+6eOK7pvK/q0XYNTVgLRpIkaR41IV1dCTyg6+tVwGHAVT3a7gz8Zj6CkiRJDdLyClsTen8Z1WQDADJzLXDGDG33B66Yj6AkjcA11hYs11iTmqEJCduJ3L3C1lNE3B/4JfCpsUckSZKapcYKW0QsAg4HDgF2oRrt+yxw7KCdmCLiIcBBVJMmdwUWA9cAnwNOHXYnp0bsJTpGC7pzktQkMcSuCAv8d85CVv/z4+972Hh+eA69amDfIuK9wOuAc4AvAQ+nenzrm8ABmbm+z2ffCbwWOJdqN6ffA/tRLWd2BbB3Zt4xKIYmVNgkSZL6q6nCFhG7USVnZ2fm87uOXwecBryI/qN/nwfekZm3dB37QET8BHgz8ErgfYPiaMIsUUmSpP4WbTye12Avpqownjrt+JnAaqrhzhll5vJpydqUszrvfz5MECZskiRJM9sTWA98p/tgZq4BLu+cn42p5/dvHKaxQ6KSJKn56pt0sANwU2cVi+luAPaNiE0z885hLxgRGwHHAusYcjKlFTZJktRaEbE0IpZ3vZZOa7IE6JWswV2L/S8Z8dueSrWk2bGZ+eNhPmCFTZIkNd+YKmyZuQxY1qfJauB+M5xb3NVmKBHxVuBQYFlmvmPYz5mwSZKk5qtvSHQl8IiI2KzHsOiOVMOlQw2HRsTxwD8BHwZePUoQDolKkiTN7FKqfGmv7oMRsRjYHVg+zEUi4jjgOOBjwKtyxEUJTdgkSXMiMwe+pFmrb1mPs6gW4j9i2vGDqZ5d++TUgYjYNSIeNv0CEXEscDzwceDl/RbanYlDopIkSTPIzCsj4gzg0Ig4GziPaqeD1wEXcfdZnl8DdqZrZ4iIeC1wAvAz4Hzgb6ftCnJjZn51UBwmbJKkufG7FYPbbLMLAFsOsY3VbVbk1K3GvUSpqmsrgKXAM4GbgNOpZnkOqpZNrdO2E/DRHucvAgYmbO4lKkmaGyZsC1n9e4l+4mnj+YE46Mv1920IPsMmSZLUcA6JSpKk5qt3SLR2VtgkSZIart3pqiRJmgxW2CRJktRk7U5XJUnSZGh5ha3dvZckSZPBhE2SpDnQWWNtGK6xJo3GhE2SJDWfFTZJkporhtgVYYHv2iOZsEmSpAlghU2SJKnhWp6wuQ6bJElSw7U7XZUkSZPBCpskSZKarN3pqiRJmgwtr7C1u/eSJGkytDxhc0hUkiSp4dqdrkqSGs9FcQVYYas7AEmSJPXX7nRVkrSguI3VAmaFTZIkSU3W2HQ1IpYA+wLbAjcCF2fm2nqjkiRJtWh5ha323kfEPwBfzMwfdx17CfAeYJuupjdFxGsy8+z5jlGSJNWs5QlbE4ZE3w08buqLiHga8GFgLXAC8HLgHVTJ5Wci4nG9LtL1+aURsTwili9btmx8UUuSJM2TJqSr058QfQuwEnhUZt78x0YRHwSuAI4G/mami2XmMmAqU/PJUkmSFgIrbM0RERsBewGndidrAJn5c+BfgCfUEZskSVJdmpauLgE2An48w/mrqCYhSJKkNml5ha0pvb9vROxENTx6G3DvGdrdG1g1b1FJkqRmaHnC1pQh0VOB64BrgS2plvPo5eHAL+YrKEnSZMnMgS9pEjUhXT2hx7HfTT8QEVsBzwE+P/aIJElSs7S8wlZ77zOzV8LWy+3AA4E7xhiOJKklBm1jZTVOTVJ7wjaszPwDcEvdcUiSpBpYYatfRGwLvIxqUsF5mfmtzvF/BArgPsC3gaMy84q64pQkSTUxYatXRGwPLAd26Bw6JiIOArajer7te8DNwP7ABRHx6Mx04oEkSWqN2hM24A3A1sALgZ8BHwBOopp4sGdmXgkQEfsBXwaOAo6oJ1RJklSLllfYmrCsxzOAf83Mz2fmd4BjqCYXnDuVrAFk5gXAZ4Cn1BOmJElSPZqQru4EXNn19Q8779/r0fa7wAvGHpEkSWoWK2y1+z13TxzXdN577WiwhntuFi9JkrSgNSFdXQk8oOvrVcBhVPuGTrcz8Jv5CEqSJDVIyytsTej9ZcDeU19k5lrgjBna7g+4rIckaYO5MO6EMWGr3YncvcLWU0TcH/gl8KmxRyRJktQgtSdsmXk1cPUQ7W4Enjf+iCRJusugLazAat28aHmFrQmTDiRJktRHu9NVSZI0GVpeYWt37yVJ0mRoecLmkKgkSVLDtTtdlSRJk8EKmyRJkpqs3emqJEmaDC2vsLW795IkaTKYsEmSpJm4KK6awIRNkiQ1nxU2SZI0FzYeYhurdbOp2K1fN7hNyxOahc67K0mSmq/lCanLekiSJDVcu9NVSZI0GVpeYWt37yVJ0mRoecLmkKgkSVLDtTtdlSRJk8EKmyRJkpqs3emqJElzaFZrrA2j5dUloPX/D9rde0mSNBlM2CRJmmerbxrcZsm244+jRvcaYleEO9zHVB0mbJIkqflaXmFz0oEkSVLDtTtdlSRJk6HlFbZ2916SJE2GlidsDolKkiQ1XLvTVUmSNBmssEmSJKnJ2p2uSpKkydDyClu7ey9JqscCXxR3GC6Kq1E4JCpJkppv0cbjeQ0hIhZFxJERcVVErImIn0fEyRGx+Xx8HiByYWf4C7pzYzHKdjHr1gxuu/HiDYtHk2Gh/iws1H6pMuj+Nuje7jDENlYrx/v7fHAA47ZuzXg6uPHigX2LiPcCrwPOAb4EPBw4DPgmcEBmrh/n56FBQ6IRsQewJ7AjsARYDdwALM/MS+uMTZIktVNE7EaVXJ2dmc/vOn4dcBrwIuBT4/r8lNoTtojYHzgDeAi9M/iMiKuBwzLz/HkNTpIkNUN9kw5eTJWfnDrt+JnAO4GD6J9wbejngZoTtojYD/gysBI4DriEqqq2BlhMVW3bB3gVcF5EPDUzL6gpXEmS1D57AuuB73QfzMw1EXF55/w4Pw/UX2E7AbgSeGJm3t7j/I+A8yPiPVTjvMcDJmySJLVNfRW2HYCbMnNtj3M3APtGxKaZeeeYPg/Un7A9FjhqhmTtjzJzVUQsA9496IIRsRRY2vnykMxc1qtNr+OTbk76NcpU+3l6IHeh3i9YQH2b9rNgvybPQu1b3341aFLBIL0mFCzUe9bHWCY+TMsbAJZN+/+6BOiVbEE1IjjVZqaEa0M/D9S/rMedwFZDtt2KAZ0ByMxlmblH5zXTD/LSGY5POvs1eRZq3+zX5FmofVuo/YKF3bd5My1v6JU7rAY2m+Hji7vazGRDPw/Un7B9AzgyIh7Zr1Hn/JHARfMSlSRJUmUlsG1E9Eq6dqQa7uxXUNrQzwP1D4keDXwbuCwiLuCuSQdrqbLRqUkHTwZuAY6pJ0xJktRSlwJPAfaiep4egIhYDOxOVXwa5+eBmhO2zLw6Ivakmtb6LOCAHs3WUi0098bMvGaOvvVCHfO3X5NnofbNfk2ehdq3hdovWNh9a5KzgDcBR9CVcAEHUz179smpAxGxK7BJZl41m8/305idDiJiCfAoqtkUUwvnrgSuHDQpQZIkaVwi4nTgUKoC0nlUOxW8DvgWsP/UTgURsQLYOTNjNp/vG0NTErZeImJr4JXAuZn507rjkSRJ7RMRG1FVyJYCuwA3UVXOjs3MVV3tVtA7YRvq831jaHjCtitwNXBgZp5ddzySJEl1qHWWaESc1u8FHEu17sorO8feO8vvsygijoyIqyJiTUT8PCJOjojN57RDNYiInOE1VMZet4h4Y0R8LiKu7cS9YkD7x0fE+RFxW0TcGhFfjojd5yncoY3Sr4j4SJ/7+IJ5DHugiHhIRJwYEZdExG869+HyiHhzrz9PEfHQiPhCRNwcEbdHxDc729E1yij9iojj+9yvo+rqw0w69+CTEfGjiLglIlZ3/i48JSL+ZIb2k3DPhu7XpN2z6SJiSURc14n3fT3OT8Q904ape5booUDSfzG8BJ7e9d+Hz+L7vIdqrPgc4GTuGjt+TEQcMMzYccN9k3s+fPr7OgKZhbcDvwUuA7bp1zAi9gYupJpJfGzn8KHANyNi38y8coxxjmrofnX5+x7HvtPjWJ1eAbwWOJfqQdnfA/sBJwEvjIi9M/MO+GOF/GJgHfAuqpneBwNfiYinN2xv4KH71eVIqmGNbt8dd6Cz8ADgT6j+/vsF1f14JNXQzIsiYvfM/DVM3D0bul9dJuWeTXci0HNV8wm7Z9oQmVnbC7iO6ofrCGDnHq8nUe2/dcjUsVl8j9061/i3accPo0oA/7bO/wdz8P8wgY/UHccGxP9nXf/9A2BFn7bfAW4Fduw6tmPn2H/U3ZcN6NdHqj+K9cc9RL/2ALbucfykzs/ioV3HPgv8Adi969gWwPXAj+k8ktGE14j9Or5zbJe6497APh/Y6cfRk3jPRuzXxN4zqh2B1gH/0OnD+6adn/h75mu4V90L5z6Carf6dwOnAesz8/qpF9W/mgB+03VsVC+mquCdOu34mVQzUQ+aXejNEhGbRsQWdccxqsy8dph2EfEgqg1yP5eZN3R9/gbgc8ABEbH9eKIc3bD96haVrSKi7j+XM8rM5Zl5S49TZ3Xe/xygM4z418CFmXl51+dXAR8CHsKQGx7Ph2H7NV3nftU9UjFbU3+f3hsm7571cbd+TTdJ9yyqB9XPBL4M3OM57gV0zzSEWn8xZOYdmXkU8Hiq8vZ/R8RRnR/SubInVYXtbkNLmbkGuJyF8cP8Aqrk87aI+HVEnB7VDNuFZOo+fbvHuUuokvLHzV84Y3FL53VHRHw1Ih5fd0AjeEDn/cbO+6OoFr+e6X7BZPzZm96vbldQ3a81EXFxRDy9R5vGiIjFEbFtRDwgIp4CfLBz6rzO+0TesyH61W2i7hnVEO7DqB796GUi75lmpxH/ks/My6h+qE6gKl1fFhH7zNHld6Da9qHXxqs3UG0Xsekcfa86fIfq/9kLgJcCX+eu57omruLWxw6d9xt6nJs6tuM8xTLXfkX1nOVrgOdSPf+2B9U97LWYdKN0/oF1LNWwzac6hyf+fs3QL4DfUT0zehjwbOCNVI9sfDEiXjbPYY7iVcBvgJ8DX6F6tvKgzJxayHNS79mgfsEE3rOIeCDV78QTM3PFDM0m9Z5pFhpTFs7qwf9/joh/A95P9SD9eVRj9htiCdVuCb2s6WozcB+vJsrM6VWYj0XEFcDbqCZovG3+oxqLJZ33XvdyzbQ2EyUzp2+59oWI+BRVBfj9wIPnP6qRnArsDbwpM3/cObYQ7levfpGZ0x+vICL+lepZxfdExOdzyHWV5tkXgKuonm96DNVQ2nZd5yf1ng3q16Tes/dTPed9Sp82k3rPNAuNqLB1y8zrMvNpwMuohkr7zSAdxmqqknEvi7vaLCTvpkpAn1l3IHNo6h71upcL7j5m5k+oHiZ+UEQ8pO54ZhIRb6Wq6C7LzHd0nZro+9WnXz1l5v8AH6Cq7uw75vBmJTN/kZnnZ+YXMvM4qor8/4mIN3aaTOQ9G6JfM32usfcsIg6i2nvy1ZnZb8b/RN4zzU7jErYpmfkJ4E+pHhw9dwMutZJq2LPXD/SOVMOlE1ldm0nnD/hKZpgGPqFWdt57lfenjvUaFphkKzrvjbyPEXE88E/Ah4FXTzs9sfdrQL/6WdF5b+T9mi4zrwC+BxSdQxN7z7r16Fc/Kzrvjblnnd9Vp1CNMP0qIh7UmXS1c6fJ1p1j27BA7pmG09iEDSAz78zMWzJz3QZc5lKqfu7VfTAiFgO7A8s34NqN1OnbA+j9oPSkurTz3uvZxr2phs4nYT2lUUwNhTbuPkbEccBxwMeAV2Xm9EcXrqQappnpfkED/+wN0a9+Gnu/+rgXcJ/Of0/kPZtBd7/6aeI9uxfVkO4zgZ90vS7snD+o8/WrWFj3TIPUva7IuF9UCyn2W4ftoLpj3IC+3XeG4+9m2jpEk/Bi8Hpll1KtubZD17EdOsfOrzv+2fQL2BxY3OP4Y6j+Iv7vuuPvEduxnZ+vjwGL+rT7HNX6UI/uOja1PtTVNGx9qGH6RfXcb6/12v4U+B+qRVnvVXdfpsW2/QzH9+vcn69N4j0btl+Tds+ATagmkU1/vabz8/mlztcPmbR75mvDXo3eS3SuRMTpVM+jnENVZp7a6eBbwP45oTsdRMR7qP4VdQHwM6o/pM+g+gvrv4D98p6rszdKRPw9d5X6DwM2pdqNAuD6zPx4V9t9qfr6C+D0rs/cH/iLzPz+vAQ9hGH7FdW2Wl+ienD6J8DtwKOpVt5fDzwlM/9zHkPvKyJeC7yP6uftLVQxdrsxM7/aafsgqlnMv6eaBXsr1QrsjwSemZlfma+4Bxm2X51hqOuo7tePgJuBh1JVO7YAXpyZn5u3wIcQEedQ7Qjwdapf4ouplsB5EdXzTU/OzhpeE3bPhurXJN6zXiJiF6p+nJGZh3Ydn5h7pg1Ud8Y4Hy9gI+D1VKs+r6Ua0z8F2KLu2DawX8+mmsZ+A9WMoNupZha+iR5Vmya+qMr8OcPrwh7t9wG+BqwCbuv0/7F192O2/QK2Bz5ONcvtVqq/dH8GfBR4WN396NGvj/Tp1z3uGdU/jv4v1bIKq4H/BA6oux+z7RfVw90fohqKurlzv34JfB7Yq+5+zNC3FwJfpFr2Yg1wR+fn7XRgpx7tJ+WeDdWvSbxnM/R3F3rsdDBJ98zXhr1aUWGTJEmaZI2edCBJkiQTNkmSpMYzYZMkSWo4EzZJkqSGM2GTJElqOBM2SZKkhjNhkyRJariN6w5AUj2KotiFauX0j5Zl+bKu4x8BXgo8sCzLFXXENopR4y2K4kLgSWVZxgZ+3xUAZVnusiHXGfA9LmQOYpU0+UzYpDEqimL6ytTrqVZbvwL4l7IsPzn/UY3XTImgJGn2HBKV5scJndc7qbateiLwiaIoTqkzqBm8kWqrmxvqDkSSVLHCJs2DsiyP7/66KIq/BL4KHFEUxWlNGnosy/KXVHstSpIawoRNqkFZll8riuIqqkrWnsCK7qFE4O3AW4H9gG2B/cuyvBCgKIr7AG8AnkO1IfSdwHLg/5Rl+R/Tv1dRFFtSVfde2LnWCmAZ8IVesfV7Jqwoir2A1wNP6Fzrt1Sban+oLMvPFkVxPHBcp/lLi6J4adfHX16W5Ue6rvVU4HBgL2BL4BfA2cDbyrL8XY+4Duhc+7HAWuAbwDG9+jCqoig2BZYCzwB2A7YHbgcuA04uy/JLfT67NfA24LnAfYFrgQ8Ap5dleY/NmouieDzV/XsCcB/gRuA84ISyLFfORX8kLTwOiUr1mXqQfPov9V2B/6JKxj5JlVzdClAUxc7Ad6kSld9QJQZnUSV+Xy6K4uDuCxVFsRnwNeBI4CbgvcBFwFuA94wSbOfaF1MlihcDJwNfBO4HFJ1mF3a+B8D3uWso+ATg8q5rHQt8GXh85xqnAT8FjgK+VRTFVtO+9wuArwB7AJ8DPkiVHH0beOAo/ZjBfTpxb0lV+TwFOBd4DHBeURSvmuFzmwLnA08FPgOcCWzTudb7pjcuiuLlwLeApwMXAKdSJduvApYXRbHTHPRF0gJkhU2qQada9FCqZO3SaaefALyjLMs39fjoR4GdgReXZfmZruttQ5UsnVYUxbllWd7YOfV6qgre2cCBZVmu77R/J1XiN2y8jwBKqsTxf5Vl+cNp5x8AUJblhZ3Zk4cDl08fCu603Y8qgfs28IzualpRFC8DPtw5f2Tn2BZUCdr6zvde3tX+PcARw/ajj5uBncuy/MW0WLemSrDeVRTFJ8uyvGPa5/6EqqL252VZru185jiqe1oURXFWWZbf6Bx/SKcfK6hmfv7xGcGiKPanShTfS1Wpk6S7scImzYOiKI7vvN5WFMXnqapLAZxaluX105rfSJWwTL/Go4EnAf/WnawBdJKe44DFwPO7Tr2cKtE5eipZ67S/jqqqNazXUP0D763Tk7XO9X5xz4/M6HWd94OnD312hkwvB/6u6/CzqSpgn+pO1jqOB24Z4Xv3VJbl2l59KMvyFuBfgXtTJb69vHEqWet85rdUw9lQ/f+f8hpgE+Dw7mSt85mvU1X0ntUZwpaku7HCJs2Pqee6Evgd8E2qZT0+0aPt97sTgC77dN637jwrNt12nfeHwx+fXXsQ8POyLK/p0f7CrrgG2bvzPuOzXCPYB/g9cGBRFAf2OL8psF1RFPcty/J/qJ5Zg2oo927KsrylKIrLqRLZDVIUxW5Uz5Y9kapytnhakx17fGwd1fDwdBd23h/TdWzq/j2p+P/tnXeYHmW5h2+IBDGRQIi0BKVILydIPEKKhiaJh8AlwgFEQjsCPhBKQEEEpCtqCIg+gJSAFKkxdIkSQpGiNDkkBJDDIiVBgSAQgVBy/vi9k52dne/75ttdwq557uvaa5KZd94p35TfPO01KxN/KwK9gHVowvoZBMHiQQi2IFgENFn4dE6N+Suk6bbprxZ907Rfmr5co12t7ZSxXJp2RamPFdCzp5FY7Au8StceRylmtjkwLe3X7cja9QayTg5GVr6lS1Z9xd0/qLNP/XLzst/vuw12p2+D5UEQLIaEYAuC7ke7zMJE5vo71N2ruDOz9ivVWL5yE/uUuS4HArOaWK+MfwJLunv/JtpD1xxHLY4FlgG2zLJxM8zs+0iwlTHAzHqViLZsn/Lu2uzf/dz9jU7ubxAEixkRwxYEPYf703RElcbu/ibKvBxoZmuVNBnZgW2PrtA2Ey+9YMNMqQAAE+1JREFU6vS1fHJBVuHhNG3n9kxJAYMr9lOPzwOvFcVare3m+AQwtGT+yDR9JDevqd8vCIIgTwi2IOghpID7u4GdzGzfsjZmtrGZrZibNQnd56eb2ZK5dmvQGvxfhXNQvNZxKWO0uN1Buf/ORVbCWiUqsnIi55vZqiV99UkuyozrU5/fNLMhheYn0Nbt2FFagP5mtklhX/ZDJTvq8aNUPiVbpz+y2IHOf8YvUOzexJQx2gYz621mIeaCICglXKJB0LP4Joq1utDMDkH12l4HBgGbABuh4Pa/p/YTUN20bwAPm9ltSODsigrP7lBlo+4+08wM1X17xMyuB55GcVlDgDdRkV/c/S0zewAYYWaXA08hq9sN7v5YKhp8NPAj4GkzuwUVDO6LSpZ8BbgHGJXrb39Ub+5uM7sKjcQwPB3vXShRoDOciYTZPWZ2NXJfDknbuBbYucZ6s1Fs2+NmdgPKAt0ZJS14VtIjHcesJLQvAmaY2e/SuVkKidsRqLbeep08liAI/g0JC1sQ9CBS6YnNgB8gEbQHspQNBf4GHIBGHsjavwtsg6xan0H10UYCp5DqnDWx7fORgLkp9fFdJPheAX5ZaL4nKog7CiUXnExrtifufjoSWTcDw1AttV1QjNyvaLVQZe2vTX09hEZsOBCNsrAFEnudwt1/B4wBZiIxux8aTWHLtI+1mI/O71RgN3T+/4nO88El27kM/X6XI4F9MPAt5JK9ltYCxEEQBG1YYsGCWvHNQRAEQRAEQXcgLGxBEARBEATdnBBsQRAEQRAE3ZwQbEEQBEEQBN2cEGxBEARBEATdnBBsQRAEQRAE3ZwQbEEQBEEQBN2cEGxBECy2mNl0M1sktY3MrMXMWhbFtoIg+PcjRjoIgkWEme0FHARsgIrePgL8zN1varKfFVHh3O3RCAdvAvcBp7r7/SXtqwiSse5+aW6d6dQfQ3MZd3+nsJ1eqHjsgcDawLLAC8Af0XHOqLAfQQ/GzLYHjgQ2RWPJzkAjPlzSgb6GogLKmwOfROPiXgSc7e4fFNoORiN6bAusiUbg+AcaBeOn7v4wDTCzAcDjwErAH919eGH5CsDXgf8CNkZFnuejQtWTgEnu/mGzxxkEVQkLWxAsAszsZ8DFaMii84HL0EP/RjNrVxG/Tj+fQ4OhH4JGGPgFqsQ/Eg2r9PWS1U6s8TchLX8f+H2NTdZa9/2Stlek41odmAycjV6ye6FhsbaqepyLkLHA+h/3Tvw7kK7jG9FwYZeh63xV4OJ0/TfT1460Djn2WzSSRm80YseVJauci0bUWBpdexOR+NoNeKDGfVHkPKBPneW7oGP6EhoS7kzgOnS8FwBXm9kSFbYTBB0iRjoIgo+YZCn4I/AM8EV3n5vmr46GWuoDrOfuLRX6mgLsCPwcOMzdF6T5n099vQ+s7e6vVejrAPSi+62771RYNh34irtXegGZ2ReBPyGLyn+6+79yy/ZBlpE73L07irZFQuYOdffVP9496XrStTwLmAdsll3LZrY88GdgLWCou99Xoa9lkdDvBwxz9wfT/E+icXS3AHZ39ytz64wDbnX3vxb62gOJx1eBVd19fo1tjgUuQUODOeUWtq3QvXpz3pJmZiuja381YGd3v67RMQZBRwiXaA/FzPZGYx9uiqw27yHT/DlpvMKydfoDR6AX/pppnRbgVuBkd5/XbNt6LyEzOwF99W7p7tNz8xcAd6Kv31OA0cDKwH7ufrGZrQPsi8Zo/Bxyrc0BbgNOSuNplh3fV4Fx6Au4HxoA/WHkQvmDmY1K+z/J3fctWX9p4MX034FpHM6u4MA0PTUTawDu3mJmvwSOA/ZB56om6YX1NeBD4NhMrKW+/mpm56PfbA9k3WrE/ml6XtUDqcOaaXp7Xqwlrk/TzxRXMrP1QAOjV9lIuu4nofP1InA8ugfeRmOcHu7ur5vZpmj80mFocPVpwCFFUVwmTJOVZCwaF3Rt4NPIvTYTuMjdryr0MQj4HrqOVwP+hQTHje5+coPj6Yd+h9HAOsCKaCzS+4DTari4R6TtbYrO6VzSvenuJ+barYTGex2DXOfvAS+nvk9y9/+rt29Nsi+ybp2eP8fuPtfMTgMuRPdBQ8EG7IyO69eZWEt9vWNmxwK3A98hZ2lz99Lr3d0vN7Mfot9xY/RR0wYz+yz6ALoQPR9KcfdpNebPMbNzgVORpTsEW/CREC7Rnss5yPV0FzLNX4nEzaVm1u4lYWZrIPFyDPBOWv8iFGN0OLmXaTNtO0F/4H4UnzIZufZeTst2Qg/354HfIPExE/gf4M9mNrDk+E5Egm5kmk5AD/b10eDapPnPALumF2WRb6DYl4u7UKwBZFal35Usu7XQph79kfh4xd3fLFmevYC3btSRmX0BDcbeQm13KGa2q5kdbWbjzWx0ErVlZPFpW5nZMoVl26fpH0rWeyL9NcsOyBX8D2QlfBrYG5hiZpsD96AP0guRdXMMcLOZVXnmnYrc1ysDVwNnpH0fiNxiCzGzIcBf0IfCS8BZaGD3N4ETKmxr/bS9D9PxnIF+j62Au9NHRn57o4DpwHB0fU8ApqCB6i3X7lPpuI8AnkP38IXoo25HFEfZlXTVNd6or7uQIB5a51os8l6atnPjJ3F+MRLJ4yv219Q2gqCrCAtbz2Ujd38mP8PMeqOH49Fmdq67v5hbfBkSdMe4+48K6w0A3upg246yMXApsK+7Fx9ylwITi6IpWdBuRYHI3ynMPx54FhhROO7MAoK7L0hfwj8F9kQiMU9mcfpVbt3lgMOaPLYp7v5oWr8PetG/5e6zS9o+nabrVOh3LkpWGGBmfd29+DtkVq71KvR1QJqe3yBQuhgv9HczO8jdr83PdPfHzWwiEvSzzOwmJFo2BEalfo6tsF9V2QHY2t3vBEhC7DZklb0F2N/dL88am9mFyAo0hlaLXy0OQNa7jYrWwnT9Z//uDVyDhPQe7n5Foe1qFY7jCeSqe6Ww7iDkZptIW+HybfShPdLd/1Jr35BoXws4090PL7Trjaxh+XknVNjXPNPzVnNg3TR9qtjQ3Web2TxgkJl9qsQCW6ReX++b2bPoulqTBmLfzL6ExOmLKKatyGHoI++r7v5G8iw0hZl9AllkoVxkBkGXEIKth1IUa2ne/ORi2wo9sH8NYGabAUOBR4HTS9Zb+LJopm0nmQ8cWSLWKAqu3PypZjYD2K6waFyaHlG2bsGFOgm5yg4gJ9jMbF2UFXmHu+dfFMvRwFVZQgs6fyDXLOgLvoxs/nKNOnX3t81sGsqEO4mcRcDM1kQWSIDl6/VjZn2B3ZE14KIaza4HfoYyWV9FAn4vZLG5ysy2d/c27iN3H29mTyKRYblFDwGX5F3uuXU6GqT9m0yspX4+NLNLkWB7PC/WEr9Ggm0wjQUbyGLyQXFm4fofg6zcNxTFWmr7fKONuHvpdeHuL5jZtcA4M/usu/+t0OTtBvtWr918dP/lafYaB1n6Mqpc531Su0aCrUvumRQ/l2U+jy/JLN0AOA04193LrL9V+TFKPLjF3W/rRD9BUJcQbD2UFHdxFBJmnwWKbqi823DzNL2tQtp5M207Q4u7/71sQXJT7IFcXP+BBEivXJPiy2ZzYAEVvm7d/VUzuxoYa2ZD3f3etCizrp1baN8CLIrMr6rZP4chd9/hZrYFcC9y4+6ELIybUCI0CuyO4rImu/ucsgbuPrEw60ngGDN7CbmoTyMX75N+s7OQUDsWWWlfRwJpInCrmR3s7r+seJyNeLBk3ktp2i5OidbYxEEV+r4cfQTMMLNrULzlfSXiKrtXasY9VcHMhgGHomD6FVE2ZJ6BQCbYLke/9QNmdhVwBwqQL8Z13omO+ejk/r4FuUgfLQoX6JRwrkrWf1dkuTXsK1m2b0Cxaz9x96sLy5dCYm42igfsEGZ2CPqImYWs9kHwkRExbD2QZE15GMV5zUEp5aegcgtZvaO8yyP7Ei21XBVopm1nKBUKiTPQw3QDWuPRsnISz9H+hbYcMNfd21kTauBpegAsTDbYCyUpTKnYR1Wyl3xZzFx+fi1rQhvcfSawGbIUroaExTboGsgsbKVCOEc7128TXIAsc4PN7NO5+Xulffm5u//Y3V9w97fc/R5kiXob+HGy7nUFZefr/QrLlqrQ9+FIGM8DjkaC7BUzuz5l42Z0+l4xlZu4C9X2eghZfU9G13pmQVx4L7v7ZBQT+AiyGF4JPG9mD5rZtrl2byBBOQldL2chkTvHzE5MgqUraXSdL5umb3RhX6X3TBJrN6M4vzPc/aiSZt9HSRv7lIQWVMLMDkLndSZKrGqYmR0EnSEsbD2T8ciqso+7X5xfYGa7o5dnntfTtF2wfgnNtAUFSxcFVEY9l0Xp17GpKOwhKN5kaDG4Ph1fkdeBFcxsmSqizd0fMLOHgf82s8NQht4KKMOtjfWuszFs7j7PzF4EBprZKiVxbGunabt4nTr7/yx6WbfBVD4DVEahFFOB0SHIGje16jZz237HzN5EVs8+KE4NWhML7ihZZ46ZzUIvyHUpt4B1G5IF6izgrHQ9DkcZzbsAG5rZhim+stl7pYyTkcV4iLu3iccys/MoKV7s7jejBIo+KCN6exTTeZOZbZpEfRYKsF+yfm6AQiUOQvGeS6Ls5GxbJzS538UYtieBASgWs00mqJmtgq6VFyrEr2V9DUl9tblWUrzYGkiAt8tyTR8RNwMjkGWtTKyBEm6WAKabWdnyYaZs9n+6e7vnWHpuZLXetq7lLQiCriQEW88k+8ovSx8vq06flQbYzsyOaeDqbKYtKBB+EzNbyt3fKywb0mDdMtZEL5OpJWJtEK2B9cV93h4Ft/+24nbOQUUwx6Lq5QvS/4t0NoYNVFJiz7R/kwptR+fadJbMwlaM38qTJRtc4LmyIFVJsX7LI6GWj5nKrEC1Moiz+aV1sLor6UU8GZhsZrcj0bMREhLZvTKagiu9CT4PzCgRa0sioVhv3+ah62aamc1FcY2jkcUn324ByuKdYarj9zc0KsBxuWadjWGbhkqojKJ96Y5mr/FpKCRiFMoSz/Nl4FPAXSVJSf1QWMTmqIROvSSX39P2+s3oC+yKMtZvoiTezsyOQnFrjwLbdmFcbxDUJQRbz6QlTUeiyuIAmNl2tL60F+LuD5nZvSiZ4CigmPm5AjDP3d9ppm2a9Sf0tboPbbMr90YP8I4e23Az65XF2yRX2vmUX7NnI8E2wcz+VJIlOrAkGeEKFFT/PVSNfWqNRI4WOh/Ddi4SbD8wsynetnDuQagkQxshlzL+BqASHvmkkKXTfr2bm7cEcqENRUU9p5ftRLLIfJP6yQaZy/3dkvM4ILefVxYSRu5Gv8F4M7suH+9lZgei2LE5FMREsmIsihiqSqTzOxyYlhe0yYWYZRBmL/Eb0fW6g5nt7u6/KfRVdt0VaQHWNrNV3f2ltN4SSEC1K71hZlsD95ZYklfK75uZbYQyk1vqtcvogvM/Cd1LB5vZJG9bOPeY1KaNqE0CaxVkxcpbnq9FCU+7mdnZ3rZw7impzTmFvpZHFuMhwA/d/aR6O1srljLdk7sCf3X3ds9SMzsOCeOHUGZpuEGDRUYItp6JI4F0jZldRypBgL5Ir0YPnCLfQl/Ep5nZN9K/l0Auua+iUhAtHWh7dtqXc9LL5HmUKDAUfaFmrrJqByb32ZXIBfWomU1FsSzboppwj6JA9vw6U021544DnkhWhOfRy2k4soTsXVjnX2Z2CXK/QtcUj611TPea2RnIlf1Yyv7rjX6n/sC4khfrweilfSJt63mtjepz/R79Br3RudkAuULHUpvdUPxPzWSDxJeBC8zsTlS37jWU2PI19Fs8SPtAbUdWkU2Ap8zsBuQy/AKySn0AHJQPeLfWYXwaJUksSpZBNddazOwBFDP5SXSO10cZoU/AwqzsXZBQuMI0csT9qf36KCGo0TN2IhIyj6R7+T30obMBEoRjCu0nAKubCv62IIvlZugcP0drGZZtgDPSx9csFNc4CNVg+xCVtuky3P1ZM/suKkD7YEqImI+K4A4CJnj7UQ6+joTeJeTuT1d5jW8j4TY9PQ9eQ6Vc1k3zryr0NRmJtWeAJWu4eBeGKnQE01jAJ6Hr9W7gkBJ3aksxTCUIuopIOuiBuPtjwJYoQ/BrKH5lWZQ9VuqaSXFPXwB+gjIEDwb2Qy/iCeQC1ZtsOxO9HLLipPujB/UWdDxWaT+UhbgMskBth8TfUGoEGrv78Shw+14kEo9M6z1BKm9SQmZlmo0yyj4y3P0I9FKag87RWOSmGuPuxXpw9XgZZfx9EQX574uC4w8Hhjf44q+abPAQyvJcERUTPgJ9DPwvErjD3P31/AopcHsYEpmzkSXvMCRcrkHxiJML29kkTcvGhvy4mIcsy7PQ9XYoOpY30H3WpnBusv4MRhafzyFRvicVXenufh764JmNYk/3QB8bX0KJRUWy7NwNkTX9QPRhchq5Yc9Qss6ZSDzuiH7DLyNX4Agv1NHrClyjDeyAruux6HqbA+zt7kc22dcUFN5xF7oGxyExOx7YrcSdv0aaroXOe9nfYDpHto1e6Nou28bendxGENQkxhINFlusdZijU9z9uAbNgy4mlUQ4E9jY3Wc0ah8EQbA4Exa2YLEkZZuNR/FcH5k7NKjLV5CLMcRaEARBA8LCFixWmNlwJBRGIlfuL9x9XN2VgiAIguBjJpIOgsWNbVCsyWso67TDVc6DIAiCYFERFrYgCIIgCIJuTsSwBUEQBEEQdHNCsAVBEARBEHRzQrAFQRAEQRB0c0KwBUEQBEEQdHNCsAVBEARBEHRzQrAFQRAEQRB0c/4faCAi4o9VnCsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "y_pred   = model.predict_classes(x_test)\n",
     "conf_mat = confusion_matrix(y_test,y_pred, normalize=\"true\", labels=range(43))\n",
diff --git a/GTSRB/04.1-Data-augmentation.ipynb b/GTSRB/04.1-Data-augmentation.ipynb
deleted file mode 100644
index cf5ef5807f421a12b5afa8f12bc777c954ecfb90..0000000000000000000000000000000000000000
--- a/GTSRB/04.1-Data-augmentation.ipynb
+++ /dev/null
@@ -1,353 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "German Traffic Sign Recognition Benchmark (GTSRB)\n",
-    "=================================================\n",
-    "---\n",
-    "Introduction au Deep Learning  (IDLE) - S. Aria, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020\n",
-    "\n",
-    "## Episode 3 : Tracking, visualizing and save models\n",
-    "\n",
-    "Our main steps:\n",
-    " - Monitoring and understanding our model training\n",
-    " - Analyze the results \n",
-    " - Improving our model\n",
-    " - Add recovery points\n",
-    "\n",
-    "\n",
-    "## 1/ Import and init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IDLE 2020 - Practical Work Module\n",
-      "  Version            : 0.1.4\n",
-      "  Run time           : Monday 13 January 2020, 21:13:18\n",
-      "  Matplotlib style   : idle/talk.mplstyle\n",
-      "  TensorFlow version : 2.0.0\n",
-      "  Keras version      : 2.2.4-tf\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras.callbacks import TensorBoard\n",
-    "\n",
-    "import numpy as np\n",
-    "import h5py\n",
-    "import matplotlib.pyplot as plt\n",
-    "import os, time, random\n",
-    "\n",
-    "import idle.pwk as ooo\n",
-    "from importlib import reload\n",
-    "\n",
-    "ooo.init()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2/ Reload dataset\n",
-    "Dataset is one of the saved dataset: RGB25, RGB35, L25, L35, etc.  \n",
-    "First of all, we're going to use a smart dataset : **set-24x24-L**  \n",
-    "(with a GPU, it only takes 35'' compared to more than 5' with a CPU !)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset loaded (228.8 Mo)\n",
-      "\n",
-      "CPU times: user 0 ns, sys: 297 ms, total: 297 ms\n",
-      "Wall time: 294 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "dataset ='set-24x24-L'\n",
-    "\n",
-    "# ---- Read dataset\n",
-    "#\n",
-    "filename='./data/'+dataset+'.h5'\n",
-    "with  h5py.File(filename) as f:\n",
-    "    x_train = f['x_train'][:]\n",
-    "    y_train = f['y_train'][:]\n",
-    "    x_test  = f['x_test'][:]\n",
-    "    y_test  = f['y_test'][:]\n",
-    "\n",
-    "# ---- Dataset shape\n",
-    "#\n",
-    "(n,lx,ly,lz) = x_train.shape\n",
-    "data_shape   = (lx, ly, lz)\n",
-    "\n",
-    "# ---- done\n",
-    "print('Dataset loaded ({:.1f} Mo)\\n'.format(os.path.getsize(filename)/(1024*1024)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3/ Have a look to the dataset\n",
-    "Note: Data must be reshape for matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train :  (39209, 24, 24, 1)\n",
-      "y_train :  (39209,)\n",
-      "x_test  :  (12630, 24, 24, 1)\n",
-      "y_test  :  (12630,)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 864x291.6 with 36 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(\"x_train : \", x_train.shape)\n",
-    "print(\"y_train : \", y_train.shape)\n",
-    "print(\"x_test  : \", x_test.shape)\n",
-    "print(\"y_test  : \", y_test.shape)\n",
-    "\n",
-    "ooo.plot_images(x_train, y_train, range(36), columns=12, x_size=1, y_size=1)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4/ Create model\n",
-    "Some hyperparameters :"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size  =  64\n",
-    "num_classes =  43\n",
-    "epochs      =  8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "My models :"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "def get_model_v1():\n",
-    "    model = keras.models.Sequential()\n",
-    "    model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
-    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
-    "    model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
-    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
-    "    model.add( keras.layers.Flatten()) \n",
-    "    model.add( keras.layers.Dense(500, activation='relu'))\n",
-    "    model.add( keras.layers.Dense(500, activation='relu'))\n",
-    "    model.add( keras.layers.Dense(43, activation='softmax'))\n",
-    "    return model\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_2\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_4 (Conv2D)            (None, 22, 22, 96)        960       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 11, 11, 96)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 9, 9, 192)         166080    \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 192)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 3072)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_6 (Dense)              (None, 500)               1536500   \n",
-      "_________________________________________________________________\n",
-      "dense_7 (Dense)              (None, 500)               250500    \n",
-      "_________________________________________________________________\n",
-      "dense_8 (Dense)              (None, 43)                21543     \n",
-      "=================================================================\n",
-      "Total params: 1,975,583\n",
-      "Trainable params: 1,975,583\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ---- The model I want to test..\n",
-    "#\n",
-    "model = get_model_v1()\n",
-    "model.summary()\n",
-    "model.compile(optimizer='adam',\n",
-    "              loss='sparse_categorical_crossentropy',\n",
-    "              metrics=['accuracy'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4/ Data augmentation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datagen = keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,\n",
-    "                             featurewise_std_normalization=False,\n",
-    "                             width_shift_range=0.1,\n",
-    "                             height_shift_range=0.1,\n",
-    "                             zoom_range=0.2,\n",
-    "                             shear_range=0.1,\n",
-    "                             rotation_range=10.)\n",
-    "datagen.fit(x_train[:2000])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5/ Run model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 2000 samples, validate on 200 samples\n",
-      "Epoch 1/8\n",
-      "2000/2000 [==============================] - 2s 767us/sample - loss: 0.2712 - accuracy: 0.9195 - val_loss: 1.0751 - val_accuracy: 0.7650\n",
-      "Epoch 2/8\n",
-      "2000/2000 [==============================] - 2s 829us/sample - loss: 0.2290 - accuracy: 0.9295 - val_loss: 1.1727 - val_accuracy: 0.7400\n",
-      "Epoch 3/8\n",
-      "2000/2000 [==============================] - 2s 906us/sample - loss: 0.1570 - accuracy: 0.9515 - val_loss: 1.0644 - val_accuracy: 0.8350\n",
-      "Epoch 4/8\n",
-      "2000/2000 [==============================] - 2s 896us/sample - loss: 0.1282 - accuracy: 0.9640 - val_loss: 1.0879 - val_accuracy: 0.8150\n",
-      "Epoch 5/8\n",
-      "2000/2000 [==============================] - 2s 913us/sample - loss: 0.0847 - accuracy: 0.9750 - val_loss: 1.1590 - val_accuracy: 0.8050\n",
-      "Epoch 6/8\n",
-      "2000/2000 [==============================] - 2s 920us/sample - loss: 0.0754 - accuracy: 0.9810 - val_loss: 1.2716 - val_accuracy: 0.8100\n",
-      "Epoch 7/8\n",
-      "2000/2000 [==============================] - 2s 930us/sample - loss: 0.0910 - accuracy: 0.9750 - val_loss: 1.1533 - val_accuracy: 0.8350\n",
-      "Epoch 8/8\n",
-      "2000/2000 [==============================] - 2s 973us/sample - loss: 0.0671 - accuracy: 0.9825 - val_loss: 1.1136 - val_accuracy: 0.8350\n",
-      "CPU times: user 1min 21s, sys: 7.22 s, total: 1min 29s\n",
-      "Wall time: 14.3 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "# ---- Shuffle train data\n",
-    "# x_train,y_train=ooo.shuffle_np_dataset(x_train,y_train)\n",
-    "\n",
-    "# ---- Train\n",
-    "# Note: To be faster in our example, we take only 2000 values\n",
-    "#       but in the real world, we'd take the whole dataset!\n",
-    "#\n",
-    "history = model.fit(  \n",
-    "                      x_train[:2000], y_train[:2000],  \n",
-    "#                       datagen.flow(x_train[:2000], y_train[:2000], batch_size=batch_size),\n",
-    "#                       batch_size=batch_size,\n",
-    "                      epochs=epochs,\n",
-    "                      verbose=1,\n",
-    "                      validation_data=(x_test[:200], y_test[:200]))\n",
-    "\n",
-    "# model.save('./run/models/last-model.h5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/GTSRB/07-Full-convolutions.ipynb b/GTSRB/05-Full-convolutions.ipynb
similarity index 54%
rename from GTSRB/07-Full-convolutions.ipynb
rename to GTSRB/05-Full-convolutions.ipynb
index 05a727817b006f4ce71007cf3860eafc0ddaa3fb..95d1d49ae4ce345a8ab162afd20996f438851202 100644
--- a/GTSRB/07-Full-convolutions.ipynb
+++ b/GTSRB/05-Full-convolutions.ipynb
@@ -8,62 +8,88 @@
     "=================================================\n",
     "---\n",
     "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  \n",
-    "Vesion : 1.2.1\n",
     "\n",
-    "## Episode 7 : Full Convolutions\n",
+    "## Episode 5 : Full Convolutions\n",
     "\n",
     "Our main steps:\n",
     " - Try n models with n datasets\n",
+    " - Save a Pandas/h5 report\n",
+    " - Can be run in :\n",
+    "    - Notebook mode\n",
+    "    - Batch mode \n",
+    "    - Tensorboard follow up\n",
+    "    \n",
+    "To export a notebook as a script :  \n",
+    "```jupyter nbconvert --to script <notebook>```\n",
+    "\n",
+    "To run a notebook :  \n",
+    "```jupyter nbconvert --to notebook --execute <notebook>```\n",
     "\n",
-    "## 1/ Import and init"
+    "## 1/ Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import h5py\n",
+    "import os,time\n",
+    "\n",
+    "from IPython.display import display\n",
+    "\n",
+    "VERSION='1.2'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2/ Init and start"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "IDLE 2020 - Practical Work Module\n",
-      "  Version            : 0.1.4\n",
-      "  Run time           : Friday 17 January 2020, 21:38:34\n",
-      "  Matplotlib style   : idle/talk.mplstyle\n",
+      "\n",
+      "Full Convolutions Notebook\n",
+      "  Version            : 1.0\n",
+      "  Run time           : Sunday 19 January 2020, 12:37:56\n",
       "  TensorFlow version : 2.0.0\n",
       "  Keras version      : 2.2.4-tf\n"
      ]
     }
    ],
    "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow import keras\n",
-    "from tensorflow.keras.callbacks import TensorBoard\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import h5py\n",
-    "import os,time\n",
-    "\n",
-    "import pandas as pd\n",
-    "import idle.pwk as ooo\n",
-    "from importlib import reload\n",
-    "from IPython.display import display\n",
-    "\n",
-    "ooo.init()"
+    "print('\\nFull Convolutions Notebook')\n",
+    "print('  Version            : {}'.format(VERSION))\n",
+    "print('  Run time           : {}'.format(time.strftime(\"%A %-d %B %Y, %H:%M:%S\")))\n",
+    "print('  TensorFlow version :',tf.__version__)\n",
+    "print('  Keras version      :',tf.keras.__version__)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2/ Load dataset functions"
+    "## 3/ Dataset loading"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,12 +113,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3/ Models collection"
+    "## 4/ Models collection"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,29 +202,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4/ Callbacks  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "total 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%bash\n",
-    "# To clean old logs and saved model, run this cell\n",
-    "#\n",
-    "/bin/rm -r ./run/logs 2>/dev/null\n",
-    "/bin/rm -r ./run/models 2>/dev/null\n",
-    "/bin/ls -l ./run  2>/dev/null"
+    "## 5/ Multiple datasets, multiple models ;-)"
    ]
   },
   {
@@ -206,35 +210,6 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "ooo.mkdir('./run/models')\n",
-    "ooo.mkdir('./run/logs')\n",
-    "\n",
-    "# ---- Callback tensorboard\n",
-    "log_dir = \"./run/logs/tb_\" + ooo.tag_now()\n",
-    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
-    "\n",
-    "# ---- Callback ModelCheckpoint - Save best model\n",
-    "save_dir = \"./run/models/best-model.h5\"\n",
-    "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
-    "\n",
-    "# ---- Callback ModelCheckpoint - Save model each epochs\n",
-    "save_dir = \"./run/models/model-{epoch:04d}.h5\"\n",
-    "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_freq=2000*5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6/ Multiple datasets, multiple models ;-)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def multi_run(datasets, models, batch_size=64, epochs=16):\n",
     "\n",
@@ -249,26 +224,32 @@
     "\n",
     "    # ---- Let's go\n",
     "    #\n",
-    "    for dname in datasets:\n",
-    "        print(\"\\nDataset : \",dname)\n",
+    "    for d_name in datasets:\n",
+    "        print(\"\\nDataset : \",d_name)\n",
     "\n",
     "        # ---- Read dataset\n",
-    "        x_train,y_train,x_test,y_test = read_dataset(dname)\n",
-    "        dsize=os.path.getsize('./data/'+dname+'.h5')/(1024*1024)\n",
-    "        report['Dataset'].append(dname)\n",
-    "        report['Size'].append(dname)\n",
+    "        x_train,y_train,x_test,y_test = read_dataset(d_name)\n",
+    "        d_size=os.path.getsize('./data/'+d_name+'.h5')/(1024*1024)\n",
+    "        report['Dataset'].append(d_name)\n",
+    "        report['Size'].append(d_size)\n",
     "        \n",
     "        # ---- Get the shape\n",
     "        (n,lx,ly,lz) = x_train.shape\n",
     "\n",
     "        # ---- For each model\n",
-    "        for kmodel,fmodel in models.items():\n",
-    "            print(\"    Run model {}  : \".format(kmodel), end='')\n",
+    "        for m_name,m_function in models.items():\n",
+    "            print(\"    Run model {}  : \".format(m_name), end='')\n",
     "            # ---- get model\n",
     "            try:\n",
-    "                model=fmodel(lx,ly,lz)\n",
+    "                model=m_function(lx,ly,lz)\n",
     "                # ---- Compile it\n",
     "                model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "                # ---- Callbacks tensorboard\n",
+    "                log_dir = \"./run/logs/tb_{}_{}\".format(d_name,m_name)\n",
+    "                tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
+    "                # ---- Callbacks bestmodel\n",
+    "                save_dir = \"./run/models/model_{}_{}.h5\".format(d_name,m_name)\n",
+    "                bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
     "                # ---- Train\n",
     "                start_time = time.time()\n",
     "                history = model.fit(  x_train[:1000], y_train[:1000],\n",
@@ -276,237 +257,199 @@
     "                                    epochs          = epochs,\n",
     "                                    verbose         = 0,\n",
     "                                    validation_data = (x_test, y_test),\n",
-    "                                    callbacks       = [tensorboard_callback, bestmodel_callback, savemodel_callback])\n",
+    "                                    callbacks       = [tensorboard_callback, bestmodel_callback])\n",
     "                # ---- Result\n",
     "                end_time = time.time()\n",
     "                duration = end_time-start_time\n",
     "                accuracy = max(history.history[\"val_accuracy\"])*100\n",
     "                #\n",
-    "                report[kmodel+' Accuracy'].append(accuracy)\n",
-    "                report[kmodel+' Duration'].append(duration)\n",
+    "                report[m_name+' Accuracy'].append(accuracy)\n",
+    "                report[m_name+' Duration'].append(duration)\n",
     "                print(\"Accuracy={:.2f} and Duration={:.2f})\".format(accuracy,duration))\n",
     "            except:\n",
-    "                report[kmodel+' Accuracy'].append('-')\n",
-    "                report[kmodel+' Duration'].append('-')\n",
+    "                report[m_name+' Accuracy'].append('-')\n",
+    "                report[m_name+' Duration'].append('-')\n",
     "                print('-')\n",
-    "    print(\"\\n\")\n",
     "    return report"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6/ Run\n",
+    "### 6.1/ Clean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reset directories : ./run/logs and ./run/models .\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "/bin/rm -r ./run/logs   2>/dev/null\n",
+    "/bin/rm -r ./run/models 2>/dev/null\n",
+    "/bin/mkdir -p -m 755 ./run/logs\n",
+    "/bin/mkdir -p -m 755 ./run/models\n",
+    "echo -e \"\\nReset directories : ./run/logs and ./run/models .\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.2 Start Tensorboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensorbord started with pid 1610\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "tensorboard_start --logdir ./run/logs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.3/ run and save report"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "---- Run --------------------------------------------------\n",
       "\n",
       "Dataset :  set-24x24-L\n",
-      "    Run model v1  : Accuracy=9.46 and Duration=7.51)\n",
+      "    Run model v1  : -\n",
       "    Run model v3  : -\n",
       "\n",
       "Dataset :  set-24x24-RGB\n",
-      "    Run model v1  : Accuracy=15.95 and Duration=7.95)\n",
+      "    Run model v1  : -\n",
       "    Run model v3  : -\n",
       "\n",
-      "\n",
-      "CPU times: user 1min 35s, sys: 3.31 s, total: 1min 38s\n",
-      "Wall time: 17 s\n"
+      "Report saved as  ./run/report-2020-01-19_14h56m27s.h5\n",
+      "-----------------------------------------------------------\n",
+      "CPU times: user 29.2 s, sys: 4 s, total: 33.2 s\n",
+      "Wall time: 7.57 s\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
     "\n",
-    "# datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
-    "# models   = {'v1':get_model_v1, 'v2':get_model_v2, 'v3':get_model_v3}\n",
+    "print('\\n---- Run','-'*50)\n",
+    "\n",
+    "# ---- Datasets and models list\n",
     "\n",
+    "# For tests\n",
     "datasets = ['set-24x24-L', 'set-24x24-RGB']\n",
     "models   = {'v1':get_model_v1, 'v3':get_model_v3}\n",
     "\n",
+    "# The real one\n",
+    "# datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
+    "# models   = {'v1':get_model_v1, 'v2':get_model_v2, 'v3':get_model_v3}\n",
+    "\n",
+    "# ---- Report name\n",
+    "\n",
+    "report_name='./run/report-{}.h5'.format(time.strftime(\"%Y-%m-%d_%Hh%Mm%Ss\"))\n",
+    "\n",
+    "# ---- Run\n",
+    "\n",
     "out    = multi_run(datasets, models, batch_size=64, epochs=2)\n",
-    "report = pd.DataFrame (out)\n"
+    "\n",
+    "# ---- Save report\n",
+    "\n",
+    "output = pd.DataFrame (out)\n",
+    "params = pd.DataFrame( {'datasets':datasets, 'models':list(models.keys())} )\n",
+    "\n",
+    "output.to_hdf(report_name, 'output')\n",
+    "params.to_hdf(report_name, 'params')\n",
+    "\n",
+    "print('\\nReport saved as ',report_name)\n",
+    "print('-'*59)\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 38,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Dataset</th>\n",
-       "      <th>Size</th>\n",
-       "      <th>v1 Accuracy</th>\n",
-       "      <th>v1 Duration</th>\n",
-       "      <th>v3 Accuracy</th>\n",
-       "      <th>v3 Duration</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>set-24x24-L</td>\n",
-       "      <td>set-24x24-L</td>\n",
-       "      <td>9.46160</td>\n",
-       "      <td>7.514726</td>\n",
-       "      <td>-</td>\n",
-       "      <td>-</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>set-24x24-RGB</td>\n",
-       "      <td>set-24x24-RGB</td>\n",
-       "      <td>15.94616</td>\n",
-       "      <td>7.946994</td>\n",
-       "      <td>-</td>\n",
-       "      <td>-</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Dataset           Size  v1 Accuracy  v1 Duration v3 Accuracy  \\\n",
-       "0    set-24x24-L    set-24x24-L      9.46160     7.514726           -   \n",
-       "1  set-24x24-RGB  set-24x24-RGB     15.94616     7.946994           -   \n",
-       "\n",
-       "  v3 Duration  \n",
-       "0           -  \n",
-       "1           -  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "display(report)\n",
-    "df.to_hdf('foo.h5', 'df')"
+    "### 6.4/ Stop Tensorboard"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Dataset</th>\n",
-       "      <th>Size</th>\n",
-       "      <th>v1 Accuracy</th>\n",
-       "      <th>v1 Duration</th>\n",
-       "      <th>v3 Accuracy</th>\n",
-       "      <th>v3 Duration</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>set-24x24-L</td>\n",
-       "      <td>set-24x24-L</td>\n",
-       "      <td>11.892320</td>\n",
-       "      <td>8.730333</td>\n",
-       "      <td>-</td>\n",
-       "      <td>-</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>set-24x24-RGB</td>\n",
-       "      <td>set-24x24-RGB</td>\n",
-       "      <td>12.707838</td>\n",
-       "      <td>8.308997</td>\n",
-       "      <td>-</td>\n",
-       "      <td>-</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Dataset           Size  v1 Accuracy  v1 Duration v3 Accuracy  \\\n",
-       "0    set-24x24-L    set-24x24-L    11.892320     8.730333           -   \n",
-       "1  set-24x24-RGB  set-24x24-RGB    12.707838     8.308997           -   \n",
-       "\n",
-       "  v3 Duration  \n",
-       "0           -  \n",
-       "1           -  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensorbord stopped (1610)\n"
+     ]
     }
    ],
    "source": [
-    "df=pd.read_hdf('foo.h5', 'df')\n",
-    "display(df)"
+    "%%bash\n",
+    "tensorboard_stop"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "---\n",
-    "\n",
-    "\n",
-    "### Some results :  \n",
-    "\n"
+    "## 7/ That's all folks.."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Sunday 19 January 2020, 14:32:36\n",
+      "The work is done.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "|         Datasets         |       Size      |     Model : v1     |    Model : v2      |     Model : v3     |\n",
-    "|:------------------------:|:---------------:|:------------------:|:------------------:|:------------------:|\n",
-    "| set-24x24-L              |          229 Mo |   95.91%  75.04s   |   96.86% 102.28s   |   -      -         |\n",
-    "| set-24x24-RGB            |          684 Mo |   96.60%  77.24s   |   97.32% 103.93s   |   -      -         |\n",
-    "| set-48x48-L              |          914 Mo | **96.71%** 123.94s |   97.68% 149.57s   |  97.60%  91.53s    |\n",
-    "| set-48x48-RGB            |         2736 Mo |   96.36% 117.74s   | **98.20%** 142.63s |  97.28%  91.29s    |\n",
-    "| set-24x24-L-LHE          |          229 Mo |   95.95%  66.12s   |   96.75%  89.45s   |   -      -         |\n",
-    "| set-24x24-RGB-HE         |          684 Mo |   95.30%  68.89s   |   96.28%  92.15s   |   -      -         |\n",
-    "| set-48x48-L-LHE          |          914 Mo |   96.69% 109.28s   |   97.94% 135.17s   | **97.97%** 83.80s  |\n",
-    "| set-48x48-RGB-HE         |         2736 Mo |   95.29% 117.70s   | **98.13%** 141.56s |  97.00%  89.38s    |"
+    "print('\\n{}'.format(time.strftime(\"%A %-d %B %Y, %H:%M:%S\")))\n",
+    "print(\"The work is done.\\n\")"
    ]
   },
   {
diff --git a/GTSRB/05-Full-convolutions.nbconvert.ipynb b/GTSRB/05-Full-convolutions.nbconvert.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8f71569c32ea8709a07bffe031f4fef03ce505fe
--- /dev/null
+++ b/GTSRB/05-Full-convolutions.nbconvert.ipynb
@@ -0,0 +1,484 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "German Traffic Sign Recognition Benchmark (GTSRB)\n",
+    "=================================================\n",
+    "---\n",
+    "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  \n",
+    "\n",
+    "## Episode 5 : Full Convolutions\n",
+    "\n",
+    "Our main steps:\n",
+    " - Try n models with n datasets\n",
+    " - Save a Pandas/h5 report\n",
+    " - Can be run in :\n",
+    "    - Notebook mode\n",
+    "    - Batch mode \n",
+    "    - Tensorboard follow up\n",
+    "    \n",
+    "To export a notebook as a script :  \n",
+    "```jupyter nbconvert --to script <notebook>```\n",
+    "\n",
+    "To run a notebook :  \n",
+    "```jupyter nbconvert --to notebook --execute <notebook>```\n",
+    "\n",
+    "## 1/ Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import h5py\n",
+    "import os,time\n",
+    "\n",
+    "from IPython.display import display\n",
+    "\n",
+    "VERSION='1.2'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2/ Init and start"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Full Convolutions Notebook\n",
+      "  Version            : 1.2\n",
+      "  Run time           : Sunday 19 January 2020, 15:35:20\n",
+      "  TensorFlow version : 2.0.0\n",
+      "  Keras version      : 2.2.4-tf\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('\\nFull Convolutions Notebook')\n",
+    "print('  Version            : {}'.format(VERSION))\n",
+    "print('  Run time           : {}'.format(time.strftime(\"%A %-d %B %Y, %H:%M:%S\")))\n",
+    "print('  TensorFlow version :',tf.__version__)\n",
+    "print('  Keras version      :',tf.keras.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3/ Dataset loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_dataset(name):\n",
+    "    '''Reads h5 dataset from ./data\n",
+    "\n",
+    "    Arguments:  dataset name, without .h5\n",
+    "    Returns:    x_train,y_train,x_test,y_test data'''\n",
+    "    # ---- Read dataset\n",
+    "    filename='./data/'+name+'.h5'\n",
+    "    with  h5py.File(filename) as f:\n",
+    "        x_train = f['x_train'][:]\n",
+    "        y_train = f['y_train'][:]\n",
+    "        x_test  = f['x_test'][:]\n",
+    "        y_test  = f['y_test'][:]\n",
+    "\n",
+    "    return x_train,y_train,x_test,y_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4/ Models collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# A basic model\n",
+    "#\n",
+    "def get_model_v1(lx,ly,lz):\n",
+    "    \n",
+    "    model = keras.models.Sequential()\n",
+    "    \n",
+    "    model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
+    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.2))\n",
+    "\n",
+    "    model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
+    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.2))\n",
+    "\n",
+    "    model.add( keras.layers.Flatten()) \n",
+    "    model.add( keras.layers.Dense(1500, activation='relu'))\n",
+    "    model.add( keras.layers.Dropout(0.5))\n",
+    "\n",
+    "    model.add( keras.layers.Dense(43, activation='softmax'))\n",
+    "    return model\n",
+    "    \n",
+    "# A more sophisticated model\n",
+    "#\n",
+    "def get_model_v2(lx,ly,lz):\n",
+    "    model = keras.models.Sequential()\n",
+    "\n",
+    "    model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))\n",
+    "    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
+    "    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.2))\n",
+    "\n",
+    "    model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
+    "    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
+    "    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.2))\n",
+    "\n",
+    "    model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))\n",
+    "    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
+    "    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.2))\n",
+    "\n",
+    "    model.add( keras.layers.Flatten())\n",
+    "    model.add( keras.layers.Dense(512, activation='relu'))\n",
+    "    model.add( keras.layers.Dropout(0.5))\n",
+    "    model.add( keras.layers.Dense(43, activation='softmax'))\n",
+    "    return model\n",
+    "\n",
+    "# My sphisticated model, but small and fast\n",
+    "#\n",
+    "def get_model_v3(lx,ly,lz):\n",
+    "    model = keras.models.Sequential()\n",
+    "    model.add( keras.layers.Conv2D(32, (3,3),   activation='relu', input_shape=(lx,ly,lz)))\n",
+    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.5))\n",
+    "\n",
+    "    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
+    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.5))\n",
+    "\n",
+    "    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
+    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.5))\n",
+    "\n",
+    "    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
+    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
+    "    model.add( keras.layers.Dropout(0.5))\n",
+    "\n",
+    "    model.add( keras.layers.Flatten()) \n",
+    "    model.add( keras.layers.Dense(1152, activation='relu'))\n",
+    "    model.add( keras.layers.Dropout(0.5))\n",
+    "\n",
+    "    model.add( keras.layers.Dense(43, activation='softmax'))\n",
+    "    return model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5/ Multiple datasets, multiple models ;-)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def multi_run(datasets, models, batch_size=64, epochs=16):\n",
+    "\n",
+    "    # ---- Columns of report\n",
+    "    #\n",
+    "    report={}\n",
+    "    report['Dataset']=[]\n",
+    "    report['Size']   =[]\n",
+    "    for m in models:\n",
+    "        report[m+' Accuracy'] = []\n",
+    "        report[m+' Duration'] = []\n",
+    "\n",
+    "    # ---- Let's go\n",
+    "    #\n",
+    "    for d_name in datasets:\n",
+    "        print(\"\\nDataset : \",d_name)\n",
+    "\n",
+    "        # ---- Read dataset\n",
+    "        x_train,y_train,x_test,y_test = read_dataset(d_name)\n",
+    "        d_size=os.path.getsize('./data/'+d_name+'.h5')/(1024*1024)\n",
+    "        report['Dataset'].append(d_name)\n",
+    "        report['Size'].append(d_size)\n",
+    "        \n",
+    "        # ---- Get the shape\n",
+    "        (n,lx,ly,lz) = x_train.shape\n",
+    "\n",
+    "        # ---- For each model\n",
+    "        for m_name,m_function in models.items():\n",
+    "            print(\"    Run model {}  : \".format(m_name), end='')\n",
+    "            # ---- get model\n",
+    "            try:\n",
+    "                model=m_function(lx,ly,lz)\n",
+    "                # ---- Compile it\n",
+    "                model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "                # ---- Callbacks tensorboard\n",
+    "                log_dir = \"./run/logs/tb_{}_{}\".format(d_name,m_name)\n",
+    "                tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
+    "                # ---- Callbacks bestmodel\n",
+    "                save_dir = \"./run/models/model_{}_{}.h5\".format(d_name,m_name)\n",
+    "                bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
+    "                # ---- Train\n",
+    "                start_time = time.time()\n",
+    "                history = model.fit(  x_train[:1000], y_train[:1000],\n",
+    "                                    batch_size      = batch_size,\n",
+    "                                    epochs          = epochs,\n",
+    "                                    verbose         = 0,\n",
+    "                                    validation_data = (x_test, y_test),\n",
+    "                                    callbacks       = [tensorboard_callback, bestmodel_callback])\n",
+    "                # ---- Result\n",
+    "                end_time = time.time()\n",
+    "                duration = end_time-start_time\n",
+    "                accuracy = max(history.history[\"val_accuracy\"])*100\n",
+    "                #\n",
+    "                report[m_name+' Accuracy'].append(accuracy)\n",
+    "                report[m_name+' Duration'].append(duration)\n",
+    "                print(\"Accuracy={:.2f} and Duration={:.2f})\".format(accuracy,duration))\n",
+    "            except:\n",
+    "                report[m_name+' Accuracy'].append('-')\n",
+    "                report[m_name+' Duration'].append('-')\n",
+    "                print('-')\n",
+    "    return report"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6/ Run\n",
+    "### 6.1/ Clean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reset directories : ./run/logs and ./run/models .\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "/bin/rm -r ./run/logs   2>/dev/null\n",
+    "/bin/rm -r ./run/models 2>/dev/null\n",
+    "/bin/mkdir -p -m 755 ./run/logs\n",
+    "/bin/mkdir -p -m 755 ./run/models\n",
+    "echo -e \"\\nReset directories : ./run/logs and ./run/models .\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.2 Start Tensorboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensorbord started with pid 2128\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "tensorboard_start --logdir ./run/logs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.3/ run and save report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "---- Run --------------------------------------------------\n",
+      "\n",
+      "Dataset :  set-24x24-L\n",
+      "    Run model v1  : Accuracy=6.76 and Duration=8.58)\n",
+      "    Run model v3  : -\n",
+      "\n",
+      "Dataset :  set-24x24-RGB\n",
+      "    Run model v1  : Accuracy=17.55 and Duration=8.21)\n",
+      "    Run model v3  : -\n",
+      "\n",
+      "Report saved as  ./run/report-2020-01-19_15h35m25s.h5\n",
+      "-----------------------------------------------------------\n",
+      "CPU times: user 1min 37s, sys: 8.44 s, total: 1min 45s\n",
+      "Wall time: 18.5 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "print('\\n---- Run','-'*50)\n",
+    "\n",
+    "# ---- Datasets and models list\n",
+    "\n",
+    "# For tests\n",
+    "datasets = ['set-24x24-L', 'set-24x24-RGB']\n",
+    "models   = {'v1':get_model_v1, 'v3':get_model_v3}\n",
+    "\n",
+    "# The real one\n",
+    "# datasets = ['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']\n",
+    "# models   = {'v1':get_model_v1, 'v2':get_model_v2, 'v3':get_model_v3}\n",
+    "\n",
+    "# ---- Report name\n",
+    "\n",
+    "report_name='./run/report-{}.h5'.format(time.strftime(\"%Y-%m-%d_%Hh%Mm%Ss\"))\n",
+    "\n",
+    "# ---- Run\n",
+    "\n",
+    "out    = multi_run(datasets, models, batch_size=64, epochs=2)\n",
+    "\n",
+    "# ---- Save report\n",
+    "\n",
+    "output = pd.DataFrame (out)\n",
+    "params = pd.DataFrame( {'datasets':datasets, 'models':list(models.keys())} )\n",
+    "\n",
+    "output.to_hdf(report_name, 'output')\n",
+    "params.to_hdf(report_name, 'params')\n",
+    "\n",
+    "print('\\nReport saved as ',report_name)\n",
+    "print('-'*59)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.4/ Stop Tensorboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensorbord stopped (2128)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "tensorboard_stop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7/ That's all folks.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Sunday 19 January 2020, 15:35:44\n",
+      "The work is done.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('\\n{}'.format(time.strftime(\"%A %-d %B %Y, %H:%M:%S\")))\n",
+    "print(\"The work is done.\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/GTSRB/05-Full-convolutions.py b/GTSRB/05-Full-convolutions.py
new file mode 100644
index 0000000000000000000000000000000000000000..780579b8a1e395556079d92f96b02459247c6273
--- /dev/null
+++ b/GTSRB/05-Full-convolutions.py
@@ -0,0 +1,270 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# German Traffic Sign Recognition Benchmark (GTSRB)
+# =================================================
+# ---
+# Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  
+# 
+# ## Episode 5 : Full Convolutions
+# 
+# Our main steps:
+#  - Try n models with n datasets
+#  - Save a Pandas/h5 report
+#  - Can be run in :
+#     - Notebook mode
+#     - Batch mode 
+#     - Tensorboard follow up
+#     
+# To export a notebook as a script :  
+# ```jupyter nbconvert --to script <notebook>```
+# 
+# To run a notebook :  
+# ```jupyter nbconvert --to notebook --execute <notebook>```
+# 
+# ## 1/ Import
+
+# In[1]:
+
+
+import tensorflow as tf
+from tensorflow import keras
+
+import numpy as np
+import pandas as pd
+import h5py
+import os,time
+
+from IPython.display import display
+
+VERSION='1.2'
+
+
+# ## 2/ Init and start
+
+# In[2]:
+
+
+print('\nFull Convolutions Notebook')
+print('  Version            : {}'.format(VERSION))
+print('  Run time           : {}'.format(time.strftime("%A %-d %B %Y, %H:%M:%S")))
+print('  TensorFlow version :',tf.__version__)
+print('  Keras version      :',tf.keras.__version__)
+
+
+# ## 3/ Dataset loading
+
+# In[3]:
+
+
+def read_dataset(name):
+    '''Reads h5 dataset from ./data
+
+    Arguments:  dataset name, without .h5
+    Returns:    x_train,y_train,x_test,y_test data'''
+    # ---- Read dataset
+    filename='./data/'+name+'.h5'
+    with  h5py.File(filename) as f:
+        x_train = f['x_train'][:]
+        y_train = f['y_train'][:]
+        x_test  = f['x_test'][:]
+        y_test  = f['y_test'][:]
+
+    return x_train,y_train,x_test,y_test
+
+
+# ## 4/ Models collection
+
+# In[4]:
+
+
+
+# A basic model
+#
+def get_model_v1(lx,ly,lz):
+    
+    model = keras.models.Sequential()
+    
+    model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Flatten()) 
+    model.add( keras.layers.Dense(1500, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+    
+# A more sophisticated model
+#
+def get_model_v2(lx,ly,lz):
+    model = keras.models.Sequential()
+
+    model.add( keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=(lx,ly,lz), activation='relu'))
+    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
+    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))
+    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add( keras.layers.Dropout(0.2))
+
+    model.add( keras.layers.Flatten())
+    model.add( keras.layers.Dense(512, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+
+# My sphisticated model, but small and fast
+#
+def get_model_v3(lx,ly,lz):
+    model = keras.models.Sequential()
+    model.add( keras.layers.Conv2D(32, (3,3),   activation='relu', input_shape=(lx,ly,lz)))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Conv2D(64, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Conv2D(128, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Conv2D(256, (3, 3), activation='relu'))
+    model.add( keras.layers.MaxPooling2D((2, 2)))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Flatten()) 
+    model.add( keras.layers.Dense(1152, activation='relu'))
+    model.add( keras.layers.Dropout(0.5))
+
+    model.add( keras.layers.Dense(43, activation='softmax'))
+    return model
+
+
+# ## 5/ Multiple datasets, multiple models ;-)
+
+# In[5]:
+
+
+def multi_run(datasets, models, batch_size=64, epochs=16):
+
+    # ---- Columns of report
+    #
+    report={}
+    report['Dataset']=[]
+    report['Size']   =[]
+    for m in models:
+        report[m+' Accuracy'] = []
+        report[m+' Duration'] = []
+
+    # ---- Let's go
+    #
+    for d_name in datasets:
+        print("\nDataset : ",d_name)
+
+        # ---- Read dataset
+        x_train,y_train,x_test,y_test = read_dataset(d_name)
+        d_size=os.path.getsize('./data/'+d_name+'.h5')/(1024*1024)
+        report['Dataset'].append(d_name)
+        report['Size'].append(d_size)
+        
+        # ---- Get the shape
+        (n,lx,ly,lz) = x_train.shape
+
+        # ---- For each model
+        for m_name,m_function in models.items():
+            print("    Run model {}  : ".format(m_name), end='')
+            # ---- get model
+            try:
+                model=m_function(lx,ly,lz)
+                # ---- Compile it
+                model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+                # ---- Callbacks tensorboard
+                log_dir = "./run/logs/tb_{}_{}".format(d_name,m_name)
+                tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
+                # ---- Callbacks bestmodel
+                save_dir = "./run/models/model_{}_{}.h5".format(d_name,m_name)
+                bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)
+                # ---- Train
+                start_time = time.time()
+                history = model.fit(  x_train[:1000], y_train[:1000],
+                                    batch_size      = batch_size,
+                                    epochs          = epochs,
+                                    verbose         = 0,
+                                    validation_data = (x_test, y_test),
+                                    callbacks       = [tensorboard_callback, bestmodel_callback])
+                # ---- Result
+                end_time = time.time()
+                duration = end_time-start_time
+                accuracy = max(history.history["val_accuracy"])*100
+                #
+                report[m_name+' Accuracy'].append(accuracy)
+                report[m_name+' Duration'].append(duration)
+                print("Accuracy={:.2f} and Duration={:.2f})".format(accuracy,duration))
+            except:
+                report[m_name+' Accuracy'].append('-')
+                report[m_name+' Duration'].append('-')
+                print('-')
+    return report
+
+
+# ## 6/ Run
+# ### 6.1/ Clean
+
+# In[6]:
+
+
+get_ipython().run_cell_magic('bash', '', '\n/bin/rm -r ./run/logs   2>/dev/null\n/bin/rm -r ./run/models 2>/dev/null\n/bin/mkdir -p -m 755 ./run/logs\n/bin/mkdir -p -m 755 ./run/models\necho -e "\\nReset directories : ./run/logs and ./run/models ."')
+
+
+# ### 6.2 Start Tensorboard
+
+# In[22]:
+
+
+get_ipython().run_cell_magic('bash', '', 'tensorboard_start --logdir ./run/logs')
+
+
+# ### 6.3/ run and save report
+
+# In[24]:
+
+
+get_ipython().run_cell_magic('time', '', '\nprint(\'\\n---- Run\',\'-\'*50)\n\n# ---- Datasets and models list\n\n# For tests\ndatasets = [\'set-24x24-L\', \'set-24x24-RGB\']\nmodels   = {\'v1\':get_model_v1, \'v3\':get_model_v3}\n\n# The real one\n# datasets = [\'set-24x24-L\', \'set-24x24-RGB\', \'set-48x48-L\', \'set-48x48-RGB\', \'set-24x24-L-LHE\', \'set-24x24-RGB-HE\', \'set-48x48-L-LHE\', \'set-48x48-RGB-HE\']\n# models   = {\'v1\':get_model_v1, \'v2\':get_model_v2, \'v3\':get_model_v3}\n\n# ---- Report name\n\nreport_name=\'./run/report-{}.h5\'.format(time.strftime("%Y-%m-%d_%Hh%Mm%Ss"))\n\n# ---- Run\n\nout    = multi_run(datasets, models, batch_size=64, epochs=2)\n\n# ---- Save report\n\noutput = pd.DataFrame (out)\nparams = pd.DataFrame( {\'datasets\':datasets, \'models\':list(models.keys())} )\n\noutput.to_hdf(report_name, \'output\')\nparams.to_hdf(report_name, \'params\')\n\nprint(\'\\nReport saved as \',report_name)\nprint(\'-\'*59)')
+
+
+# ### 6.4/ Stop Tensorboard
+
+# In[23]:
+
+
+get_ipython().run_cell_magic('bash', '', 'tensorboard_stop')
+
+
+# ## 7/ That's all folks..
+
+# In[21]:
+
+
+print('\n{}'.format(time.strftime("%A %-d %B %Y, %H:%M:%S")))
+print("The work is done.\n")
+
+
+# In[ ]:
+
+
+
+
diff --git a/GTSRB/05.1-Full-convolutions-run.ipynb b/GTSRB/05.1-Full-convolutions-run.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..608973b46a2ba7304f80ef836f4365f01f717965
--- /dev/null
+++ b/GTSRB/05.1-Full-convolutions-run.ipynb
@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "German Traffic Sign Recognition Benchmark (GTSRB)\n",
+    "=================================================\n",
+    "---\n",
+    "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  \n",
+    "\n",
+    "## Episode 5.1 : Full Convolutions / run\n",
+    "\n",
+    "Our main steps:\n",
+    " - Run Full-convolution.ipynb as a batch :\n",
+    "    - Notebook mode\n",
+    "    - Script mode \n",
+    " - Tensorboard follow up\n",
+    "    \n",
+    "## 1/ Run a notebook as a batch\n",
+    "To run a notebook :  \n",
+    "```jupyter nbconvert --to notebook --execute <notebook>```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "# ---- This will execute and save a notebook\n",
+    "#\n",
+    "jupyter nbconvert --to notebook --execute '05-Full-convolutions.ipynb'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2/ Export as a script\n",
+    "To export a notebook as a script :  \n",
+    "```jupyter nbconvert --to script <notebook>```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Converting notebook 05-Full-convolutions.ipynb to script\n",
+      "[NbConvertApp] Writing 8775 bytes to 05-Full-convolutions.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "# ---- This will convert notebook to a notebook.py script\n",
+    "#\n",
+    "jupyter nbconvert --to script '05-Full-convolutions.ipynb'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 8156\n",
+      "-rw-r--r-- 1 pjluc pjluc   16893 Jan 19 15:07 01-Preparation-of-data.ipynb\n",
+      "-rw-r--r-- 1 pjluc pjluc    9181 Jan 19 15:08 02-First-convolutions.ipynb\n",
+      "-rw-r--r-- 1 pjluc pjluc   12879 Jan 19 15:09 03-Tracking-and-visualizing.ipynb\n",
+      "-rw-r--r-- 1 pjluc pjluc   10667 Jan 19 15:10 04-Data-augmentation.ipynb\n",
+      "-rw-r--r-- 1 pjluc pjluc    3325 Jan 19 15:50 05.1-Full-convolutions-run.ipynb\n",
+      "-rw-r--r-- 1 pjluc pjluc   19865 Jan 19 15:17 05.2-Full-convolutions-reports.ipynb\n",
+      "-rw-r--r-- 1 pjluc pjluc   14466 Jan 19 15:11 05-Full-convolutions.ipynb\n",
+      "-rw-r--r-- 1 pjluc pjluc   14533 Jan 19 15:35 05-Full-convolutions.nbconvert.ipynb\n",
+      "-rw-r--r-- 1 pjluc pjluc    8775 Jan 19 15:43 05-Full-convolutions.py\n",
+      "-rw-r--r-- 1 pjluc pjluc   12346 Jan 19 15:47 99 Scripts-Tensorboard.ipynb\n",
+      "drwxr-xr-x 1 pjluc pjluc     512 Jan 10 22:10 data\n",
+      "drwxr-xr-x 1 pjluc pjluc     512 Jan 19 15:07 fidle\n",
+      "-rw-r--r-- 1 pjluc pjluc 7391072 Jan 19 00:41 foo.h5\n",
+      "-rw-r--r-- 1 pjluc pjluc    2816 Jan 11 15:45 README.ipynb\n",
+      "drwxr-xr-x 1 pjluc pjluc     512 Jan 19 15:35 run\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3/ Batch submission\n",
+    "Create batch script :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing ./run/batch_full_convolutions.sh\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile \"./run/batch_full_convolutions.sh\"\n",
+    "\n",
+    "#!/bin/batch\n",
+    "\n",
+    "# ----------------------------------\n",
+    "#   _           _       _\n",
+    "#  | |__   __ _| |_ ___| |__\n",
+    "#  | '_ \\ / _` | __/ __| '_ \\\n",
+    "#  | |_) | (_| | || (__| | | |\n",
+    "#  |_.__/ \\__,_|\\__\\___|_| |_|\n",
+    "#                  Full convolutions\n",
+    "# ----------------------------------\n",
+    "#\n",
+    "CONDA_ENV=\"deeplearning2\"\n",
+    "RUN_DIR=\"~/fidle/GTSRB\"\n",
+    "RUN_SCRIPT=\"05-Full-convolutions.py\"\n",
+    "\n",
+    "# ---- Cuda Conda initialization\n",
+    "#\n",
+    "echo -e 'Init environment with cuda and conda...\\n'\n",
+    "source /applis/environments/cuda_env.sh dahu 10.0\n",
+    "source /applis/environments/conda.sh\n",
+    "#\n",
+    "conda activate \"$CONDA_ENV\"\n",
+    "\n",
+    "# ---- Run it...\n",
+    "#\n",
+    "cd $RUN_DIR\n",
+    "$RUN_SCRIPT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/GTSRB/05.2-Full-convolutions-reports.ipynb b/GTSRB/05.2-Full-convolutions-reports.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c02d06d86acd664c8bebc53dc434be86efa87290
--- /dev/null
+++ b/GTSRB/05.2-Full-convolutions-reports.ipynb
@@ -0,0 +1,368 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "German Traffic Sign Recognition Benchmark (GTSRB)\n",
+    "=================================================\n",
+    "---\n",
+    "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  \n",
+    "\n",
+    "## Episode 5.2 : Full Convolutions Reports\n",
+    "\n",
+    "Ou main steps :\n",
+    " - Show reports\n",
+    "\n",
+    "## 1/ Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os,glob\n",
+    "from pathlib import Path\n",
+    "from IPython.display import display, Markdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2/ A nice function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_report(report_name):\n",
+    "    # ---- Read report\n",
+    "    output = pd.read_hdf(report_name, 'output')\n",
+    "    params = pd.read_hdf(report_name, 'params')\n",
+    "    # ---- Build format\n",
+    "    lambda_acc = lambda x : '{:.2f} %'.format(x) if (isinstance(x, float)) else '{:}'.format(x)\n",
+    "    lambda_dur = lambda x : '{:.1f} s'.format(x) if (isinstance(x, float)) else '{:}'.format(x)\n",
+    "    format_dict = {'Size':'{:.2f} Mo'}\n",
+    "    for m in params['models'].tolist():\n",
+    "        format_dict[m+' Accuracy']=lambda_acc\n",
+    "        format_dict[m+' Duration']=lambda_dur\n",
+    "    t=output.style.format(format_dict).hide_index()\n",
+    "    display(t)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3/ Reports display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Report :  report-2020-01-19_12h45m57s \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Dataset</th>        <th class=\"col_heading level0 col1\" >Size</th>        <th class=\"col_heading level0 col2\" >v1 Accuracy</th>        <th class=\"col_heading level0 col3\" >v1 Duration</th>        <th class=\"col_heading level0 col4\" >v3 Accuracy</th>        <th class=\"col_heading level0 col5\" >v3 Duration</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                                <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row0_col2\" class=\"data row0 col2\" >11.99 %</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row0_col3\" class=\"data row0 col3\" >8.5 s</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row0_col4\" class=\"data row0 col4\" >-</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row0_col5\" class=\"data row0 col5\" >-</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                                <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row1_col2\" class=\"data row1 col2\" >16.95 %</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row1_col3\" class=\"data row1 col3\" >8.4 s</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row1_col4\" class=\"data row1 col4\" >-</td>\n",
+       "                        <td id=\"T_5d373846_3ac6_11ea_820c_836bead0fd53row1_col5\" class=\"data row1 col5\" >-</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb458777110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Report :  report-2020-01-19_13h53m43s \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Dataset</th>        <th class=\"col_heading level0 col1\" >Size</th>        <th class=\"col_heading level0 col2\" >v1 Accuracy</th>        <th class=\"col_heading level0 col3\" >v1 Duration</th>        <th class=\"col_heading level0 col4\" >v3 Accuracy</th>        <th class=\"col_heading level0 col5\" >v3 Duration</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                                <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row0_col2\" class=\"data row0 col2\" >10.48 %</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row0_col3\" class=\"data row0 col3\" >8.4 s</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row0_col4\" class=\"data row0 col4\" >-</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row0_col5\" class=\"data row0 col5\" >-</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                                <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row1_col2\" class=\"data row1 col2\" >15.67 %</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row1_col3\" class=\"data row1 col3\" >8.3 s</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row1_col4\" class=\"data row1 col4\" >-</td>\n",
+       "                        <td id=\"T_5d39a356_3ac6_11ea_820c_836bead0fd53row1_col5\" class=\"data row1 col5\" >-</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb45815d350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Report :  report-2020-01-19_14h26m21s \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Dataset</th>        <th class=\"col_heading level0 col1\" >Size</th>        <th class=\"col_heading level0 col2\" >v1 Accuracy</th>        <th class=\"col_heading level0 col3\" >v1 Duration</th>        <th class=\"col_heading level0 col4\" >v3 Accuracy</th>        <th class=\"col_heading level0 col5\" >v3 Duration</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                                <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row0_col2\" class=\"data row0 col2\" >10.68 %</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row0_col3\" class=\"data row0 col3\" >8.5 s</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row0_col4\" class=\"data row0 col4\" >-</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row0_col5\" class=\"data row0 col5\" >-</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                                <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row1_col2\" class=\"data row1 col2\" >18.24 %</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row1_col3\" class=\"data row1 col3\" >8.4 s</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row1_col4\" class=\"data row1 col4\" >-</td>\n",
+       "                        <td id=\"T_5d3bfef8_3ac6_11ea_820c_836bead0fd53row1_col5\" class=\"data row1 col5\" >-</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb457f9d350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Report :  report-2020-01-19_14h27m02s \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Dataset</th>        <th class=\"col_heading level0 col1\" >Size</th>        <th class=\"col_heading level0 col2\" >v1 Accuracy</th>        <th class=\"col_heading level0 col3\" >v1 Duration</th>        <th class=\"col_heading level0 col4\" >v3 Accuracy</th>        <th class=\"col_heading level0 col5\" >v3 Duration</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                                <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row0_col2\" class=\"data row0 col2\" >7.00 %</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row0_col3\" class=\"data row0 col3\" >9.2 s</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row0_col4\" class=\"data row0 col4\" >-</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row0_col5\" class=\"data row0 col5\" >-</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                                <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row1_col2\" class=\"data row1 col2\" >11.59 %</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row1_col3\" class=\"data row1 col3\" >8.9 s</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row1_col4\" class=\"data row1 col4\" >-</td>\n",
+       "                        <td id=\"T_5d3f7fba_3ac6_11ea_820c_836bead0fd53row1_col5\" class=\"data row1 col5\" >-</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb45849c750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Report :  report-2020-01-19_14h31m50s \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Dataset</th>        <th class=\"col_heading level0 col1\" >Size</th>        <th class=\"col_heading level0 col2\" >v1 Accuracy</th>        <th class=\"col_heading level0 col3\" >v1 Duration</th>        <th class=\"col_heading level0 col4\" >v3 Accuracy</th>        <th class=\"col_heading level0 col5\" >v3 Duration</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                                <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row0_col2\" class=\"data row0 col2\" >10.22 %</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row0_col3\" class=\"data row0 col3\" >8.6 s</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row0_col4\" class=\"data row0 col4\" >-</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row0_col5\" class=\"data row0 col5\" >-</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                                <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row1_col2\" class=\"data row1 col2\" >12.98 %</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row1_col3\" class=\"data row1 col3\" >8.2 s</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row1_col4\" class=\"data row1 col4\" >-</td>\n",
+       "                        <td id=\"T_5d42c210_3ac6_11ea_820c_836bead0fd53row1_col5\" class=\"data row1 col5\" >-</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb45850fed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Report :  report-2020-01-19_14h56m27s \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Dataset</th>        <th class=\"col_heading level0 col1\" >Size</th>        <th class=\"col_heading level0 col2\" >v1 Accuracy</th>        <th class=\"col_heading level0 col3\" >v1 Duration</th>        <th class=\"col_heading level0 col4\" >v3 Accuracy</th>        <th class=\"col_heading level0 col5\" >v3 Duration</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                                <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row0_col0\" class=\"data row0 col0\" >set-24x24-L</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row0_col1\" class=\"data row0 col1\" >228.77 Mo</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row0_col2\" class=\"data row0 col2\" >-</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row0_col3\" class=\"data row0 col3\" >-</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row0_col4\" class=\"data row0 col4\" >-</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row0_col5\" class=\"data row0 col5\" >-</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                                <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row1_col0\" class=\"data row1 col0\" >set-24x24-RGB</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row1_col1\" class=\"data row1 col1\" >684.39 Mo</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row1_col2\" class=\"data row1 col2\" >-</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row1_col3\" class=\"data row1 col3\" >-</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row1_col4\" class=\"data row1 col4\" >-</td>\n",
+       "                        <td id=\"T_5d461c80_3ac6_11ea_820c_836bead0fd53row1_col5\" class=\"data row1 col5\" >-</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb4587551d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for file in glob.glob(\"./run/*.h5\"):\n",
+    "    print(\"\\n\\nReport : \",Path(file).stem,'\\n') \n",
+    "    show_report(file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "\n",
+    "### Some old results :  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "|         Datasets         |       Size      |     Model : v1     |    Model : v2      |     Model : v3     |\n",
+    "|:------------------------:|:---------------:|:------------------:|:------------------:|:------------------:|\n",
+    "| set-24x24-L              |          229 Mo |   95.91%  75.04s   |   96.86% 102.28s   |   -      -         |\n",
+    "| set-24x24-RGB            |          684 Mo |   96.60%  77.24s   |   97.32% 103.93s   |   -      -         |\n",
+    "| set-48x48-L              |          914 Mo | **96.71%** 123.94s |   97.68% 149.57s   |  97.60%  91.53s    |\n",
+    "| set-48x48-RGB            |         2736 Mo |   96.36% 117.74s   | **98.20%** 142.63s |  97.28%  91.29s    |\n",
+    "| set-24x24-L-LHE          |          229 Mo |   95.95%  66.12s   |   96.75%  89.45s   |   -      -         |\n",
+    "| set-24x24-RGB-HE         |          684 Mo |   95.30%  68.89s   |   96.28%  92.15s   |   -      -         |\n",
+    "| set-48x48-L-LHE          |          914 Mo |   96.69% 109.28s   |   97.94% 135.17s   | **97.97%** 83.80s  |\n",
+    "| set-48x48-RGB-HE         |         2736 Mo |   95.29% 117.70s   | **98.13%** 141.56s |  97.00%  89.38s    |"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/GTSRB/99 Scripts-Tensorboard.ipynb b/GTSRB/99 Scripts-Tensorboard.ipynb
index 5dd94028b6cf27d02f8c4650da94d3ba6bb5af61..290e20a92c015034e43150daf1f8f0a16b960de3 100644
--- a/GTSRB/99 Scripts-Tensorboard.ipynb	
+++ b/GTSRB/99 Scripts-Tensorboard.ipynb	
@@ -21,17 +21,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensorbord started - pid is  211387\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%bash\n",
     "tensorboard_start --logdir ./run/logs"
@@ -39,17 +31,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensorboard status - pid is  214798\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%bash\n",
     "tensorboard_status"
@@ -57,17 +41,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensorboard process not found...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%bash\n",
     "tensorboard_stop"
@@ -82,21 +58,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting /home/paroutyj/bin/tensorboard_start\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%writefile \"~/bin/tensorboard_start\"\n",
     "\n",
@@ -114,7 +78,8 @@
     "# -----------------------------------------------------------\n",
     "# Jean-Luc Parouty CNRS/SIMaP Janvier 2020 - version 1.02\n",
     "\n",
-    "VERSION='1.02'\n",
+    "VERSION='1.2'\n",
+    "CONDA_ENV='GPU'\n",
     "\n",
     "# ---- Usage\n",
     "#\n",
@@ -124,7 +89,17 @@
     "        echo -e \"Exemple :  $(basename $0) --logdir ./run/logs\\n\"\n",
     "        exit\n",
     "fi\n",
-    "\n",
+    "                        \n",
+    "# ---- Conda activate\n",
+    "#\n",
+    "source /applis/environments/conda.sh\n",
+    "conda activate \"$CONDA_ENV\" >/dev/null 2>&1\n",
+    "status=$?\n",
+    "if [ $status -ne 0 ]; then\n",
+    "        echo -e \"Oups ! Conda environment is not available... contact your support ;-(\\n\"\n",
+    "        exit 1\n",
+    "fi\n",
+    "                        \n",
     "# ---- Port number\n",
     "#\n",
     "PORT_JPY=\"$(/usr/bin/id -u)\"\n",
@@ -152,21 +127,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing /home/paroutyj/bin/tensorboard_status\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%writefile \"~/bin/tensorboard_status\"\n",
     "\n",
@@ -212,21 +175,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting /home/paroutyj/bin/tensorboard_stop\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%%writefile \"~/bin/tensorboard_stop\"\n",
     "\n",
@@ -280,19 +231,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-rwxr-xr-x 1 paroutyj l-simap 1560 Jan 14 15:41 /home/paroutyj/bin/tensorboard_start\n",
-      "-rwxr-xr-x 1 paroutyj l-simap 1227 Jan 14 15:52 /home/paroutyj/bin/tensorboard_status\n",
-      "-rwxr-xr-x 1 paroutyj l-simap 1239 Jan 14 15:40 /home/paroutyj/bin/tensorboard_stop\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%bash\n",
     "/bin/chmod 755 ~/bin/tensorboard_*  2>/dev/null\n",
@@ -316,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,38 +266,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "      <iframe id=\"tensorboard-frame-f9668cb06b1e0723\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
-       "      </iframe>\n",
-       "      <script>\n",
-       "        (function() {\n",
-       "          const frame = document.getElementById(\"tensorboard-frame-f9668cb06b1e0723\");\n",
-       "          const url = new URL(\"/\", window.location);\n",
-       "          url.port = 21277;\n",
-       "          frame.src = url;\n",
-       "        })();\n",
-       "      </script>\n",
-       "  "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%tensorboard --port 21277 --host 0.0.0.0 --logdir ./run/logs"
    ]
@@ -375,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,33 +296,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "      <iframe id=\"tensorboard-frame-4b7ace605984009d\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
-       "      </iframe>\n",
-       "      <script>\n",
-       "        (function() {\n",
-       "          const frame = document.getElementById(\"tensorboard-frame-4b7ace605984009d\");\n",
-       "          const url = new URL(\"/\", window.location);\n",
-       "          url.port = 21277;\n",
-       "          frame.src = url;\n",
-       "        })();\n",
-       "      </script>\n",
-       "  "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tsb.start('--port 21277 --host 0.0.0.0 --logdir ./run/logs')"
    ]
@@ -424,17 +312,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No known TensorBoard instances running.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "a=tsb.list()"
    ]
@@ -449,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/GTSRB/idle/__init__.py b/GTSRB/fidle/__init__.py
similarity index 100%
rename from GTSRB/idle/__init__.py
rename to GTSRB/fidle/__init__.py
diff --git a/GTSRB/idle/pwk.py b/GTSRB/fidle/pwk.py
similarity index 96%
rename from GTSRB/idle/pwk.py
rename to GTSRB/fidle/pwk.py
index 5e4dad91f0806925498d023e0deb6afa74470fd6..6375b8f04c15212e3e840ccc024019513c145025 100644
--- a/GTSRB/idle/pwk.py
+++ b/GTSRB/fidle/pwk.py
@@ -8,13 +8,13 @@
 #                                                        module pwk                                   
 # ==================================================================
 # A simple module to host some common functions for practical work
-# pjluc 2019
+# pjluc 2020
 
 import os
 import glob
 from datetime import datetime
 import itertools
-import datetime
+import datetime, time
 
 import math
 import numpy as np
@@ -26,22 +26,22 @@ import matplotlib
 import matplotlib.pyplot as plt
 import seaborn as sn
 
-VERSION='0.1.4'
+VERSION='0.1.5'
 
 
 # -------------------------------------------------------------
 # init_all
 # -------------------------------------------------------------
 #
-def init(mplstyle='idle/talk.mplstyle'):
+def init(mplstyle='fidle/talk.mplstyle'):
     global VERSION
     # ---- matplotlib
     matplotlib.style.use(mplstyle)
     # ---- Hello world
-    now = datetime.datetime.now()
+#     now = datetime.datetime.now()
     print('IDLE 2020 - Practical Work Module')
     print('  Version            :', VERSION)
-    print('  Run time           : {}'.format(now.strftime("%A %-d %B %Y, %H:%M:%S")))
+    print('  Run time           : {}'.format(time.strftime("%A %-d %B %Y, %H:%M:%S")))
     print('  Matplotlib style   :', mplstyle)
     print('  TensorFlow version :',tf.__version__)
     print('  Keras version      :',tf.keras.__version__)
diff --git a/GTSRB/idle/talk.mplstyle b/GTSRB/fidle/talk.mplstyle
similarity index 100%
rename from GTSRB/idle/talk.mplstyle
rename to GTSRB/fidle/talk.mplstyle