{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"MNIST_GAN.ipynb","provenance":[],"collapsed_sections":[]},"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":"3.7.8"},"widgets":{"application/vnd.jupyter.widget-state+json":{"e48ab3ae02944c058a06c1d93f266f08":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","state":{"_view_name":"HBoxView","_dom_classes":[],"_model_name":"HBoxModel","_view_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_view_count":null,"_view_module_version":"1.5.0","box_style":"","layout":"IPY_MODEL_a8997c287a7f4cc6968c1cc170378f39","_model_module":"@jupyter-widgets/controls","children":["IPY_MODEL_b1f2942b42454c1da16c62b1656facd7","IPY_MODEL_b1898547f05b47d686c9ee8e86377ad6","IPY_MODEL_ab20fe953431488a9b1954b3f7646b72"]}},"a8997c287a7f4cc6968c1cc170378f39":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","state":{"_view_name":"LayoutView","grid_template_rows":null,"right":null,"justify_content":null,"_view_module":"@jupyter-widgets/base","overflow":null,"_model_module_version":"1.2.0","_view_count":null,"flex_flow":null,"width":null,"min_width":null,"border":null,"align_items":null,"bottom":null,"_model_module":"@jupyter-widgets/base","top":null,"grid_column":null,"overflow_y":null,"overflow_x":null,"grid_auto_flow":null,"grid_area":null,"grid_template_columns":null,"flex":null,"_model_name":"LayoutModel","justify_items":null,"grid_row":null,"max_height":null,"align_content":null,"visibility":null,"align_self":null,"height":null,"min_height":null,"padding":null,"grid_auto_rows":null,"grid_gap":null,"max_width":null,"order":null,"_view_module_version":"1.2.0","grid_template_areas":null,"object_position":null,"object_fit":null,"grid_auto_columns":null,"margin":null,"display":null,"left":null}},"b1f2942b42454c1da16c62b1656facd7":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","state":{"_view_name":"HTMLView","style":"IPY_MODEL_c009a45381a84807b506789bcee17a2e","_dom_classes":[],"description":"","_model_name":"HTMLModel","placeholder":"​","_view_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","value":"Dl Completed...: 100%","_view_count":null,"_view_module_version":"1.5.0","description_tooltip":null,"_model_module":"@jupyter-widgets/controls","layout":"IPY_MODEL_b1044b44f65148e7b542664102fd2596"}},"b1898547f05b47d686c9ee8e86377ad6":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","state":{"_view_name":"ProgressView","style":"IPY_MODEL_f68c31c7fa2949ebb1ca0c5d70a69a01","_dom_classes":[],"description":"","_model_name":"FloatProgressModel","bar_style":"success","max":4,"_view_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","value":4,"_view_count":null,"_view_module_version":"1.5.0","orientation":"horizontal","min":0,"description_tooltip":null,"_model_module":"@jupyter-widgets/controls","layout":"IPY_MODEL_00e4ac00ab574ba4ae913c016ea4b15d"}},"ab20fe953431488a9b1954b3f7646b72":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","state":{"_view_name":"HTMLView","style":"IPY_MODEL_800d380dee7a421aba34dec7b746e269","_dom_classes":[],"description":"","_model_name":"HTMLModel","placeholder":"​","_view_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","value":" 4/4 [00:08&lt;00:00,  2.07s/ file]","_view_count":null,"_view_module_version":"1.5.0","description_tooltip":null,"_model_module":"@jupyter-widgets/controls","layout":"IPY_MODEL_11abf069223f4337b30eb6a139ae613a"}},"c009a45381a84807b506789bcee17a2e":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","state":{"_view_name":"StyleView","_model_name":"DescriptionStyleModel","description_width":"","_view_module":"@jupyter-widgets/base","_model_module_version":"1.5.0","_view_count":null,"_view_module_version":"1.2.0","_model_module":"@jupyter-widgets/controls"}},"b1044b44f65148e7b542664102fd2596":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","state":{"_view_name":"LayoutView","grid_template_rows":null,"right":null,"justify_content":null,"_view_module":"@jupyter-widgets/base","overflow":null,"_model_module_version":"1.2.0","_view_count":null,"flex_flow":null,"width":null,"min_width":null,"border":null,"align_items":null,"bottom":null,"_model_module":"@jupyter-widgets/base","top":null,"grid_column":null,"overflow_y":null,"overflow_x":null,"grid_auto_flow":null,"grid_area":null,"grid_template_columns":null,"flex":null,"_model_name":"LayoutModel","justify_items":null,"grid_row":null,"max_height":null,"align_content":null,"visibility":null,"align_self":null,"height":null,"min_height":null,"padding":null,"grid_auto_rows":null,"grid_gap":null,"max_width":null,"order":null,"_view_module_version":"1.2.0","grid_template_areas":null,"object_position":null,"object_fit":null,"grid_auto_columns":null,"margin":null,"display":null,"left":null}},"f68c31c7fa2949ebb1ca0c5d70a69a01":{"model_module":"@jupyter-widgets/controls","model_name":"ProgressStyleModel","state":{"_view_name":"StyleView","_model_name":"ProgressStyleModel","description_width":"","_view_module":"@jupyter-widgets/base","_model_module_version":"1.5.0","_view_count":null,"_view_module_version":"1.2.0","bar_color":null,"_model_module":"@jupyter-widgets/controls"}},"00e4ac00ab574ba4ae913c016ea4b15d":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","state":{"_view_name":"LayoutView","grid_template_rows":null,"right":null,"justify_content":null,"_view_module":"@jupyter-widgets/base","overflow":null,"_model_module_version":"1.2.0","_view_count":null,"flex_flow":null,"width":null,"min_width":null,"border":null,"align_items":null,"bottom":null,"_model_module":"@jupyter-widgets/base","top":null,"grid_column":null,"overflow_y":null,"overflow_x":null,"grid_auto_flow":null,"grid_area":null,"grid_template_columns":null,"flex":null,"_model_name":"LayoutModel","justify_items":null,"grid_row":null,"max_height":null,"align_content":null,"visibility":null,"align_self":null,"height":null,"min_height":null,"padding":null,"grid_auto_rows":null,"grid_gap":null,"max_width":null,"order":null,"_view_module_version":"1.2.0","grid_template_areas":null,"object_position":null,"object_fit":null,"grid_auto_columns":null,"margin":null,"display":null,"left":null}},"800d380dee7a421aba34dec7b746e269":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","state":{"_view_name":"StyleView","_model_name":"DescriptionStyleModel","description_width":"","_view_module":"@jupyter-widgets/base","_model_module_version":"1.5.0","_view_count":null,"_view_module_version":"1.2.0","_model_module":"@jupyter-widgets/controls"}},"11abf069223f4337b30eb6a139ae613a":{"model_module":"@jupyter-widgets/base","model_name":"LayoutModel","state":{"_view_name":"LayoutView","grid_template_rows":null,"right":null,"justify_content":null,"_view_module":"@jupyter-widgets/base","overflow":null,"_model_module_version":"1.2.0","_view_count":null,"flex_flow":null,"width":null,"min_width":null,"border":null,"align_items":null,"bottom":null,"_model_module":"@jupyter-widgets/base","top":null,"grid_column":null,"overflow_y":null,"overflow_x":null,"grid_auto_flow":null,"grid_area":null,"grid_template_columns":null,"flex":null,"_model_name":"LayoutModel","justify_items":null,"grid_row":null,"max_height":null,"align_content":null,"visibility":null,"align_self":null,"height":null,"min_height":null,"padding":null,"grid_auto_rows":null,"grid_gap":null,"max_width":null,"order":null,"_view_module_version":"1.2.0","grid_template_areas":null,"object_position":null,"object_fit":null,"grid_auto_columns":null,"margin":null,"display":null,"left":null}}}}},"cells":[{"cell_type":"markdown","metadata":{"id":"5dXrOnljVrEV"},"source":["# Standard GAN on MNIST\n","\n","Based on the original Generative Adversarial Network (GAN), as introduced by Goodfellow et al. in 2014 [1]\n","\n","## Learning goals\n","\n","- Learn about the GAN deep neural network\n","- Design a clean implementation using Keras high level models (Sequential)\n","- Use the new Tensorflow Dataset input data pipeline"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nVgdy_Xe5pQ9","executionInfo":{"status":"ok","timestamp":1608031236459,"user_tz":-60,"elapsed":1292,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}},"outputId":"1eb70d9d-1617-4c99-baa3-838658a017be"},"source":["COLAB = True\n","if COLAB:\n","  from google.colab import drive\n","  drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nz-LijqlGlcx","executionInfo":{"status":"ok","timestamp":1608031239664,"user_tz":-60,"elapsed":4490,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}},"outputId":"d3968520-b555-4f6f-e891-2cd201876989"},"source":["!pip install tensorview"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: tensorview in /usr/local/lib/python3.6/dist-packages (0.4.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from tensorview) (3.2.2)\n","Requirement already satisfied: pyecharts-snapshot>=0.1.10tensorflow>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorview) (0.2.0)\n","Requirement already satisfied: pandas>=0.24.1 in /usr/local/lib/python3.6/dist-packages (from tensorview) (1.1.5)\n","Requirement already satisfied: pyecharts>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorview) (1.9.0)\n","Requirement already satisfied: linora>=0.9.3 in /usr/local/lib/python3.6/dist-packages (from tensorview) (0.9.3)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->tensorview) (2.4.7)\n","Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib->tensorview) (1.18.5)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->tensorview) (0.10.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->tensorview) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->tensorview) (1.3.1)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from pyecharts-snapshot>=0.1.10tensorflow>=2.0.0->tensorview) (7.0.0)\n","Requirement already satisfied: pyppeteer>=0.0.25 in /usr/local/lib/python3.6/dist-packages (from pyecharts-snapshot>=0.1.10tensorflow>=2.0.0->tensorview) (0.2.2)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.1->tensorview) (2018.9)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.6/dist-packages (from pyecharts>=1.2.0->tensorview) (2.11.2)\n","Requirement already satisfied: prettytable in /usr/local/lib/python3.6/dist-packages (from pyecharts>=1.2.0->tensorview) (2.0.0)\n","Requirement already satisfied: simplejson in /usr/local/lib/python3.6/dist-packages (from pyecharts>=1.2.0->tensorview) (3.17.2)\n","Requirement already satisfied: xgboost>=0.81 in /usr/local/lib/python3.6/dist-packages (from linora>=0.9.3->tensorview) (0.90)\n","Requirement already satisfied: tensorflow>=2.0.0rc0 in /usr/local/lib/python3.6/dist-packages (from linora>=0.9.3->tensorview) (2.3.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->tensorview) (1.15.0)\n","Requirement already satisfied: urllib3<2.0.0,>=1.25.8 in /usr/local/lib/python3.6/dist-packages (from pyppeteer>=0.0.25->pyecharts-snapshot>=0.1.10tensorflow>=2.0.0->tensorview) (1.26.2)\n","Requirement already satisfied: appdirs<2.0.0,>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from pyppeteer>=0.0.25->pyecharts-snapshot>=0.1.10tensorflow>=2.0.0->tensorview) (1.4.4)\n","Requirement already satisfied: tqdm<5.0.0,>=4.42.1 in /usr/local/lib/python3.6/dist-packages (from pyppeteer>=0.0.25->pyecharts-snapshot>=0.1.10tensorflow>=2.0.0->tensorview) (4.54.1)\n","Requirement already satisfied: websockets<9.0,>=8.1 in /usr/local/lib/python3.6/dist-packages (from pyppeteer>=0.0.25->pyecharts-snapshot>=0.1.10tensorflow>=2.0.0->tensorview) (8.1)\n","Requirement already satisfied: pyee<8.0.0,>=7.0.1 in /usr/local/lib/python3.6/dist-packages (from pyppeteer>=0.0.25->pyecharts-snapshot>=0.1.10tensorflow>=2.0.0->tensorview) (7.0.4)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->pyecharts>=1.2.0->tensorview) (1.1.1)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from prettytable->pyecharts>=1.2.0->tensorview) (50.3.2)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prettytable->pyecharts>=1.2.0->tensorview) (0.2.5)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost>=0.81->linora>=0.9.3->tensorview) (1.4.1)\n","Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (3.3.0)\n","Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.1.0)\n","Requirement already satisfied: tensorflow-estimator<2.4.0,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (2.3.0)\n","Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.12.1)\n","Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (0.10.0)\n","Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (2.10.0)\n","Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.1.2)\n","Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (0.36.1)\n","Requirement already satisfied: tensorboard<3,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (2.3.0)\n","Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (3.12.4)\n","Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.34.0)\n","Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.6.3)\n","Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (0.2.0)\n","Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (0.3.3)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (2.23.0)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.17.2)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.0.1)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (0.4.2)\n","Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.7.0)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (3.3.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (2020.12.5)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (2.10)\n","Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (4.1.1)\n","Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (0.2.8)\n","Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (4.6)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (1.3.0)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (3.1.1)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (0.4.8)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (3.1.0)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow>=2.0.0rc0->linora>=0.9.3->tensorview) (3.4.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uG7wVA9_Gjie","executionInfo":{"status":"ok","timestamp":1608031244459,"user_tz":-60,"elapsed":9283,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}},"outputId":"4edcd720-3bd7-4887-cad1-1f8dc78191b6"},"source":["import sys\n","import tensorflow as tf\n","import plotly.graph_objects as go\n","from ipywidgets import widgets\n","import numpy as np\n","from tensorflow.keras import models, layers, losses, optimizers, metrics\n","import tensorflow_datasets as tf_ds\n","import tensorview as tv\n","import matplotlib.pyplot as plt\n","from pathlib import Path"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.2) or chardet (3.0.4) doesn't match a supported version!\n","  RequestsDependencyWarning)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"bey-00iC7nZ5","executionInfo":{"status":"ok","timestamp":1608031244460,"user_tz":-60,"elapsed":9281,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["if COLAB:\n","  model_path = Path('/content/drive/My Drive/Colab Notebooks/DsStepByStep')\n","else:\n","  model_path = Path('model')"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"8UEycXEfGjii","executionInfo":{"status":"ok","timestamp":1608031244460,"user_tz":-60,"elapsed":9279,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["batch_size = 100\n","latent_dim = 100\n","image_width, image_height, image_channels = 32, 32, 1\n","mnist_dim = image_width * image_height * image_channels"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"_IaQ3HHrGjil","executionInfo":{"status":"ok","timestamp":1608031244461,"user_tz":-60,"elapsed":9278,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["disc_learning_rate = 0.0002\n","gen_learning_rate  = 0.0002\n","\n","relu_alpha = 0.01"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"oWTst73xGjio"},"source":["# Data\n","\n","MNIST dataset is optimized to be stored efficiently: images are closely cropped at 28x28 pixels and stored as 1 byte per pixel (uint8 format). However, to get proper performance we need to modify the input data to insert some padding around and convert the pixel format to float on 32 bits."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":236,"referenced_widgets":["e48ab3ae02944c058a06c1d93f266f08","a8997c287a7f4cc6968c1cc170378f39","b1f2942b42454c1da16c62b1656facd7","b1898547f05b47d686c9ee8e86377ad6","ab20fe953431488a9b1954b3f7646b72","c009a45381a84807b506789bcee17a2e","b1044b44f65148e7b542664102fd2596","f68c31c7fa2949ebb1ca0c5d70a69a01","00e4ac00ab574ba4ae913c016ea4b15d","800d380dee7a421aba34dec7b746e269","11abf069223f4337b30eb6a139ae613a"]},"id":"u8RjDXCeGjio","executionInfo":{"status":"ok","timestamp":1608031255506,"user_tz":-60,"elapsed":20318,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}},"outputId":"9a8f8129-67b9-4b6f-b1a2-bc1c26fcb9fb"},"source":["def normalize_img(image, label):\n","  \"\"\"Normalizes images: `uint8` -> `float32` and pad to 32 x 32\"\"\"\n","  image_float = tf.cast(image, tf.float32) / 128. - 1.\n","  image_padded = tf.pad(image_float, [[0, 0], [2, 2], [2, 2], [0, 0]])\n","  return image_padded, label\n","\n","(ds_train, ds_test) = tf_ds.load('mnist', split=['train', 'test'], batch_size=batch_size, as_supervised=True)\n","ds_train = ds_train.map(normalize_img)\n","ds_train = ds_train.cache()\n","\n","ds_test = ds_test.map(normalize_img)\n","ds_test = ds_test.cache()\n","\n","ds_train, ds_test"],"execution_count":7,"outputs":[{"output_type":"stream","text":["\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"],"name":"stdout"},{"output_type":"stream","text":["WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n","local data directory. If you'd instead prefer to read directly from our public\n","GCS bucket (recommended if you're running on GCP), you can instead pass\n","`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n","\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e48ab3ae02944c058a06c1d93f266f08","version_minor":0,"version_major":2},"text/plain":["HBox(children=(HTML(value='Dl Completed...'), FloatProgress(value=0.0, max=4.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n","\n","\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["(<CacheDataset shapes: ((None, 32, 32, 1), (None,)), types: (tf.float32, tf.int64)>,\n"," <CacheDataset shapes: ((None, 32, 32, 1), (None,)), types: (tf.float32, tf.int64)>)"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"1JlWSw_MGjis"},"source":["# Models\n","\n","GAN model is built out of a generator and a discriminator:\n","- The generator gets as input some random noise on space of 100 dimensions, and issues an image (32x32 pixel raster)\n","- The discriminator is trained to distinguish generated images by the generator (i.e. _fakes_), and reference images from the MNIST\n","\n","The generator and discriminator architecture are more or less symmetrical. The generator is increasing the output space dimension step by step using wider and wider layers. The discriminator is similar to other classification networks reducing the input space dimensions down to the binary classification layer.\n","\n","The \"game\" is to jointly train the generator and discriminator in order to have the best generator but still being able to detect generated images."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nAx1wHnZGjis","executionInfo":{"status":"ok","timestamp":1608031256246,"user_tz":-60,"elapsed":21053,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}},"outputId":"6d1eef3c-4fe5-493a-b10f-f686101fa3b4"},"source":["generator = models.Sequential([\n","    layers.Dense(256, input_dim=latent_dim),\n","    layers.LeakyReLU(relu_alpha),\n","    layers.Dropout(0.3),\n","    layers.Dense(512),\n","    layers.LeakyReLU(relu_alpha),\n","    layers.Dense(1024),\n","    layers.LeakyReLU(relu_alpha),\n","    layers.Dense(mnist_dim, activation='tanh'),\n","    layers.Reshape([32, 32, 1]),\n","], name='generator')\n","\n","generator.compile()\n","generator.summary()"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Model: \"generator\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","dense (Dense)                (None, 256)               25856     \n","_________________________________________________________________\n","leaky_re_lu (LeakyReLU)      (None, 256)               0         \n","_________________________________________________________________\n","dropout (Dropout)            (None, 256)               0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 512)               131584    \n","_________________________________________________________________\n","leaky_re_lu_1 (LeakyReLU)    (None, 512)               0         \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1024)              525312    \n","_________________________________________________________________\n","leaky_re_lu_2 (LeakyReLU)    (None, 1024)              0         \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 1024)              1049600   \n","_________________________________________________________________\n","reshape (Reshape)            (None, 32, 32, 1)         0         \n","=================================================================\n","Total params: 1,732,352\n","Trainable params: 1,732,352\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QDm7igtsGjiv","executionInfo":{"status":"ok","timestamp":1608031256247,"user_tz":-60,"elapsed":21049,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}},"outputId":"b9263b00-9c6e-49be-ebcd-df467efb060c"},"source":["discriminator = models.Sequential([\n","    layers.Input(shape=[32, 32, 1]),\n","    layers.Flatten(),\n","    layers.Dropout(0.3),\n","    layers.Dense(1024),\n","    layers.LeakyReLU(relu_alpha),\n","    layers.Dense(512),\n","    layers.LeakyReLU(),\n","    layers.Dense(256),\n","    layers.LeakyReLU(relu_alpha),\n","    layers.Dense(1) # activation='sigmoid'\n","], name='discriminator')\n","\n","discriminator.compile()\n","discriminator.summary()"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Model: \"discriminator\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","flatten (Flatten)            (None, 1024)              0         \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 1024)              0         \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 1024)              1049600   \n","_________________________________________________________________\n","leaky_re_lu_3 (LeakyReLU)    (None, 1024)              0         \n","_________________________________________________________________\n","dense_5 (Dense)              (None, 512)               524800    \n","_________________________________________________________________\n","leaky_re_lu_4 (LeakyReLU)    (None, 512)               0         \n","_________________________________________________________________\n","dense_6 (Dense)              (None, 256)               131328    \n","_________________________________________________________________\n","leaky_re_lu_5 (LeakyReLU)    (None, 256)               0         \n","_________________________________________________________________\n","dense_7 (Dense)              (None, 1)                 257       \n","=================================================================\n","Total params: 1,705,985\n","Trainable params: 1,705,985\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"romdxpbw-j5s"},"source":["# Training\n","\n","Training is alternatively on the distriminator and generator.\n","\n","The discriminator is trained on a batch made of half genuine images and half trained images.\n","\n","The generator is trained with its output fed into the discriminator (whose wheights are frozen in this phase).\n","\n","GAN reputation as difficult to be trained is well deserved and originates in the joint optimization which is similar to a minimax problem (min discrination error, max fidelity of the fakes). As seen below, the noise on the losses and accuracies is high. The main facilitators helping this training are:\n","- Use of leaky ReLU activations to avoid gradient vanishing\n","- Small learning rate to decrease the noise and instability\n"]},{"cell_type":"code","metadata":{"id":"SeCGTzu-2NQM","executionInfo":{"status":"ok","timestamp":1608031256248,"user_tz":-60,"elapsed":21048,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["epochs = 60\n","batch_per_epoch = 60000/batch_size\n","loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"4yo31GrO2NQM","executionInfo":{"status":"ok","timestamp":1608031256248,"user_tz":-60,"elapsed":21046,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["def generator_loss(disc_generated_output):\n","    return loss_object(tf.ones_like(disc_generated_output), disc_generated_output)"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"BvQrAKte2NQM","executionInfo":{"status":"ok","timestamp":1608031256249,"user_tz":-60,"elapsed":21045,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["def discriminator_loss(disc_real_output, disc_generated_output):\n","\n","    real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)\n","    generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)\n","\n","    return real_loss + generated_loss"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"u-1sK7842NQN","executionInfo":{"status":"ok","timestamp":1608031256250,"user_tz":-60,"elapsed":21044,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["@tf.function\n","def train_step(generator, discriminator, \n","               generator_optimizer, discriminator_optimizer, \n","               generator_latent, batch, \n","               epoch):\n","    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n","        \n","        gen_latent = generator_latent()\n","        \n","        gen_output = generator(gen_latent, training=True)\n","\n","        disc_real_output = discriminator(batch, training=True)\n","        disc_generated_output = discriminator(gen_output, training=True)\n","\n","        gen_loss = generator_loss(disc_generated_output)\n","        disc_loss = discriminator_loss(disc_real_output, disc_generated_output)\n","\n","        generator_gradients = gen_tape.gradient(gen_loss, generator.trainable_variables)\n","        discriminator_gradients = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n","\n","    generator_optimizer.apply_gradients(zip(generator_gradients, generator.trainable_variables))\n","    discriminator_optimizer.apply_gradients(zip(discriminator_gradients, discriminator.trainable_variables))\n","\n","    return gen_loss, disc_loss"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"shisEexV2NQN","executionInfo":{"status":"ok","timestamp":1608031256250,"user_tz":-60,"elapsed":21042,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["generator_optimizer = tf.keras.optimizers.Adam(gen_learning_rate, beta_1=0.05)\n","discriminator_optimizer = tf.keras.optimizers.Adam(disc_learning_rate, beta_1=0.05)"],"execution_count":14,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":326},"id":"8O3d25nR2NQN","executionInfo":{"status":"ok","timestamp":1608031645999,"user_tz":-60,"elapsed":410786,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}},"outputId":"947ab3f9-674c-4268-fdce-1d618d256ee7"},"source":["tv_plot = tv.train.PlotMetrics(wait_num=200, columns=2, iter_num=epochs * batch_per_epoch)\n","\n","def generator_latent():\n","    return tf.random.normal((batch_size, latent_dim), 0, 1)\n","\n","for epoch in range(epochs):\n","\n","    for train_batch in iter(ds_train):\n","        \n","        g_loss, d_loss = train_step(generator, discriminator, \n","                                    generator_optimizer, discriminator_optimizer, \n","                                    generator_latent, train_batch[0], \n","                                    epoch)\n","        # Plot\n","        tv_plot.update({ 'discriminator_loss': d_loss,# 'discriminator_acc': d_acc,\n","                        'generator_loss': g_loss, # 'generator_acc': g_acc\n","                       })\n","        tv_plot.draw()"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x576 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":729},"id":"eP6QbtOIGji6","executionInfo":{"status":"ok","timestamp":1608031660532,"user_tz":-60,"elapsed":425313,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}},"outputId":"0c740b7a-c0f9-4680-c1f8-fa97a3ee6ede"},"source":["gen_latent = generator_latent()\n","gen_imgs = generator(gen_latent, training=True).numpy()\n","\n","fig, axes = plt.subplots(8, 8, sharex=True, sharey=True, figsize=(10, 10))\n","for img, ax in zip(gen_imgs, axes.ravel()):\n","    ax.imshow(img.reshape(image_width, image_height), interpolation='nearest', cmap='gray')\n","    ax.axis('off')\n","fig.tight_layout()"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 64 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"gb2wNL_zGji8","executionInfo":{"status":"ok","timestamp":1608031662507,"user_tz":-60,"elapsed":427283,"user":{"displayName":"Antoine Hue","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjHLnV0vY49RQIAKevTzqKX0LJl27D2nm-E1hnNlA=s64","userId":"06624221712884014093"}}},"source":["discriminator.save(model_path / 'mnist_gan_discriminator.h5')\n","generator.save(model_path / 'mnist_gan_generator.h5')"],"execution_count":17,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cG_Prf2ri-Ry"},"source":["# Conclusion\n","\n","The generator is not fooling that much the discriminator since the discriminator accuracy is well above 50% and its loss is stable at a low level. However, the generated digits are quite well looking to an human eye.\n","\n","## Where to go from here\n","\n","- More advanced GAN based on convolutional layers ([HTML](MNIST_DCGAN.html) / [Jupyter](MNIST_DCGAN.ipynb))\n","- Revisit the fundamentals about deep neural networks in the CNN versus Dense classification ([HTML](../cnn/CnnVsDense-Part1.html) / [Jupyter](../cnn/CnnVsDense-Part1.jupyter) )\n","\n","## References\n","\n","1. [\"Generative adversarial nets\"](http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf), I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, NIPS 2014"]}]}