diff --git a/BE2_GAN_and_cGAN.ipynb b/BE2_GAN_and_cGAN.ipynb
index 7243c8909272f2e776695baf70e3aea2aa9b3aef..8d8e66543f204de4f012ed28d71188d75a917e29 100644
--- a/BE2_GAN_and_cGAN.ipynb
+++ b/BE2_GAN_and_cGAN.ipynb
@@ -69,15 +69,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "colab": {},
     "colab_type": "code",
     "id": "sIL7UvYAZx6L"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'torch'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_27176\\1645866687.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparallel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
+     ]
+    }
+   ],
    "source": [
-    "#TO DO: your code here to adapt the code from the tutorial to experiment on MNIST dataset"
+    "#TO DO: your code here to adapt the code from the tutorial to experiment on MNIST dataset\n",
+    "\n",
+    "from __future__ import print_function\n",
+    "#%matplotlib inline\n",
+    "import argparse\n",
+    "import os\n",
+    "import random\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.parallel\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "import torch.optim as optim\n",
+    "import torch.utils.data\n",
+    "import torchvision.datasets as dset\n",
+    "import torchvision.transforms as transforms\n",
+    "import torchvision.utils as vutils\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.animation as animation\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "manualSeed = 999\n",
+    "#manualSeed = random.randint(1, 10000) # use if you want new results\n",
+    "print(\"Random Seed: \", manualSeed)\n",
+    "random.seed(manualSeed)\n",
+    "torch.manual_seed(manualSeed)\n",
+    "\n",
+    "# Create the generator\n",
+    "netG = Generator(ngpu).to(device)\n",
+    "\n",
+    "# Handle multi-gpu if desired\n",
+    "if (device.type == 'cuda') and (ngpu > 1):\n",
+    "    netG = nn.DataParallel(netG, list(range(ngpu)))\n",
+    "\n",
+    "# Apply the weights_init function to randomly initialize all weights\n",
+    "#  to mean=0, stdev=0.02.\n",
+    "netG.apply(weights_init)\n",
+    "\n",
+    "# Print the model\n",
+    "print(netG)"
    ]
   },
   {
@@ -1225,7 +1277,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,
diff --git a/README.md b/README.md
index 57884980d12716147ef767baaee44c3afe0e851a..44b0f1bd63230afdec9977e131472fc701c8e436 100644
--- a/README.md
+++ b/README.md
@@ -4,4 +4,10 @@ We recommand to use the notebook (.ipynb) but the Python script (.py) is also pr
 
 # How to submit your Work ?
 
-This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered.
\ No newline at end of file
+This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered.
+
+
+# DCGAN
+
+To reduce the time of execution we use Google Colab GPU;
+The MNIST dataset uses black and white images so the first step is to change the number of channels for 1 (in the tutorial was 3 because of RGB data used);