From 3128f4f62e57a8abfee329feb9461458ac209655 Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@grenoble-inp.fr>
Date: Tue, 18 Feb 2020 22:34:58 +0100
Subject: [PATCH] Linear reg. done.

Former-commit-id: 96473a5b7cc4bddc4834734b4e65af9eda96df00
---
 00 - LinearReg/08.1-Logistic-Regression.ipynb | 837 -----------------
 00 - LinearReg/08.2-Logistic-Regression.ipynb | 810 -----------------
 BHPD/01-DNN-Regression.ipynb                  |   4 +-
 GTSRB/99 Scripts-Tensorboard.ipynb            |   2 +-
 GTSRB/README.ipynb                            |  81 --
 .../01-Linear-Regression.ipynb                |   0
 .../02-Gradient-descent.ipynb                 |   0
 .../03-Polynomial-Regression.ipynb            |   0
 LinearReg/04-Logistic-Regression.ipynb        | 847 ++++++++++++++++++
 pres_numpy.ipynb => Prerequisites/Numpy.ipynb | 135 +--
 README.md                                     |  40 +-
 VAE/06-VAE-withCelebA-post.ipynb              |   2 +-
 12 files changed, 951 insertions(+), 1807 deletions(-)
 delete mode 100644 00 - LinearReg/08.1-Logistic-Regression.ipynb
 delete mode 100644 00 - LinearReg/08.2-Logistic-Regression.ipynb
 delete mode 100644 GTSRB/README.ipynb
 rename {00 - LinearReg => LinearReg}/01-Linear-Regression.ipynb (100%)
 rename {00 - LinearReg => LinearReg}/02-Gradient-descent.ipynb (100%)
 rename {00 - LinearReg => LinearReg}/03-Polynomial-Regression.ipynb (100%)
 create mode 100644 LinearReg/04-Logistic-Regression.ipynb
 rename pres_numpy.ipynb => Prerequisites/Numpy.ipynb (80%)

diff --git a/00 - LinearReg/08.1-Logistic-Regression.ipynb b/00 - LinearReg/08.1-Logistic-Regression.ipynb
deleted file mode 100644
index 4b099a9..0000000
--- a/00 - LinearReg/08.1-Logistic-Regression.ipynb	
+++ /dev/null
@@ -1,837 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![header1](../fidle/img/00-Fidle-header-01.png)\n",
-    "\n",
-    "# Logistic regression, in pure tensorflow\n",
-    "Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.  \n",
-    "Note: This notebook use tensoflow 2 in compatibility mode 1.  \n",
-    "A good reason to use Keras ;-)\n",
-    "\n",
-    "## Objectives :\n",
-    "A logistic regression has the objective of providing a probability of belonging to a class.  \n",
-    "X contains characteristics  \n",
-    "y contains the probability of membership (1 or 0)  \n",
-    "\n",
-    "## Principe :\n",
-    "We'll look for a value of $\\theta$ such that the linear regression $\\theta^{T}X$ can be used to calculate our probability:  \n",
-    "\n",
-    "$\\hat{p} = h_\\theta(X) = \\sigma(\\theta^T{X})$  \n",
-    "\n",
-    "Where $\\sigma$ is the logit function, typically a sigmoid (S) function:  \n",
-    "\n",
-    "$\n",
-    "\\sigma(t) = \\dfrac{1}{1 + \\exp(-t)}\n",
-    "$  \n",
-    "\n",
-    "The predicted value $\\hat{y}$ will then be calculated as follows:\n",
-    "\n",
-    "$\n",
-    "\\hat{y} =\n",
-    "\\begin{cases}\n",
-    "  0 & \\text{if } \\hat{p} < 0.5 \\\\\n",
-    "  1 & \\text{if } \\hat{p} \\geq 0.5\n",
-    "\\end{cases}\n",
-    "$\n",
-    "\n",
-    "**Calculation of the cost of the regression:**  \n",
-    "For a training observation x, the cost can be calculated as follows:  \n",
-    "\n",
-    "$\n",
-    "c(\\theta) =\n",
-    "\\begin{cases}\n",
-    "  -\\log(\\hat{p}) & \\text{if } y = 1 \\\\\n",
-    "  -\\log(1 - \\hat{p}) & \\text{if } y = 0\n",
-    "\\end{cases}\n",
-    "$\n",
-    "\n",
-    "The regression cost function (log loss) over the whole training set can be written as follows:  \n",
-    "\n",
-    "$\n",
-    "J(\\theta) = -\\dfrac{1}{m} \\sum_{i=1}^{m}{\\left[ y^{(i)} log\\left(\\hat{p}^{(i)}\\right) + (1 - y^{(i)}) log\\left(1 - \\hat{p}^{(i)}\\right)\\right]}\n",
-    "$\n",
-    "## Step 1 - Import and init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "\n",
-       "div.warn {    \n",
-       "    background-color: #fcf2f2;\n",
-       "    border-color: #dFb5b4;\n",
-       "    border-left: 5px solid #dfb5b4;\n",
-       "    padding: 0.5em;\n",
-       "    font-weight: bold;\n",
-       "    font-size: 1.1em;;\n",
-       "    }\n",
-       "\n",
-       "\n",
-       "\n",
-       "div.nota {    \n",
-       "    background-color: #DAFFDE;\n",
-       "    border-left: 5px solid #92CC99;\n",
-       "    padding: 0.5em;\n",
-       "    }\n",
-       "\n",
-       "\n",
-       "\n",
-       "</style>\n",
-       "\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "FIDLE 2020 - Practical Work Module\n",
-      "Version              : 0.2.9\n",
-      "Run time             : Tuesday 18 February 2020, 18:55:04\n",
-      "TensorFlow version   : 2.0.0\n",
-      "Keras version        : 2.2.4-tf\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import sklearn as sl\n",
-    "from sklearn import metrics\n",
-    "\n",
-    "import tensorflow.compat.v1 as tf\n",
-    "tf.disable_v2_behavior()\n",
-    "\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import math\n",
-    "import random\n",
-    "import os\n",
-    "import sys\n",
-    "\n",
-    "sys.path.append('..')\n",
-    "import fidle.pwk as ooo\n",
-    "\n",
-    "ooo.init()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.1 - Usefull stuff"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def vector_infos(name,V):\n",
-    "    '''Displaying some information about a vector'''\n",
-    "    with np.printoptions(precision=4, suppress=True):\n",
-    "        print(\"{:16} : ndim={}  shape={:10}  Mean = {}  Std = {}\".format( name,V.ndim, str(V.shape), V.mean(axis=0), V.std(axis=0)))\n",
-    "\n",
-    "def random_batch(X_train, y_train, batch_size):\n",
-    "    '''Returning a data set for a batch'''\n",
-    "    indices = np.random.randint(0, len(X_train), batch_size)\n",
-    "    X_batch = X_train[indices]\n",
-    "    y_batch = y_train[indices]\n",
-    "    return X_batch, y_batch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.2 - Parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_size      = 1000       # Number of observations\n",
-    "data_cols      = 2          # observation size\n",
-    "data_noise     = 0.2\n",
-    "test_ratio     = 0.2        # Ratio of data reserved for validation\n",
-    "random_seed    = 123\n",
-    "\n",
-    "learning_rate  = 0.01\n",
-    "n_epochs       = 1000\n",
-    "batch_size     = 50\n",
-    "\n",
-    "epsilon        = 1e-7       # To avoid overflows on some calculations (log())\n",
-    "\n",
-    "learning_rate2 = 0.01       # Pour la version 2\n",
-    "n_epochs2      = 6000\n",
-    "batch_size2    = 50\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Data preparation\n",
-    "### 2.1 - Get some data\n",
-    "Here the data is retrieved from sklearn's `make_moons' test set generator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_moons          : ndim=2  shape=(1000, 2)   Mean = [0.5008 0.252 ]  Std = [0.883  0.5359]\n",
-      "y_moons          : ndim=1  shape=(1000,)     Mean = 0.5  Std = 0.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import make_moons\n",
-    "\n",
-    "X_moons, y_moons = make_moons(data_size, noise=data_noise, random_state=random_seed)\n",
-    "\n",
-    "fig, ax = plt.subplots(1, 1)\n",
-    "fig.set_size_inches(8,6)\n",
-    "ax.plot(X_moons[y_moons == 1, 0], X_moons[y_moons == 1, 1], 'go', markersize=4, label=\"y=1 (positive)\")\n",
-    "ax.plot(X_moons[y_moons == 0, 0], X_moons[y_moons == 0, 1], 'ro', markersize=4, label=\"y=0 (negative)\")\n",
-    "# ax.set_title(\"Données brutes\")\n",
-    "# ax.legend()\n",
-    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "plt.xlabel('$x_1$')\n",
-    "plt.ylabel('$x_2$')\n",
-    "plt.show()\n",
-    "\n",
-    "vector_infos('X_moons',X_moons)\n",
-    "vector_infos('y_moons',y_moons)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.2 - Preparation of data\n",
-    "\n",
-    "We're going to:\n",
-    "- normalize the data\n",
-    "- add a column of 1 for bias\n",
-    "- Transform y_moons into a vector\n",
-    "- split the data to have : :\n",
-    "  - a training set\n",
-    "  - a test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_scaled         : ndim=2  shape=(1000, 2)   Mean = [ 0. -0.]  Std = [1. 1.]\n",
-      "X_scaled_1       : ndim=2  shape=(1000, 3)   Mean = [ 1.  0. -0.]  Std = [0. 1. 1.]\n",
-      "X_train          : ndim=2  shape=(800, 3)    Mean = [ 1.      0.0206 -0.0056]  Std = [0.     0.9867 1.008 ]\n",
-      "y_train          : ndim=2  shape=(800, 1)    Mean = [0.5162]  Std = [0.4997]\n",
-      "X_test           : ndim=2  shape=(200, 3)    Mean = [ 1.     -0.0825  0.0225]  Std = [0.     1.0476 0.9672]\n",
-      "y_test           : ndim=2  shape=(200, 1)    Mean = [0.435]  Std = [0.4958]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ----- Normalization\n",
-    "\n",
-    "scaler = sl.preprocessing.StandardScaler()\n",
-    "X_scaled   = scaler.fit_transform(X_moons)\n",
-    "\n",
-    "# ----- Add column of 1\n",
-    "\n",
-    "X_scaled_1 = np.c_[np.ones((data_size, 1)), X_scaled]\n",
-    "\n",
-    "# ----- Reshape y_moon\n",
-    "\n",
-    "y_moons_v = y_moons.reshape(-1,1)\n",
-    "\n",
-    "# ----- Dataset -> train, test\n",
-    "\n",
-    "test_size = int(data_size * test_ratio)\n",
-    "X_train = X_scaled_1[:-test_size]\n",
-    "X_test  = X_scaled_1[-test_size:]\n",
-    "y_train = y_moons_v[:-test_size]\n",
-    "y_test  = y_moons_v[-test_size:]\n",
-    "\n",
-    "vector_infos('X_scaled',X_scaled)\n",
-    "vector_infos('X_scaled_1',X_scaled_1)\n",
-    "vector_infos('X_train',X_train)\n",
-    "vector_infos('y_train',y_train)\n",
-    "vector_infos('X_test',X_test)\n",
-    "vector_infos('y_test',y_test)\n",
-    "\n",
-    "y_train_h = y_train.reshape(-1,) # for matplotlib visualization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.3 - Have a look"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "#### Train data :"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Test data :"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ooo.display_md(\"#### Train data :\")\n",
-    "fig, axs = plt.subplots()\n",
-    "fig.set_size_inches(8,6)\n",
-    "axs.plot(X_train[y_train_h == 1, 1], X_train[y_train_h == 1, 2], 'o', color='green', markersize=4, label=\"Train / Positifs\")\n",
-    "axs.plot(X_train[y_train_h == 0, 1], X_train[y_train_h == 0, 2], 'o', color='red',   markersize=4, label=\"Train / Négatifs\")\n",
-    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "plt.xlabel('$x_1$')\n",
-    "plt.ylabel('$x_2$')\n",
-    "plt.show()\n",
-    "\n",
-    "ooo.display_md(\"#### Test data :\")\n",
-    "fig, axs = plt.subplots()\n",
-    "fig.set_size_inches(8,6)\n",
-    "axs.plot(X_test[:, 1], X_test[:, 2], 'o',color='gray', markersize=4, label=\"A classer !\")\n",
-    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "plt.xlabel('$x_1$')\n",
-    "plt.ylabel('$x_2$')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Logistic model #1\n",
-    "### 3.1 - Build model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf.reset_default_graph()\n",
-    "\n",
-    "X = tf.placeholder(tf.float32, shape=(None, data_cols + 1), name=\"X\")\n",
-    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
-    "\n",
-    "initializer = tf.random_uniform([data_cols + 1, 1], -1.0, 1.0, seed=random_seed)\n",
-    "theta = tf.Variable(initializer, name=\"theta\")\n",
-    "\n",
-    "logits = tf.matmul(X, theta, name=\"logits\")\n",
-    "\n",
-    "#y_proba = tf.sigmoid(logits)\n",
-    "y_proba = 1 / (1 + tf.exp(-logits))\n",
-    "\n",
-    "#loss = tf.losses.log_loss(y, y_proba)\n",
-    "loss = -tf.reduce_mean(y * tf.log(y_proba + epsilon) + (1 - y) * tf.log(1 - y_proba + epsilon))\n",
-    "\n",
-    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
-    "training_op = optimizer.minimize(loss)\n",
-    "\n",
-    "init = tf.global_variables_initializer()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3.2 - Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 0 \tLoss: 0.66150576\n",
-      "Epoch: 100 \tLoss: 0.31738347\n",
-      "Epoch: 200 \tLoss: 0.31690764\n",
-      "Epoch: 300 \tLoss: 0.31771263\n",
-      "Epoch: 400 \tLoss: 0.3185466\n",
-      "Epoch: 500 \tLoss: 0.3182384\n",
-      "Epoch: 600 \tLoss: 0.318601\n",
-      "Epoch: 700 \tLoss: 0.3180429\n",
-      "Epoch: 800 \tLoss: 0.31820744\n",
-      "Epoch: 900 \tLoss: 0.31877863\n",
-      "Epoch: 1000 \tLoss: 0.31937808\n"
-     ]
-    }
-   ],
-   "source": [
-    "nb_batches = int(np.ceil(data_size / batch_size))\n",
-    "\n",
-    "with tf.Session() as sess:\n",
-    "    sess.run(init)\n",
-    "\n",
-    "    for epoch in range(n_epochs+1):\n",
-    "        for batch_index in range(nb_batches):\n",
-    "            X_batch, y_batch = random_batch(X_train, y_train, batch_size)\n",
-    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
-    "            \n",
-    "        loss_val = loss.eval({X: X_test, y: y_test})\n",
-    "        \n",
-    "        if epoch % 100 == 0:\n",
-    "            print(\"Epoch:\", epoch, \"\\tLoss:\", loss_val)\n",
-    "\n",
-    "    y_proba_val = y_proba.eval(feed_dict={X: X_test, y: y_test})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "### 3.3 - Evaluation\n",
-    "\n",
-    "Accuracy = Ability to avoid false positives = $\\frac{Tp}{Tp+Fp}$  \n",
-    "Recall = Ability to find the right positives = $\\frac{Tp}{Tp+Fn}$  \n",
-    "Avec :  \n",
-    "$T_p$ (true positive) Correct positive answer  \n",
-    "$F_p$ (false positive) False positive answer  \n",
-    "$T_n$ (true negative) Correct negative answer  \n",
-    "$F_n$ (false negative) Wrong negative answer  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Precision = 0.826    Recall = 0.874\n",
-      "Prédictions et erreurs\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x720 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def show_results(y_proba_val, filename):\n",
-    "    y_pred = (y_proba_val >= 0.5)\n",
-    "\n",
-    "    precision = metrics.precision_score(y_test, y_pred)\n",
-    "    recall    = metrics.recall_score(y_test, y_pred)\n",
-    "\n",
-    "    print(\"Precision = {:5.3f}    Recall = {:5.3f}\".format(precision, recall))\n",
-    "\n",
-    "    y_pred_1d = y_pred.reshape(-1) # Passage en 1D\n",
-    "    y_test_1d = y_test.reshape(-1)\n",
-    "\n",
-    "    X_pred_positives = X_test[ y_pred_1d == True]   # items prédits    positifs\n",
-    "    X_real_positives = X_test[ y_test_1d == 1 ]     # items réellement positifs\n",
-    "    X_pred_negatives = X_test[ y_pred_1d == False]  # items prédits    négatifs\n",
-    "    X_real_negatives = X_test[ y_test_1d == 0 ]     # items réellement négatifs\n",
-    "\n",
-    "    fig, axs = plt.subplots(2, 2)\n",
-    "    fig.subplots_adjust(wspace=.1,hspace=0.2)\n",
-    "    fig.set_size_inches(14,10)\n",
-    "    print(\"Prédictions et erreurs\")\n",
-    "    \n",
-    "    axs[0,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
-    "    axs[0,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green',      markersize=4,  label=\"Réels positifs\")\n",
-    "    axs[0,0].legend()\n",
-    "    axs[0,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "    axs[0,0].set_xlabel('$x_1$')\n",
-    "    axs[0,0].set_ylabel('$x_2$')\n",
-    "\n",
-    "\n",
-    "    axs[0,1].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
-    "    axs[0,1].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red',        markersize=4,  label=\"Réels négatifs\")\n",
-    "    axs[0,1].legend()\n",
-    "    axs[0,1].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "    axs[0,1].set_xlabel('$x_1$')\n",
-    "    axs[0,1].set_ylabel('$x_2$')\n",
-    "    \n",
-    "    axs[1,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
-    "    axs[1,0].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
-    "    axs[1,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green',      markersize=4,  label=\"Réels positifs\")\n",
-    "    axs[1,0].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red',        markersize=4,  label=\"Réels négatifs\")\n",
-    "    axs[1,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "    axs[1,0].set_xlabel('$x_1$')\n",
-    "    axs[1,0].set_ylabel('$x_2$')\n",
-    "\n",
-    "    axs[1,1].pie([precision,1-precision], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
-    "                 autopct='%1.1f%%', shadow=False, startangle=70, colors=[\"lightsteelblue\",\"coral\"])\n",
-    "    axs[1,1].axis('equal')\n",
-    "\n",
-    "    plt.show()\n",
-    "\n",
-    "show_results(y_proba_val, 'LogisticReg-d')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Bending the space to a model #2 ;-)\n",
-    "\n",
-    "We're going to increase the characteristics of our observations, with : ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ et ${x_2}^3$  \n",
-    "\n",
-    "$\n",
-    "X=\n",
-    "\\begin{bmatrix}1 & x_{11} & x_{12} \\\\\n",
-    "\\vdots & \\dots\\\\\n",
-    "1 & x_{m1} & x_{m2}  \\end{bmatrix}\n",
-    "\\text{et }\n",
-    "X_{ng}=\\begin{bmatrix}1 & x_{11} & x_{12} & x_{11}^2 & x_{12}^2& x_{11}^3 & x_{12}^3 \\\\\n",
-    "\\vdots & & & \\dots \\\\\n",
-    "1 & x_{m1} & x_{m2} & x_{m1}^2 & x_{m2}^2& x_{m1}^3 & x_{m2}^3 \\end{bmatrix}\n",
-    "$\n",
-    "\n",
-    "Note : `sklearn.preprocessing.PolynomialFeatures` can do that for us, but we'll do it ourselves:\n",
-    "### 4.1 - Extend data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train_enhanced = np.c_[X_train,\n",
-    "                         X_train[:, 1] ** 2,\n",
-    "                         X_train[:, 2] ** 2,\n",
-    "                         X_train[:, 1] ** 3,\n",
-    "                         X_train[:, 2] ** 3]\n",
-    "X_test_enhanced = np.c_[X_test,\n",
-    "                        X_test[:, 1] ** 2,\n",
-    "                        X_test[:, 2] ** 2,\n",
-    "                        X_test[:, 1] ** 3,\n",
-    "                        X_test[:, 2] ** 3]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.2 - A more readable version of our model. Yes.\n",
-    "With logging for tensorboard and model checkpoints."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def logistic_regression(X, y, initializer=None, seed=42, learning_rate=0.01):\n",
-    "\n",
-    "    n_inputs_including_bias = int(X.get_shape()[1])\n",
-    "    \n",
-    "    with tf.name_scope(\"logistic_regression\"):\n",
-    "        \n",
-    "        # ----- Construction du modèle\n",
-    "        with tf.name_scope(\"model\"):\n",
-    "            if initializer is None:\n",
-    "                initializer = tf.random_uniform([n_inputs_including_bias, 1], -1.0, 1.0, seed=seed)\n",
-    "            theta = tf.Variable(initializer, name=\"theta\")\n",
-    "            # X.theta\n",
-    "            logits = tf.matmul(X, theta, name=\"logits\")\n",
-    "            # Probabilité\n",
-    "            y_proba = tf.sigmoid(logits)\n",
-    "            \n",
-    "        with tf.name_scope(\"train\"):\n",
-    "            # Perte logistique\n",
-    "            loss = tf.losses.log_loss(y, y_proba, scope=\"loss\")\n",
-    "            # Descente de gradient\n",
-    "            optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate2)\n",
-    "            training_op = optimizer.minimize(loss)\n",
-    "            # Trace\n",
-    "            loss_summary = tf.summary.scalar('log_loss', loss)\n",
-    "            \n",
-    "        with tf.name_scope(\"init\"):\n",
-    "            init = tf.global_variables_initializer()\n",
-    "            \n",
-    "        with tf.name_scope(\"save\"):\n",
-    "            saver = tf.train.Saver(max_to_keep=4)\n",
-    "            \n",
-    "    return y_proba, loss, training_op, loss_summary, init, saver\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.3 - Build the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf.reset_default_graph()\n",
-    "\n",
-    "log_dir = './run/logs'\n",
-    "chk_dir = './run/models'\n",
-    "os.makedirs(log_dir, mode=0o750, exist_ok=True)\n",
-    "os.makedirs(chk_dir, mode=0o750, exist_ok=True)\n",
-    "\n",
-    "X = tf.placeholder(tf.float32, shape=(None, data_cols + 1 + 4), name=\"X\")\n",
-    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
-    "\n",
-    "# Model construction\n",
-    "y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(X, y)\n",
-    "\n",
-    "# Enregistrement du modèle\n",
-    "file_writer = tf.summary.FileWriter(log_dir, tf.get_default_graph())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.4 - Train the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch:      0  Loss:   0.5859    checkpoint: ./run/models/model-ckpt-0\n",
-      "Epoch:    500  Loss:   0.1579    checkpoint: ./run/models/model-ckpt-500\n",
-      "Epoch:   1000  Loss:   0.1297    checkpoint: ./run/models/model-ckpt-1000\n",
-      "Epoch:   1500  Loss:   0.1174    checkpoint: ./run/models/model-ckpt-1500\n",
-      "Epoch:   2000  Loss:   0.1106    checkpoint: ./run/models/model-ckpt-2000\n",
-      "WARNING:tensorflow:From /home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use standard file APIs to delete files with this prefix.\n",
-      "Epoch:   2500  Loss:   0.1065    checkpoint: ./run/models/model-ckpt-2500\n",
-      "Epoch:   3000  Loss:   0.1039    checkpoint: ./run/models/model-ckpt-3000\n",
-      "Epoch:   3500  Loss:   0.1023    checkpoint: ./run/models/model-ckpt-3500\n",
-      "Epoch:   4000  Loss:   0.1010    checkpoint: ./run/models/model-ckpt-4000\n",
-      "Epoch:   4500  Loss:   0.1003    checkpoint: ./run/models/model-ckpt-4500\n",
-      "Epoch:   5000  Loss:   0.1000    checkpoint: ./run/models/model-ckpt-5000\n",
-      "Epoch:   5500  Loss:   0.0995    checkpoint: ./run/models/model-ckpt-5500\n",
-      "Epoch:   6000  Loss:   0.0992    checkpoint: ./run/models/model-ckpt-6000\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_batches = int(np.ceil(data_size / batch_size2))\n",
-    "\n",
-    "epoch_file  = chk_dir + \"/epoch.last\"\n",
-    "model_file  = chk_dir + \"/model-ckpt\"\n",
-    "model_final = chk_dir + \"/model-final\"\n",
-    "\n",
-    "with tf.Session() as sess:\n",
-    "    \n",
-    "    # ----- Point de départ ? Checkpoint ou 0\n",
-    "    #\n",
-    "    if os.path.isfile(epoch_file):\n",
-    "        # Si epoch_file existe : On récupère l'époque et on restaure le checkpoint correspondant\n",
-    "        with open(epoch_file, \"r\") as f:\n",
-    "            epoch = int(f.read())\n",
-    "        saver.restore(sess, '{}-{}'.format(model_file,epoch))\n",
-    "        start_epoch=epoch+1\n",
-    "        print(\"Reprise de l'apprentissage à l'époque : \", start_epoch)\n",
-    "        print(\"Restauration du checkpoint            : \", chk_dir,'-',epoch)\n",
-    "    else:\n",
-    "        # epoch_file introuvable : On commence à 0\n",
-    "        start_epoch = 0\n",
-    "        sess.run(init)\n",
-    "\n",
-    "    # ----- Ok, on y va...\n",
-    "    #\n",
-    "    for epoch in range(start_epoch, n_epochs2 + 1):\n",
-    "        \n",
-    "        for batch_index in range(n_batches):\n",
-    "            # Recupération du lot\n",
-    "            X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\n",
-    "            # Apprentissage\n",
-    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
-    "        \n",
-    "        # Calcul de la perte logistique et du log (en une seule fois)\n",
-    "        loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\n",
-    "        # Enregistrement des logs\n",
-    "        file_writer.add_summary(summary_str, epoch)\n",
-    "        \n",
-    "        if epoch % 500 == 0:\n",
-    "            print('Epoch: {:6d}  Loss: {:8.4f}    checkpoint: {}-{}'.format(epoch,loss_val,model_file,epoch))\n",
-    "            # Sauvegarde d'un checkpoint\n",
-    "            saver.save(sess, model_file, global_step=epoch)\n",
-    "            # Sauvearde de l'epoch\n",
-    "            with open(epoch_file, \"w\") as f:\n",
-    "                f.write(str(epoch))\n",
-    "\n",
-    "    # Sauvegarde du modèle final\n",
-    "    saver.save(sess, model_final)\n",
-    "    # Calcul des probabilités de l'échantillon test\n",
-    "    y_proba_val2 = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})\n",
-    "    # Supression de l'epoch_file\n",
-    "    os.remove(epoch_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.5 - Evaluation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Precision = 0.955    Recall = 0.966\n",
-      "Prédictions et erreurs\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x720 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_results(y_proba_val2, 'LogisticReg-e')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "![](../fidle/img/00-Fidle-logo-01_s.png)"
-   ]
-  }
- ],
- "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": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/00 - LinearReg/08.2-Logistic-Regression.ipynb b/00 - LinearReg/08.2-Logistic-Regression.ipynb
deleted file mode 100644
index b78ab1d..0000000
--- a/00 - LinearReg/08.2-Logistic-Regression.ipynb	
+++ /dev/null
@@ -1,810 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Exemple de regression logistique\n",
-    "================================\n",
-    "Logistic Regression with Mini-Batch Gradient Descent using TensorFlow\n",
-    "---------------------------------------------------------------------\n",
-    "\n",
-    "Une regression logistique à pour objectif de fournir une probabilité d'appartenance à une classe.  \n",
-    "X contient des caractéristiques  \n",
-    "y contient la probabilité d'appartenance (1 ou 0)\n",
-    "\n",
-    "**Principe :**  \n",
-    "On va rechercher une valeur de $\\theta$ tel que la regression linéaire $\\theta^{T}X$ puisse servir à calculer notre probabilité :  \n",
-    "\n",
-    "$\\hat{p} = h_\\theta(X) = \\sigma(\\theta^T{X})$  \n",
-    "\n",
-    "Où $\\sigma$ est la fonction logistique (logit), typiquement une fonction sigmoïde (en S) :  \n",
-    "\n",
-    "$\n",
-    "\\sigma(t) = \\dfrac{1}{1 + \\exp(-t)}\n",
-    "$  \n",
-    "\n",
-    "La valeur prédite $\\hat{y}$ sera alors calculée de la manière suivante :\n",
-    "\n",
-    "$\n",
-    "\\hat{y} =\n",
-    "\\begin{cases}\n",
-    "  0 & \\text{if } \\hat{p} < 0.5 \\\\\n",
-    "  1 & \\text{if } \\hat{p} \\geq 0.5\n",
-    "\\end{cases}\n",
-    "$\n",
-    "\n",
-    "**Calcul du coût de la régression :**  \n",
-    "Pour une observation d'entrainement x, le coût peut être calculé comme suit :  \n",
-    "\n",
-    "$\n",
-    "c(\\theta) =\n",
-    "\\begin{cases}\n",
-    "  -\\log(\\hat{p}) & \\text{if } y = 1 \\\\\n",
-    "  -\\log(1 - \\hat{p}) & \\text{if } y = 0\n",
-    "\\end{cases}\n",
-    "$\n",
-    "\n",
-    "La fonction de coût de la regression (perte logistique ou *log loss*) sur l'ensemble du jeu d'apprentissage peut s'écrire de la manière suivante :  \n",
-    "\n",
-    "$\n",
-    "J(\\theta) = -\\dfrac{1}{m} \\sum_{i=1}^{m}{\\left[ y^{(i)} log\\left(\\hat{p}^{(i)}\\right) + (1 - y^{(i)}) log\\left(1 - \\hat{p}^{(i)}\\right)\\right]}\n",
-    "$\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Deepmod by pjluc 2019\n",
-      "  Version          : 0.4.1\n",
-      "  Run time         : Wednesday 25 September 2019, 14:37:32\n",
-      "  Run directory    : ./run/lab-08.2\n",
-      "  Save figs        : True\n",
-      "  Matplotlib style : deepmods/talk.mplstyle\n",
-      "  Hide warning     : True\n",
-      "\n",
-      "TensorFlow version :  1.14.0\n",
-      "Keras version      :  2.2.4-tf\n",
-      "\n",
-      "Init done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import sklearn as sl\n",
-    "from sklearn import metrics\n",
-    "import tensorflow as tf\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import math\n",
-    "import random\n",
-    "import os\n",
-    "\n",
-    "import deepmods.notebook as ooo\n",
-    "\n",
-    "ooo.init(id='08.2', save_figs=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Affichage de quelques informations concernant un vecteur\n",
-    "#\n",
-    "def vector_infos(name,V):\n",
-    "    with np.printoptions(precision=4, suppress=True):\n",
-    "        print(\"{:16} : ndim={}  shape={:10}  Moyenne = {}  Ecart-type = {}\".format( name,V.ndim, str(V.shape), V.mean(axis=0), V.std(axis=0)))\n",
-    "        #print('Exemple: \\n',V[0:5])\n",
-    "\n",
-    "# Renvoi un jeu de donnée pour un batch\n",
-    "#\n",
-    "def random_batch(X_train, y_train, batch_size):\n",
-    "    indices = np.random.randint(0, len(X_train), batch_size)\n",
-    "    X_batch = X_train[indices]\n",
-    "    y_batch = y_train[indices]\n",
-    "    return X_batch, y_batch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1/ Paramètres\n",
-    "-------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_size      = 1000       # Nombre de données\n",
-    "data_cols      = 2          # Nombre de colonnes\n",
-    "data_noise     = 0.2\n",
-    "test_ratio     = 0.2        # Ratio des données de tests \n",
-    "random_seed    = 123\n",
-    "\n",
-    "learning_rate  = 0.01       # Rythme d'apprentissage\n",
-    "n_epochs       = 1000\n",
-    "batch_size     = 50\n",
-    "\n",
-    "epsilon        = 1e-7       # Pour éviter des débordement sur certains calculs (log())\n",
-    "\n",
-    "learning_rate2 = 0.01       # Pour la version 2\n",
-    "n_epochs2      = 6000\n",
-    "batch_size2    = 50\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "2/ Récupération / création des données\n",
-    "--------------------------------------\n",
-    "Les données sont ici totalement fabriquées ;-)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "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"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset X        : ndim=2  shape=(1000, 2)   Moyenne = [4.9446 6.9893]  Ecart-type = [0.9956 1.466 ]\n",
-      "Dataset y        : ndim=1  shape=(1000,)     Moyenne = 0.636  Ecart-type = 0.481148625686492\n"
-     ]
-    }
-   ],
-   "source": [
-    "hours_of_sleep_min = 5\n",
-    "hours_of_work_min  = 4\n",
-    "hours_of_game_max  = 3\n",
-    "\n",
-    "def do_i_have_it(hours_of_work, hours_of_sleep):\n",
-    "    # ---- Have to sleep and work\n",
-    "    if hours_of_sleep < hours_of_sleep_min: return 0\n",
-    "    if hours_of_work < hours_of_work_min:   return 0\n",
-    "    # ---- Gameboy is not good for you\n",
-    "    hours_of_game = 24 - 10 - hours_of_sleep - hours_of_work + random.gauss(0,0.4)\n",
-    "    if hours_of_game > hours_of_game_max:   return 0\n",
-    "    # ---- Fine, you got it\n",
-    "    return 1\n",
-    "\n",
-    "def make_students_dataset(size, noise):\n",
-    "    x = []\n",
-    "    y = []\n",
-    "    for i in range(size):\n",
-    "        w = random.gauss(5,1)\n",
-    "        s = random.gauss(7,1.5)\n",
-    "        r   = do_i_have_it(w,s)\n",
-    "        x.append([w,s])\n",
-    "        y.append(r)\n",
-    "    return (np.array(x), np.array(y))\n",
-    "\n",
-    "X_data,y_data=make_students_dataset(data_size,data_noise)\n",
-    "\n",
-    "fig, ax = plt.subplots(1, 1)\n",
-    "fig.set_size_inches(8,6)\n",
-    "ax.plot(X_data[y_data == 1, 0], X_data[y_data == 1, 1], 'go', markersize=4, label=\"y=1 (positive)\")\n",
-    "ax.plot(X_data[y_data == 0, 0], X_data[y_data == 0, 1], 'ro', markersize=4, label=\"y=0 (negative)\")\n",
-    "# plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "plt.xlabel('Hours of work')\n",
-    "plt.ylabel('Hours of sleep')\n",
-    "ooo.save_fig('LogisticReg-a', svg=True)\n",
-    "plt.show()\n",
-    "\n",
-    "vector_infos('Dataset X',X_data)\n",
-    "vector_infos('Dataset y',y_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "2/ Préparation des données\n",
-    "--------------------------\n",
-    "On va :\n",
-    "- normaliser les données\n",
-    "- rajouter une colonne de 1 pour les biais\n",
-    "- verticaliser y_moons\n",
-    "- partager les données pour avoir :\n",
-    "  - un jeu d'apprentissage\n",
-    "  - un jeu de test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_scaled         : ndim=2  shape=(1000, 2)   Moyenne = [-0.  0.]  Ecart-type = [1. 1.]\n",
-      "X_train          : ndim=2  shape=(800, 3)    Moyenne = [ 1.     -0.0038 -0.0055]  Ecart-type = [0.     1.0151 0.9875]\n",
-      "y_train          : ndim=2  shape=(800, 1)    Moyenne = [0.6388]  Ecart-type = [0.4804]\n",
-      "X_test           : ndim=2  shape=(200, 3)    Moyenne = [1.     0.0154 0.0221]  Ecart-type = [0.     0.9372 1.0484]\n",
-      "y_test           : ndim=2  shape=(200, 1)    Moyenne = [0.625]  Ecart-type = [0.4841]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ----- Normalisation des données\n",
-    "scaler = sl.preprocessing.StandardScaler()\n",
-    "X_scaled   = scaler.fit_transform(X_data)\n",
-    "\n",
-    "# ----- Ajout de la colonne de 1\n",
-    "X_scaled_1 = np.c_[np.ones((data_size, 1)), X_scaled]\n",
-    "\n",
-    "# ----- Verticalisation de y_moons\n",
-    "y_data_v = y_data.reshape(-1,1)\n",
-    "\n",
-    "# ----- Partage des données\n",
-    "test_size = int(data_size * test_ratio)\n",
-    "X_train = X_scaled_1[:-test_size]\n",
-    "X_test  = X_scaled_1[-test_size:]\n",
-    "y_train = y_data_v[:-test_size]\n",
-    "y_test  = y_data_v[-test_size:]\n",
-    "\n",
-    "vector_infos('X_scaled',X_scaled)\n",
-    "vector_infos('X_train',X_train)\n",
-    "vector_infos('y_train',y_train)\n",
-    "vector_infos('X_test',X_test)\n",
-    "vector_infos('y_test',y_test)\n",
-    "\n",
-    "y_train_h = y_train.reshape(-1,) # nécessaire pour la visu."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train data\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test data\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots()\n",
-    "fig.set_size_inches(8,6)\n",
-    "\n",
-    "# axs.set_title(\"Train data\")\n",
-    "print(\"Train data\")\n",
-    "axs.plot(X_train[y_train_h == 1, 1], X_train[y_train_h == 1, 2], 'o', color='green', markersize=4, label=\"Train / Positifs\")\n",
-    "axs.plot(X_train[y_train_h == 0, 1], X_train[y_train_h == 0, 2], 'o', color='red',   markersize=4, label=\"Train / Négatifs\")\n",
-    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "plt.xlabel('Hours of work')\n",
-    "plt.ylabel('Hours of sleep')\n",
-    "ooo.save_fig('LogisticReg-b',svg=True)\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "fig, axs = plt.subplots()\n",
-    "fig.set_size_inches(8,6)\n",
-    "\n",
-    "# axs.set_title(\"Test data\")\n",
-    "print(\"Test data\")\n",
-    "axs.plot(X_test[:, 1], X_test[:, 2], 'o',color='gray', markersize=4, label=\"A classer !\")\n",
-    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "plt.xlabel('Hours of work')\n",
-    "plt.ylabel('Hours of sleep')\n",
-    "ooo.save_fig('LogisticReg-c', svg=True)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Construction du modèle #1\n",
-    "-------------------------\n",
-    "**Modèle :**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf.reset_default_graph()\n",
-    "\n",
-    "X = tf.placeholder(tf.float32, shape=(None, data_cols + 1), name=\"X\")\n",
-    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
-    "\n",
-    "initializer = tf.random_uniform([data_cols + 1, 1], -1.0, 1.0, seed=random_seed)\n",
-    "theta = tf.Variable(initializer, name=\"theta\")\n",
-    "\n",
-    "logits = tf.matmul(X, theta, name=\"logits\")\n",
-    "\n",
-    "# Probabilité\n",
-    "#y_proba = tf.sigmoid(logits)\n",
-    "y_proba = 1 / (1 + tf.exp(-logits))\n",
-    "\n",
-    "# Perte logistique\n",
-    "#loss = tf.losses.log_loss(y, y_proba)\n",
-    "loss = -tf.reduce_mean(y * tf.log(y_proba + epsilon) + (1 - y) * tf.log(1 - y_proba + epsilon))\n",
-    "\n",
-    "# Optimisation du gradient\n",
-    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
-    "training_op = optimizer.minimize(loss)\n",
-    "\n",
-    "init = tf.global_variables_initializer()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Calcul du modèle :**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 0 \tLoss: 1.2533283\n",
-      "Epoch: 100 \tLoss: 0.3106674\n",
-      "Epoch: 200 \tLoss: 0.28309485\n",
-      "Epoch: 300 \tLoss: 0.27574852\n",
-      "Epoch: 400 \tLoss: 0.2735801\n",
-      "Epoch: 500 \tLoss: 0.27165958\n",
-      "Epoch: 600 \tLoss: 0.2728287\n",
-      "Epoch: 700 \tLoss: 0.2730859\n",
-      "Epoch: 800 \tLoss: 0.2731394\n",
-      "Epoch: 900 \tLoss: 0.27550068\n",
-      "Epoch: 1000 \tLoss: 0.27360386\n"
-     ]
-    }
-   ],
-   "source": [
-    "nb_batches = int(np.ceil(data_size / batch_size))\n",
-    "\n",
-    "with tf.Session() as sess:\n",
-    "    sess.run(init)\n",
-    "\n",
-    "    for epoch in range(n_epochs+1):\n",
-    "        for batch_index in range(nb_batches):\n",
-    "            X_batch, y_batch = random_batch(X_train, y_train, batch_size)\n",
-    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
-    "            \n",
-    "        loss_val = loss.eval({X: X_test, y: y_test})\n",
-    "        \n",
-    "        if epoch % 100 == 0:\n",
-    "            print(\"Epoch:\", epoch, \"\\tLoss:\", loss_val)\n",
-    "\n",
-    "    y_proba_val = y_proba.eval(feed_dict={X: X_test, y: y_test})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "**Evaluation du modèle :**  \n",
-    "\n",
-    "Précision = Capacité à ne pas faire de faux positifs = $\\frac{Tp}{Tp+Fp}$  \n",
-    "Rappel = Capacité à trouver les bon positifs = $\\frac{Tp}{Tp+Fn}$  \n",
-    "Avec :  \n",
-    "$T_p$ (true positive) Réponse positive correcte  \n",
-    "$F_p$ (false positive) Réponse positive fausse  \n",
-    "$T_n$ (true negative) Réponse négative correcte  \n",
-    "$F_n$ (false negative) Réponse négative fausse  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Precision = 0.916    Recall = 0.960\n",
-      "Prédictions et erreurs\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x720 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def show_results(y_proba_val, filename):\n",
-    "    y_pred = (y_proba_val >= 0.5)\n",
-    "\n",
-    "    precision = metrics.precision_score(y_test, y_pred)\n",
-    "    recall    = metrics.recall_score(y_test, y_pred)\n",
-    "\n",
-    "    print(\"Precision = {:5.3f}    Recall = {:5.3f}\".format(precision, recall))\n",
-    "\n",
-    "\n",
-    "    y_pred_1d = y_pred.reshape(-1) # Passage en 1D\n",
-    "    y_test_1d = y_test.reshape(-1)\n",
-    "\n",
-    "    X_pred_positives = X_test[ y_pred_1d == True]   # items prédits    positifs\n",
-    "    X_real_positives = X_test[ y_test_1d == 1 ]     # items réellement positifs\n",
-    "    X_pred_negatives = X_test[ y_pred_1d == False]  # items prédits    négatifs\n",
-    "    X_real_negatives = X_test[ y_test_1d == 0 ]     # items réellement négatifs\n",
-    "\n",
-    "    fig, axs = plt.subplots(2, 2)#, sharey=True, sharex=True)\n",
-    "    fig.subplots_adjust(wspace=.1,hspace=0.2)\n",
-    "    fig.set_size_inches(14,10)\n",
-    "#     fig.suptitle('Prédictions et erreurs', fontsize=16,y=0.92)\n",
-    "    print(\"Prédictions et erreurs\")\n",
-    "    \n",
-    "    axs[0,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
-    "    axs[0,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green',      markersize=4,  label=\"Réels positifs\")\n",
-    "    axs[0,0].legend()\n",
-    "    axs[0,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "    axs[0,0].set_xlabel('$x_1$')\n",
-    "    axs[0,0].set_ylabel('$x_2$')\n",
-    "\n",
-    "\n",
-    "    axs[0,1].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
-    "    axs[0,1].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red',        markersize=4,  label=\"Réels négatifs\")\n",
-    "    axs[0,1].legend()\n",
-    "    axs[0,1].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "    axs[0,1].set_xlabel('$x_1$')\n",
-    "    axs[0,1].set_ylabel('$x_2$')\n",
-    "    \n",
-    "    axs[1,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
-    "    axs[1,0].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
-    "    axs[1,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green',      markersize=4,  label=\"Réels positifs\")\n",
-    "    axs[1,0].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red',        markersize=4,  label=\"Réels négatifs\")\n",
-    "    axs[1,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
-    "    axs[1,0].set_xlabel('$x_1$')\n",
-    "    axs[1,0].set_ylabel('$x_2$')\n",
-    "\n",
-    "    axs[1,1].pie([precision,1-precision], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
-    "                 autopct='%1.1f%%', shadow=False, startangle=70, colors=[\"lightsteelblue\",\"coral\"])\n",
-    "    axs[1,1].axis('equal')\n",
-    "\n",
-    "    ooo.save_fig(filename, svg=True)\n",
-    "    plt.show()\n",
-    "\n",
-    "show_results(y_proba_val, 'LogisticReg-d')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Amélioration du modèle #2\n",
-    "-------------------------\n",
-    "On va ajouter des paramètres : ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ et ${x_2}^3$  \n",
-    "\n",
-    "$\n",
-    "X=\n",
-    "\\begin{bmatrix}1 & x_{11} & x_{12} \\\\\n",
-    "\\vdots & \\dots\\\\\n",
-    "1 & x_{m1} & x_{m2}  \\end{bmatrix}\n",
-    "\\text{et }\n",
-    "X_{ng}=\\begin{bmatrix}1 & x_{11} & x_{12} & x_{11}^2 & x_{12}^2& x_{11}^3 & x_{12}^3 \\\\\n",
-    "\\vdots & & & \\dots \\\\\n",
-    "1 & x_{m1} & x_{m2} & x_{m1}^2 & x_{m2}^2& x_{m1}^3 & x_{m2}^3 \\end{bmatrix}\n",
-    "$\n",
-    "\n",
-    "Note : `sklearn.preprocessing.PolynomialFeatures` peut faire ça pour vous "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train_enhanced = np.c_[X_train,\n",
-    "                         X_train[:, 1] ** 2,\n",
-    "                         X_train[:, 2] ** 2,\n",
-    "                         X_train[:, 1] ** 3,\n",
-    "                         X_train[:, 2] ** 3]\n",
-    "X_test_enhanced = np.c_[X_test,\n",
-    "                        X_test[:, 1] ** 2,\n",
-    "                        X_test[:, 2] ** 2,\n",
-    "                        X_test[:, 1] ** 3,\n",
-    "                        X_test[:, 2] ** 3]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def logistic_regression(X, y, initializer=None, seed=42, learning_rate=0.01):\n",
-    "\n",
-    "    n_inputs_including_bias = int(X.get_shape()[1])\n",
-    "    \n",
-    "    with tf.name_scope(\"logistic_regression\"):\n",
-    "        \n",
-    "        # ----- Construction du modèle\n",
-    "        with tf.name_scope(\"model\"):\n",
-    "            if initializer is None:\n",
-    "                initializer = tf.random_uniform([n_inputs_including_bias, 1], -1.0, 1.0, seed=seed)\n",
-    "            theta = tf.Variable(initializer, name=\"theta\")\n",
-    "            # X.theta\n",
-    "            logits = tf.matmul(X, theta, name=\"logits\")\n",
-    "            # Probabilité\n",
-    "            y_proba = tf.sigmoid(logits)\n",
-    "            \n",
-    "        with tf.name_scope(\"train\"):\n",
-    "            # Perte logistique\n",
-    "            loss = tf.losses.log_loss(y, y_proba, scope=\"loss\")\n",
-    "            # Descente de gradient\n",
-    "            optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate2)\n",
-    "            training_op = optimizer.minimize(loss)\n",
-    "            # Trace\n",
-    "            loss_summary = tf.summary.scalar('log_loss', loss)\n",
-    "            \n",
-    "        with tf.name_scope(\"init\"):\n",
-    "            init = tf.global_variables_initializer()\n",
-    "            \n",
-    "        with tf.name_scope(\"save\"):\n",
-    "            saver = tf.train.Saver(max_to_keep=4)\n",
-    "            \n",
-    "    return y_proba, loss, training_op, loss_summary, init, saver\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Construction du modèle**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "log dir is         : ./run/lab-08.2/log/2019-09-25_14h38m52s\n",
-      "To run TensorBoard : # tensorboard --logdir=\"./run/lab-08.2/log\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "tf.reset_default_graph()\n",
-    "\n",
-    "log_dir = ooo.get_log_dir()\n",
-    "chk_dir = ooo.get_check_dir()\n",
-    "\n",
-    "X = tf.placeholder(tf.float32, shape=(None, data_cols + 1 + 4), name=\"X\")\n",
-    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
-    "\n",
-    "# Construction du modèle\n",
-    "y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(X, y)\n",
-    "\n",
-    "# Enregistrement du modèle\n",
-    "file_writer = tf.summary.FileWriter(log_dir, tf.get_default_graph())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Calcul du modèle**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch:      0  Loss:   0.7885    checkpoint: ./run/lab-08.2/check/model-ckpt-0\n",
-      "Epoch:    500  Loss:   0.1769    checkpoint: ./run/lab-08.2/check/model-ckpt-500\n",
-      "Epoch:   1000  Loss:   0.1506    checkpoint: ./run/lab-08.2/check/model-ckpt-1000\n",
-      "Epoch:   1500  Loss:   0.1379    checkpoint: ./run/lab-08.2/check/model-ckpt-1500\n",
-      "Epoch:   2000  Loss:   0.1307    checkpoint: ./run/lab-08.2/check/model-ckpt-2000\n",
-      "Epoch:   2500  Loss:   0.1253    checkpoint: ./run/lab-08.2/check/model-ckpt-2500\n",
-      "Epoch:   3000  Loss:   0.1216    checkpoint: ./run/lab-08.2/check/model-ckpt-3000\n",
-      "Epoch:   3500  Loss:   0.1196    checkpoint: ./run/lab-08.2/check/model-ckpt-3500\n",
-      "Epoch:   4000  Loss:   0.1174    checkpoint: ./run/lab-08.2/check/model-ckpt-4000\n",
-      "Epoch:   4500  Loss:   0.1170    checkpoint: ./run/lab-08.2/check/model-ckpt-4500\n",
-      "Epoch:   5000  Loss:   0.1157    checkpoint: ./run/lab-08.2/check/model-ckpt-5000\n",
-      "Epoch:   5500  Loss:   0.1144    checkpoint: ./run/lab-08.2/check/model-ckpt-5500\n",
-      "Epoch:   6000  Loss:   0.1145    checkpoint: ./run/lab-08.2/check/model-ckpt-6000\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_batches = int(np.ceil(data_size / batch_size2))\n",
-    "\n",
-    "epoch_file  = chk_dir + \"/epoch.last\"\n",
-    "model_file  = chk_dir + \"/model-ckpt\"\n",
-    "model_final = chk_dir + \"/model-final\"\n",
-    "\n",
-    "with tf.Session() as sess:\n",
-    "    \n",
-    "    # ----- Point de départ ? Checkpoint ou 0\n",
-    "    #\n",
-    "    if os.path.isfile(epoch_file):\n",
-    "        # Si epoch_file existe : On récupère l'époque et on restaure le checkpoint correspondant\n",
-    "        with open(epoch_file, \"r\") as f:\n",
-    "            epoch = int(f.read())\n",
-    "        saver.restore(sess, '{}-{}'.format(model_file,epoch))\n",
-    "        start_epoch=epoch+1\n",
-    "        print(\"Reprise de l'apprentissage à l'époque : \", start_epoch)\n",
-    "        print(\"Restauration du checkpoint            : \", chk_dir,'-',epoch)\n",
-    "    else:\n",
-    "        # epoch_file introuvable : On commence à 0\n",
-    "        start_epoch = 0\n",
-    "        sess.run(init)\n",
-    "\n",
-    "    # ----- Ok, on y va...\n",
-    "    #\n",
-    "    for epoch in range(start_epoch, n_epochs2 + 1):\n",
-    "        \n",
-    "        for batch_index in range(n_batches):\n",
-    "            # Recupération du lot\n",
-    "            X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\n",
-    "            # Apprentissage\n",
-    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
-    "        \n",
-    "        # Calcul de la perte logistique et du log (en une seule fois)\n",
-    "        loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\n",
-    "        # Enregistrement des logs\n",
-    "        file_writer.add_summary(summary_str, epoch)\n",
-    "        \n",
-    "        if epoch % 500 == 0:\n",
-    "            print('Epoch: {:6d}  Loss: {:8.4f}    checkpoint: {}-{}'.format(epoch,loss_val,model_file,epoch))\n",
-    "            # Sauvegarde d'un checkpoint\n",
-    "            saver.save(sess, model_file, global_step=epoch)\n",
-    "            # Sauvearde de l'epoch\n",
-    "            with open(epoch_file, \"w\") as f:\n",
-    "                f.write(str(epoch))\n",
-    "\n",
-    "    # Sauvegarde du modèle final\n",
-    "    saver.save(sess, model_final)\n",
-    "    # Calcul des probabilités de l'échantillon test\n",
-    "    y_proba_val2 = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})\n",
-    "    # Supression de l'epoch_file\n",
-    "    os.remove(epoch_file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Precision = 0.953    Recall = 0.976\n",
-      "Prédictions et erreurs\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x720 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_results(y_proba_val2, 'LogisticReg-e')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "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": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/BHPD/01-DNN-Regression.ipynb b/BHPD/01-DNN-Regression.ipynb
index 11391c4..e7188f9 100644
--- a/BHPD/01-DNN-Regression.ipynb
+++ b/BHPD/01-DNN-Regression.ipynb
@@ -6,8 +6,8 @@
    "source": [
     "![header1](../fidle/img/00-Fidle-header-01.png)\n",
     "\n",
-    "Deep Neural Network (DNN) - BHPD dataset\n",
-    "========================================\n",
+    "# Deep Neural Network (DNN) - BHPD dataset\n",
+    "\n",
     "\n",
     "A very simple and classic example of **regression** :\n",
     "\n",
diff --git a/GTSRB/99 Scripts-Tensorboard.ipynb b/GTSRB/99 Scripts-Tensorboard.ipynb
index 549839d..5fb722b 100644
--- a/GTSRB/99 Scripts-Tensorboard.ipynb	
+++ b/GTSRB/99 Scripts-Tensorboard.ipynb	
@@ -169,7 +169,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
diff --git a/GTSRB/README.ipynb b/GTSRB/README.ipynb
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@@ -1,81 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "German Traffic Sign Recognition Benchmark (GTSRB)\n",
-    "=================================================\n",
-    "\n",
-    "---\n",
-    "Introduction au Deep Learning  (IDLE)  \n",
-    "S. Aria, E. Maldonado, JL. Parouty  \n",
-    "CNRS/SARI/DEVLOG - 2020\n",
-    "\n",
-    "Objectives of this practical work\n",
-    "---------------------------------\n",
-    "   \n",
-    "Traffic sign classification with **CNN**, using Tensorflow and **Keras**  \n",
-    "\n",
-    "\n",
-    "About the dataset\n",
-    "-----------------\n",
-    "\n",
-    "Name : [German Traffic Sign Recognition Benchmark (GTSRB)](http://benchmark.ini.rub.de/?section=gtsrb)  \n",
-    "Available [here](https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html) \n",
-    "or on **[kaggle](https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign)**  \n",
-    "\n",
-    "A nice example from : [Alex Staravoitau](https://navoshta.com/traffic-signs-classification/)  \n",
-    "\n",
-    "In few words :\n",
-    " - Images : Variable dimensions, rgb\n",
-    " - Train set : 39209 images  \n",
-    " - Test set : 12630 images\n",
-    " - Classes : 0 to 42\n",
-    "\n",
-    "Episodes\n",
-    "--------\n",
-    "   \n",
-    "**[01 - Preparation of data](01-Preparation-of-data.ipynb)**\n",
-    " - Understanding the dataset\n",
-    " - Preparing and formatting data\n",
-    " - Organize and backup data\n",
-    " \n",
-    "**[02 - First convolutions](02-First-convolutions.ipynb)**\n",
-    " - Read dataset\n",
-    " - Build a model\n",
-    " - Train the model\n",
-    " - Model evaluation\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/00 - LinearReg/01-Linear-Regression.ipynb b/LinearReg/01-Linear-Regression.ipynb
similarity index 100%
rename from 00 - LinearReg/01-Linear-Regression.ipynb
rename to LinearReg/01-Linear-Regression.ipynb
diff --git a/00 - LinearReg/02-Gradient-descent.ipynb b/LinearReg/02-Gradient-descent.ipynb
similarity index 100%
rename from 00 - LinearReg/02-Gradient-descent.ipynb
rename to LinearReg/02-Gradient-descent.ipynb
diff --git a/00 - LinearReg/03-Polynomial-Regression.ipynb b/LinearReg/03-Polynomial-Regression.ipynb
similarity index 100%
rename from 00 - LinearReg/03-Polynomial-Regression.ipynb
rename to LinearReg/03-Polynomial-Regression.ipynb
diff --git a/LinearReg/04-Logistic-Regression.ipynb b/LinearReg/04-Logistic-Regression.ipynb
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index 0000000..e6ad34d
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+++ b/LinearReg/04-Logistic-Regression.ipynb
@@ -0,0 +1,847 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![header1](../fidle/img/00-Fidle-header-01.png)\n",
+    "\n",
+    "# Logistic regression, in pure tensorflow\n",
+    "Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.  \n",
+    "Note: This notebook use tensoflow 2 in compatibility mode 1.  \n",
+    "A good reason to use Keras ;-)\n",
+    "\n",
+    "## Objectives :\n",
+    "A logistic regression has the objective of providing a probability of belonging to a class.  \n",
+    "X contains characteristics  \n",
+    "y contains the probability of membership (1 or 0)  \n",
+    "\n",
+    "## Principe :\n",
+    "We'll look for a value of $\\theta$ such that the linear regression $\\theta^{T}X$ can be used to calculate our probability:  \n",
+    "\n",
+    "$\\hat{p} = h_\\theta(X) = \\sigma(\\theta^T{X})$  \n",
+    "\n",
+    "Where $\\sigma$ is the logit function, typically a sigmoid (S) function:  \n",
+    "\n",
+    "$\n",
+    "\\sigma(t) = \\dfrac{1}{1 + \\exp(-t)}\n",
+    "$  \n",
+    "\n",
+    "The predicted value $\\hat{y}$ will then be calculated as follows:\n",
+    "\n",
+    "$\n",
+    "\\hat{y} =\n",
+    "\\begin{cases}\n",
+    "  0 & \\text{if } \\hat{p} < 0.5 \\\\\n",
+    "  1 & \\text{if } \\hat{p} \\geq 0.5\n",
+    "\\end{cases}\n",
+    "$\n",
+    "\n",
+    "**Calculation of the cost of the regression:**  \n",
+    "For a training observation x, the cost can be calculated as follows:  \n",
+    "\n",
+    "$\n",
+    "c(\\theta) =\n",
+    "\\begin{cases}\n",
+    "  -\\log(\\hat{p}) & \\text{if } y = 1 \\\\\n",
+    "  -\\log(1 - \\hat{p}) & \\text{if } y = 0\n",
+    "\\end{cases}\n",
+    "$\n",
+    "\n",
+    "The regression cost function (log loss) over the whole training set can be written as follows:  \n",
+    "\n",
+    "$\n",
+    "J(\\theta) = -\\dfrac{1}{m} \\sum_{i=1}^{m}{\\left[ y^{(i)} log\\left(\\hat{p}^{(i)}\\right) + (1 - y^{(i)}) log\\left(1 - \\hat{p}^{(i)}\\right)\\right]}\n",
+    "$\n",
+    "## Step 1 - Import and init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/compat/v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "\n",
+       "div.warn {    \n",
+       "    background-color: #fcf2f2;\n",
+       "    border-color: #dFb5b4;\n",
+       "    border-left: 5px solid #dfb5b4;\n",
+       "    padding: 0.5em;\n",
+       "    font-weight: bold;\n",
+       "    font-size: 1.1em;;\n",
+       "    }\n",
+       "\n",
+       "\n",
+       "\n",
+       "div.nota {    \n",
+       "    background-color: #DAFFDE;\n",
+       "    border-left: 5px solid #92CC99;\n",
+       "    padding: 0.5em;\n",
+       "    }\n",
+       "\n",
+       "\n",
+       "\n",
+       "</style>\n",
+       "\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "FIDLE 2020 - Practical Work Module\n",
+      "Version              : 0.2.9\n",
+      "Run time             : Tuesday 18 February 2020, 21:34:05\n",
+      "TensorFlow version   : 2.0.0\n",
+      "Keras version        : 2.2.4-tf\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import sklearn as sl\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior()\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "import random\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append('..')\n",
+    "import fidle.pwk as ooo\n",
+    "\n",
+    "ooo.init()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1 - Usefull stuff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def vector_infos(name,V):\n",
+    "    '''Displaying some information about a vector'''\n",
+    "    with np.printoptions(precision=4, suppress=True):\n",
+    "        print(\"{:16} : ndim={}  shape={:10}  Mean = {}  Std = {}\".format( name,V.ndim, str(V.shape), V.mean(axis=0), V.std(axis=0)))\n",
+    "\n",
+    "def random_batch(X_train, y_train, batch_size):\n",
+    "    '''Returning a data set for a batch'''\n",
+    "    indices = np.random.randint(0, len(X_train), batch_size)\n",
+    "    X_batch = X_train[indices]\n",
+    "    y_batch = y_train[indices]\n",
+    "    return X_batch, y_batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2 - Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_size      = 1000       # Number of observations\n",
+    "data_cols      = 2          # observation size\n",
+    "data_noise     = 0.2\n",
+    "test_ratio     = 0.2        # Ratio of data reserved for validation\n",
+    "random_seed    = 123\n",
+    "\n",
+    "learning_rate  = 0.01\n",
+    "n_epochs       = 1000\n",
+    "batch_size     = 50\n",
+    "\n",
+    "epsilon        = 1e-7       # To avoid overflows on some calculations (log())\n",
+    "\n",
+    "learning_rate2 = 0.01       # Pour la version 2\n",
+    "n_epochs2      = 6000\n",
+    "batch_size2    = 50\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Data preparation\n",
+    "### 2.1 - Get some data\n",
+    "The data here are totally fabricated and represent the **examination results** (passed or failed) based on the students' **working** and **sleeping hours** .  \n",
+    "X=(working hours, sleeping hours) y={result} where result=0 (failed) or 1 (passed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def do_i_have_it(hours_of_work, hours_of_sleep):\n",
+    "    '''Returns the exam result based on work and sleep hours'''\n",
+    "    hours_of_sleep_min = 5\n",
+    "    hours_of_work_min  = 4\n",
+    "    hours_of_game_max  = 3\n",
+    "    # ---- Have to sleep and work\n",
+    "    if hours_of_sleep < hours_of_sleep_min: return 0\n",
+    "    if hours_of_work < hours_of_work_min:   return 0\n",
+    "    # ---- Gameboy is not good for you\n",
+    "    hours_of_game = 24 - 10 - hours_of_sleep - hours_of_work + random.gauss(0,0.4)\n",
+    "    if hours_of_game > hours_of_game_max:   return 0\n",
+    "    # ---- Fine, you got it\n",
+    "    return 1\n",
+    "\n",
+    "def make_students_dataset(size, noise):\n",
+    "    '''Fabrique un dataset pour <size> étudiants'''\n",
+    "    x = []\n",
+    "    y = []\n",
+    "    for i in range(size):\n",
+    "        w = random.gauss(5,1)\n",
+    "        s = random.gauss(7,1.5)\n",
+    "        r   = do_i_have_it(w,s)\n",
+    "        x.append([w,s])\n",
+    "        y.append(r)\n",
+    "    return (np.array(x), np.array(y))\n",
+    "\n",
+    "X_data,y_data=make_students_dataset(data_size,data_noise)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 - Show it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset X        : ndim=2  shape=(1000, 2)   Mean = [5.0192 6.9813]  Std = [1.0307 1.5238]\n",
+      "Dataset y        : ndim=1  shape=(1000,)     Mean = 0.64  Std = 0.48\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1)\n",
+    "fig.set_size_inches(8,6)\n",
+    "ax.plot(X_data[y_data == 1, 0], X_data[y_data == 1, 1], 'go', markersize=4, label=\"y=1 (positive)\")\n",
+    "ax.plot(X_data[y_data == 0, 0], X_data[y_data == 0, 1], 'ro', markersize=4, label=\"y=0 (negative)\")\n",
+    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+    "plt.xlabel('Hours of work')\n",
+    "plt.ylabel('Hours of sleep')\n",
+    "plt.show()\n",
+    "\n",
+    "vector_infos('Dataset X',X_data)\n",
+    "vector_infos('Dataset y',y_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 - Preparation of data\n",
+    "\n",
+    "We're going to:\n",
+    "- normalize the data\n",
+    "- add a column of 1 for bias\n",
+    "- Transform y_moons into a vector\n",
+    "- split the data to have : :\n",
+    "  - a training set\n",
+    "  - a test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_scaled         : ndim=2  shape=(1000, 2)   Mean = [-0.  0.]  Std = [1. 1.]\n",
+      "X_train          : ndim=2  shape=(800, 3)    Mean = [ 1.     -0.006  -0.0049]  Std = [0.     0.992  0.9893]\n",
+      "y_train          : ndim=2  shape=(800, 1)    Mean = [0.6338]  Std = [0.4818]\n",
+      "X_test           : ndim=2  shape=(200, 3)    Mean = [1.     0.0239 0.0197]  Std = [0.     1.0312 1.0415]\n",
+      "y_test           : ndim=2  shape=(200, 1)    Mean = [0.665]  Std = [0.472]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ----- Normalisation des données\n",
+    "scaler = sl.preprocessing.StandardScaler()\n",
+    "X_scaled   = scaler.fit_transform(X_data)\n",
+    "\n",
+    "# ----- Ajout de la colonne de 1\n",
+    "X_scaled_1 = np.c_[np.ones((data_size, 1)), X_scaled]\n",
+    "\n",
+    "# ----- Verticalisation de y_moons\n",
+    "y_data_v = y_data.reshape(-1,1)\n",
+    "\n",
+    "# ----- Partage des données\n",
+    "test_size = int(data_size * test_ratio)\n",
+    "X_train = X_scaled_1[:-test_size]\n",
+    "X_test  = X_scaled_1[-test_size:]\n",
+    "y_train = y_data_v[:-test_size]\n",
+    "y_test  = y_data_v[-test_size:]\n",
+    "\n",
+    "vector_infos('X_scaled',X_scaled)\n",
+    "vector_infos('X_train',X_train)\n",
+    "vector_infos('y_train',y_train)\n",
+    "vector_infos('X_test',X_test)\n",
+    "vector_infos('y_test',y_test)\n",
+    "\n",
+    "y_train_h = y_train.reshape(-1,) # nécessaire pour la visu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 - Have a look"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "#### Train data :"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "#### Test data :"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ooo.display_md(\"#### Train data :\")\n",
+    "fig, axs = plt.subplots()\n",
+    "fig.set_size_inches(8,6)\n",
+    "axs.plot(X_train[y_train_h == 1, 1], X_train[y_train_h == 1, 2], 'o', color='green', markersize=4, label=\"Train / Positifs\")\n",
+    "axs.plot(X_train[y_train_h == 0, 1], X_train[y_train_h == 0, 2], 'o', color='red',   markersize=4, label=\"Train / Négatifs\")\n",
+    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+    "plt.xlabel('Hours of work')\n",
+    "plt.ylabel('Hours of sleep')\n",
+    "plt.show()\n",
+    "\n",
+    "ooo.display_md(\"#### Test data :\")\n",
+    "fig, axs = plt.subplots()\n",
+    "fig.set_size_inches(8,6)\n",
+    "axs.plot(X_test[:, 1], X_test[:, 2], 'o',color='gray', markersize=4, label=\"A classer !\")\n",
+    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+    "plt.xlabel('Hours of work')\n",
+    "plt.ylabel('Hours of sleep')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Logistic model #1\n",
+    "### 3.1 - Build model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.reset_default_graph()\n",
+    "\n",
+    "X = tf.placeholder(tf.float32, shape=(None, data_cols + 1), name=\"X\")\n",
+    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
+    "\n",
+    "initializer = tf.random_uniform([data_cols + 1, 1], -1.0, 1.0, seed=random_seed)\n",
+    "theta = tf.Variable(initializer, name=\"theta\")\n",
+    "\n",
+    "logits = tf.matmul(X, theta, name=\"logits\")\n",
+    "\n",
+    "#y_proba = tf.sigmoid(logits)\n",
+    "y_proba = 1 / (1 + tf.exp(-logits))\n",
+    "\n",
+    "#loss = tf.losses.log_loss(y, y_proba)\n",
+    "loss = -tf.reduce_mean(y * tf.log(y_proba + epsilon) + (1 - y) * tf.log(1 - y_proba + epsilon))\n",
+    "\n",
+    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+    "training_op = optimizer.minimize(loss)\n",
+    "\n",
+    "init = tf.global_variables_initializer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 - Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tLoss: 1.3474569\n",
+      "Epoch: 100 \tLoss: 0.28945148\n",
+      "Epoch: 200 \tLoss: 0.2590584\n",
+      "Epoch: 300 \tLoss: 0.24997473\n",
+      "Epoch: 400 \tLoss: 0.24577877\n",
+      "Epoch: 500 \tLoss: 0.24390194\n",
+      "Epoch: 600 \tLoss: 0.24321868\n",
+      "Epoch: 700 \tLoss: 0.2436808\n",
+      "Epoch: 800 \tLoss: 0.24361208\n",
+      "Epoch: 900 \tLoss: 0.24339706\n",
+      "Epoch: 1000 \tLoss: 0.24412125\n"
+     ]
+    }
+   ],
+   "source": [
+    "nb_batches = int(np.ceil(data_size / batch_size))\n",
+    "\n",
+    "with tf.Session() as sess:\n",
+    "    sess.run(init)\n",
+    "\n",
+    "    for epoch in range(n_epochs+1):\n",
+    "        for batch_index in range(nb_batches):\n",
+    "            X_batch, y_batch = random_batch(X_train, y_train, batch_size)\n",
+    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
+    "            \n",
+    "        loss_val = loss.eval({X: X_test, y: y_test})\n",
+    "        \n",
+    "        if epoch % 100 == 0:\n",
+    "            print(\"Epoch:\", epoch, \"\\tLoss:\", loss_val)\n",
+    "\n",
+    "    y_proba_val = y_proba.eval(feed_dict={X: X_test, y: y_test})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### 3.3 - Evaluation\n",
+    "\n",
+    "Accuracy = Ability to avoid false positives = $\\frac{Tp}{Tp+Fp}$  \n",
+    "Recall = Ability to find the right positives = $\\frac{Tp}{Tp+Fn}$  \n",
+    "Avec :  \n",
+    "$T_p$ (true positive) Correct positive answer  \n",
+    "$F_p$ (false positive) False positive answer  \n",
+    "$T_n$ (true negative) Correct negative answer  \n",
+    "$F_n$ (false negative) Wrong negative answer  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy = 0.924    Recall = 0.910\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def show_results(y_proba_val):\n",
+    "    y_pred = (y_proba_val >= 0.5)\n",
+    "\n",
+    "    precision = metrics.precision_score(y_test, y_pred)\n",
+    "    recall    = metrics.recall_score(y_test, y_pred)\n",
+    "\n",
+    "    print(\"Accuracy = {:5.3f}    Recall = {:5.3f}\".format(precision, recall))\n",
+    "\n",
+    "    y_pred_1d = y_pred.reshape(-1)\n",
+    "    y_test_1d = y_test.reshape(-1)\n",
+    "\n",
+    "    X_pred_positives = X_test[ y_pred_1d == True]   # items prédits    positifs\n",
+    "    X_real_positives = X_test[ y_test_1d == 1 ]     # items réellement positifs\n",
+    "    X_pred_negatives = X_test[ y_pred_1d == False]  # items prédits    négatifs\n",
+    "    X_real_negatives = X_test[ y_test_1d == 0 ]     # items réellement négatifs\n",
+    "\n",
+    "    fig, axs = plt.subplots(2, 2)\n",
+    "    fig.subplots_adjust(wspace=.1,hspace=0.2)\n",
+    "    fig.set_size_inches(14,10)\n",
+    "    \n",
+    "    axs[0,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
+    "    axs[0,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green',      markersize=4,  label=\"Réels positifs\")\n",
+    "    axs[0,0].legend()\n",
+    "    axs[0,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+    "    axs[0,0].set_xlabel('$x_1$')\n",
+    "    axs[0,0].set_ylabel('$x_2$')\n",
+    "\n",
+    "\n",
+    "    axs[0,1].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
+    "    axs[0,1].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red',        markersize=4,  label=\"Réels négatifs\")\n",
+    "    axs[0,1].legend()\n",
+    "    axs[0,1].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+    "    axs[0,1].set_xlabel('$x_1$')\n",
+    "    axs[0,1].set_ylabel('$x_2$')\n",
+    "    \n",
+    "    axs[1,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
+    "    axs[1,0].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
+    "    axs[1,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green',      markersize=4,  label=\"Réels positifs\")\n",
+    "    axs[1,0].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red',        markersize=4,  label=\"Réels négatifs\")\n",
+    "    axs[1,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
+    "    axs[1,0].set_xlabel('$x_1$')\n",
+    "    axs[1,0].set_ylabel('$x_2$')\n",
+    "\n",
+    "    axs[1,1].pie([precision,1-precision], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
+    "                 autopct='%1.1f%%', shadow=False, startangle=70, colors=[\"lightsteelblue\",\"coral\"])\n",
+    "    axs[1,1].axis('equal')\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "show_results(y_proba_val)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Bending the space to a model #2 ;-)\n",
+    "\n",
+    "We're going to increase the characteristics of our observations, with : ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ et ${x_2}^3$  \n",
+    "\n",
+    "$\n",
+    "X=\n",
+    "\\begin{bmatrix}1 & x_{11} & x_{12} \\\\\n",
+    "\\vdots & \\dots\\\\\n",
+    "1 & x_{m1} & x_{m2}  \\end{bmatrix}\n",
+    "\\text{et }\n",
+    "X_{ng}=\\begin{bmatrix}1 & x_{11} & x_{12} & x_{11}^2 & x_{12}^2& x_{11}^3 & x_{12}^3 \\\\\n",
+    "\\vdots & & & \\dots \\\\\n",
+    "1 & x_{m1} & x_{m2} & x_{m1}^2 & x_{m2}^2& x_{m1}^3 & x_{m2}^3 \\end{bmatrix}\n",
+    "$\n",
+    "\n",
+    "Note : `sklearn.preprocessing.PolynomialFeatures` can do that for us, but we'll do it ourselves:\n",
+    "### 4.1 - Extend data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_enhanced = np.c_[X_train,\n",
+    "                         X_train[:, 1] ** 2,\n",
+    "                         X_train[:, 2] ** 2,\n",
+    "                         X_train[:, 1] ** 3,\n",
+    "                         X_train[:, 2] ** 3]\n",
+    "X_test_enhanced = np.c_[X_test,\n",
+    "                        X_test[:, 1] ** 2,\n",
+    "                        X_test[:, 2] ** 2,\n",
+    "                        X_test[:, 1] ** 3,\n",
+    "                        X_test[:, 2] ** 3]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 - A more readable version of our model. Yes it is.\n",
+    "...and with Tensorboard tracking and checkpoint recording."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def logistic_regression(X, y, initializer=None, seed=42, learning_rate=0.01):\n",
+    "\n",
+    "    n_inputs_including_bias = int(X.get_shape()[1])\n",
+    "    \n",
+    "    with tf.name_scope(\"logistic_regression\"):\n",
+    "        \n",
+    "        # ----- Construction du modèle\n",
+    "        with tf.name_scope(\"model\"):\n",
+    "            if initializer is None:\n",
+    "                initializer = tf.random_uniform([n_inputs_including_bias, 1], -1.0, 1.0, seed=seed)\n",
+    "            theta = tf.Variable(initializer, name=\"theta\")\n",
+    "            logits = tf.matmul(X, theta, name=\"logits\")\n",
+    "            y_proba = tf.sigmoid(logits)\n",
+    "            \n",
+    "        with tf.name_scope(\"train\"):\n",
+    "            loss = tf.losses.log_loss(y, y_proba, scope=\"loss\")\n",
+    "            optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate2)\n",
+    "            training_op = optimizer.minimize(loss)\n",
+    "            loss_summary = tf.summary.scalar('log_loss', loss)\n",
+    "            \n",
+    "        with tf.name_scope(\"init\"):\n",
+    "            init = tf.global_variables_initializer()\n",
+    "            \n",
+    "        with tf.name_scope(\"save\"):\n",
+    "            saver = tf.train.Saver(max_to_keep=4)\n",
+    "            \n",
+    "    return y_proba, loss, training_op, loss_summary, init, saver\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.3 - Build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.reset_default_graph()\n",
+    "\n",
+    "log_dir = './run/logs'\n",
+    "chk_dir = './run/models'\n",
+    "os.makedirs(log_dir, mode=0o750, exist_ok=True)\n",
+    "os.makedirs(chk_dir, mode=0o750, exist_ok=True)\n",
+    "\n",
+    "X = tf.placeholder(tf.float32, shape=(None, data_cols + 1 + 4), name=\"X\")\n",
+    "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
+    "\n",
+    "# Build model\n",
+    "y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(X, y)\n",
+    "\n",
+    "# Save model\n",
+    "file_writer = tf.summary.FileWriter(log_dir, tf.get_default_graph())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.4 - Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:      0  Loss:   0.7590    checkpoint: ./run/models/model-ckpt-0\n",
+      "Epoch:    500  Loss:   0.1350    checkpoint: ./run/models/model-ckpt-500\n",
+      "Epoch:   1000  Loss:   0.1152    checkpoint: ./run/models/model-ckpt-1000\n",
+      "Epoch:   1500  Loss:   0.1085    checkpoint: ./run/models/model-ckpt-1500\n",
+      "Epoch:   2000  Loss:   0.1035    checkpoint: ./run/models/model-ckpt-2000\n",
+      "WARNING:tensorflow:From /home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use standard file APIs to delete files with this prefix.\n",
+      "Epoch:   2500  Loss:   0.1015    checkpoint: ./run/models/model-ckpt-2500\n",
+      "Epoch:   3000  Loss:   0.0997    checkpoint: ./run/models/model-ckpt-3000\n",
+      "Epoch:   3500  Loss:   0.0986    checkpoint: ./run/models/model-ckpt-3500\n",
+      "Epoch:   4000  Loss:   0.0978    checkpoint: ./run/models/model-ckpt-4000\n",
+      "Epoch:   4500  Loss:   0.0979    checkpoint: ./run/models/model-ckpt-4500\n",
+      "Epoch:   5000  Loss:   0.0971    checkpoint: ./run/models/model-ckpt-5000\n",
+      "Epoch:   5500  Loss:   0.0970    checkpoint: ./run/models/model-ckpt-5500\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_batches = int(np.ceil(data_size / batch_size2))\n",
+    "\n",
+    "model_file  = chk_dir + \"/model-ckpt\"\n",
+    "model_final = chk_dir + \"/model-final\"\n",
+    "\n",
+    "with tf.Session() as sess:\n",
+    "    \n",
+    "    sess.run(init)\n",
+    "\n",
+    "    for epoch in range(n_epochs2):\n",
+    "        \n",
+    "        for batch_index in range(n_batches):\n",
+    "            # get a batch\n",
+    "            X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\n",
+    "            # train\n",
+    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
+    "        \n",
+    "        # Calculation of logistic loss and logs\n",
+    "        loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\n",
+    "        # Logging\n",
+    "        file_writer.add_summary(summary_str, epoch)\n",
+    "        \n",
+    "        if epoch % 500 == 0:\n",
+    "            print('Epoch: {:6d}  Loss: {:8.4f}    checkpoint: {}-{}'.format(epoch,loss_val,model_file,epoch))\n",
+    "            # Save checkpoint\n",
+    "            saver.save(sess, model_file, global_step=epoch)\n",
+    "\n",
+    "    # Save the final model\n",
+    "    saver.save(sess, model_final)\n",
+    "    # Evaluation with test data\n",
+    "    y_proba_val2 = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.5 - Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy = 0.977    Recall = 0.962\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_results(y_proba_val2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "![](../fidle/img/00-Fidle-logo-01_s.png)"
+   ]
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pres_numpy.ipynb b/Prerequisites/Numpy.ipynb
similarity index 80%
rename from pres_numpy.ipynb
rename to Prerequisites/Numpy.ipynb
index 9479872..9dc65ab 100644
--- a/pres_numpy.ipynb
+++ b/Prerequisites/Numpy.ipynb
@@ -8,9 +8,11 @@
     }
    },
    "source": [
+    "![header1](../fidle/img/00-Fidle-header-01.png)\n",
+    "\n",
     "# A short introduction to Numpy\n",
-    "Strongly inspired by the UGA Python Introduction Course\n",
-    "https://gricad-gitlab.univ-grenoble-alpes.fr/python-uga/py-training-2017"
+    "Strongly inspired by the UGA Python Introduction Course  \n",
+    "See : **https://gricad-gitlab.univ-grenoble-alpes.fr/python-uga/py-training-2017**"
    ]
   },
   {
@@ -21,14 +23,14 @@
     }
    },
    "source": [
-    "## A short introduction on NumPy\n",
+    "## Step 1 - Numpy the beginning\n",
     "\n",
     "Code using `numpy` usually starts with the import statement"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,9 +90,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -265,35 +265,29 @@
     }
    },
    "source": [
-    "# Manipulating NumPy arrays"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Access elements\n",
+    "## Step 2 - Access elements\n",
+    "\n",
     "Elements in a `numpy` array can be accessed using indexing and slicing in any dimension. It also offers the same functionalities available in Fortan or Matlab.\n",
     "\n",
-    "### Indexes and slices\n",
+    "### 2.1 - Indexes and slices\n",
     "For example, we can create an array `A` and perform any kind of selection operations on it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.89925962, 0.31519992, 0.17170063, 0.06102236, 0.6055506 ],\n",
-       "       [0.43365108, 0.67461267, 0.34962124, 0.75648088, 0.53096922],\n",
-       "       [0.65643503, 0.4723704 , 0.77202087, 0.50192904, 0.14067726],\n",
-       "       [0.80709755, 0.2314217 , 0.65465368, 0.28459125, 0.54727527]])"
+       "array([[0.37962202, 0.76975047, 0.73922844, 0.88922672, 0.99782046],\n",
+       "       [0.1551213 , 0.6275266 , 0.38079222, 0.35535393, 0.86290208],\n",
+       "       [0.03586265, 0.37427542, 0.67578338, 0.80472164, 0.21367888],\n",
+       "       [0.27197917, 0.08610806, 0.16481586, 0.72530108, 0.49244613]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -397,7 +391,7 @@
     }
    },
    "source": [
-    "### Using a mask to select elements validating a condition:"
+    "### 2.2 -  Using a mask to select elements validating a condition:"
    ]
   },
   {
@@ -474,8 +468,8 @@
     }
    },
    "source": [
-    "## Perform array manipulations\n",
-    "### Apply arithmetic operations to whole arrays (element-wise):"
+    "## Step 3 -  Perform array manipulations\n",
+    "### 3.1 - Apply arithmetic operations to whole arrays (element-wise):"
    ]
   },
   {
@@ -505,7 +499,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Apply functions element-wise:"
+    "### 3.2 - Apply functions element-wise:"
    ]
   },
   {
@@ -535,7 +529,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Setting parts of arrays"
+    "### 3.3 - Setting parts of arrays"
    ]
   },
   {
@@ -590,76 +584,95 @@
     }
    },
    "source": [
-    "### Attributes and methods of `np.ndarray` (see the [doc](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray))"
+    "## Step 4 - Attributes and methods of `np.ndarray` (see the [doc](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['T', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'getfield', 'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'prod', 'ptp', 'put', 'ravel', 'real', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tobytes', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'view']\n"
+      "T               all             any             argmax          argmin          argpartition    \n",
+      "argsort         astype          base            byteswap        choose          clip            \n",
+      "compress        conj            conjugate       copy            ctypes          cumprod         \n",
+      "cumsum          data            diagonal        dot             dtype           dump            \n",
+      "dumps           fill            flags           flat            flatten         getfield        \n",
+      "imag            item            itemset         itemsize        max             mean            \n",
+      "min             nbytes          ndim            newbyteorder    nonzero         partition       \n",
+      "prod            ptp             put             ravel           real            repeat          \n",
+      "reshape         resize          round           searchsorted    setfield        setflags        \n",
+      "shape           size            sort            squeeze         std             strides         \n",
+      "sum             swapaxes        take            tobytes         tofile          tolist          \n",
+      "tostring        trace           transpose       var             view            "
      ]
     }
    ],
    "source": [
-    "print([s for s in dir(A) if not s.startswith('__')])"
+    "for i,v in enumerate([s for s in dir(A) if not s.startswith('__')]):\n",
+    "    print(f'{v:16}', end='')\n",
+    "    if (i+1) % 6 == 0 :print('')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.          3.17258959  5.82409047 16.387435    1.65138967]\n",
-      " [ 0.          1.48233207  2.86023812  1.32191048  1.88334836]\n",
-      " [ 0.          2.11698277  1.29530177  1.99231351  7.10846954]\n",
-      " [ 0.          4.32111589  1.5275252   3.51381149  1.82723405]]\n",
-      "Mean value 2.9143043986324475\n",
-      "Mean line [0.         2.77325508 2.87678889 5.80386762 3.1176104 ]\n",
-      "Mean column [5.40710095 1.50956581 2.50261352 2.23793733]\n"
+      "[[0.37962202 0.76975047 0.73922844 0.88922672 0.99782046]\n",
+      " [0.1551213  0.6275266  0.38079222 0.35535393 0.86290208]\n",
+      " [0.03586265 0.37427542 0.67578338 0.80472164 0.21367888]\n",
+      " [0.27197917 0.08610806 0.16481586 0.72530108 0.49244613]]\n",
+      "Mean value 0.5001158248338762\n",
+      "Mean line [0.21064629 0.46441514 0.49015498 0.69365084 0.64171189]\n",
+      "Mean column [0.75512962 0.47633923 0.42086439 0.34813006]\n"
      ]
     }
    ],
    "source": [
+    "\n",
     "# Ex1: Get the mean through different dimensions\n",
+    "\n",
     "print(A)\n",
-    "print('Mean value', A.mean())\n",
-    "print('Mean line', A.mean(axis=0))\n",
+    "print('Mean value',  A.mean())\n",
+    "print('Mean line',   A.mean(axis=0))\n",
     "print('Mean column', A.mean(axis=1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.          3.17258959  5.82409047 16.387435    1.65138967]\n",
-      " [ 0.          1.48233207  2.86023812  1.32191048  1.88334836]\n",
-      " [ 0.          2.11698277  1.29530177  1.99231351  7.10846954]\n",
-      " [ 0.          4.32111589  1.5275252   3.51381149  1.82723405]] (4, 5)\n",
-      "[ 0.          3.17258959  5.82409047 16.387435    1.65138967  0.\n",
-      "  1.48233207  2.86023812  1.32191048  1.88334836  0.          2.11698277\n",
-      "  1.29530177  1.99231351  7.10846954  0.          4.32111589  1.5275252\n",
-      "  3.51381149  1.82723405] (20,)\n"
+      "[[0.37962202 0.76975047 0.73922844 0.88922672 0.99782046]\n",
+      " [0.1551213  0.6275266  0.38079222 0.35535393 0.86290208]\n",
+      " [0.03586265 0.37427542 0.67578338 0.80472164 0.21367888]\n",
+      " [0.27197917 0.08610806 0.16481586 0.72530108 0.49244613]]\n",
+      "(4, 5)\n",
+      "[0.37962202 0.76975047 0.73922844 0.88922672 0.99782046 0.1551213\n",
+      " 0.6275266  0.38079222 0.35535393 0.86290208 0.03586265 0.37427542\n",
+      " 0.67578338 0.80472164 0.21367888 0.27197917 0.08610806 0.16481586\n",
+      " 0.72530108 0.49244613] (20,)\n"
      ]
     }
    ],
    "source": [
+    "\n",
     "# Ex2: Convert a 2D array in 1D keeping all elements\n",
-    "print(A, A.shape)\n",
+    "\n",
+    "print(A)\n",
+    "print(A.shape)\n",
     "A_flat = A.flatten()\n",
     "print(A_flat, A_flat.shape)"
    ]
@@ -672,7 +685,7 @@
     }
    },
    "source": [
-    "### Remark: dot product"
+    "### 4.1 - Remark: dot product"
    ]
   },
   {
@@ -702,7 +715,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### For Matlab users\n",
+    "### 4.2 -  For Matlab users\n",
     "\n",
     "|     ` `       | Matlab | Numpy |\n",
     "| ------------- | ------ | ----- |\n",
@@ -725,7 +738,7 @@
     }
    },
    "source": [
-    "#### NumPy and SciPy sub-packages:\n",
+    "### 4.3 -  NumPy and SciPy sub-packages:\n",
     "\n",
     "We already saw `numpy.random` to generate `numpy` arrays filled with random values. This submodule also provides functions related to distributions (Poisson, gaussian, etc.) and permutations."
    ]
@@ -809,7 +822,7 @@
     }
    },
    "source": [
-    "## Introduction to Pandas: Python Data Analysis Library\n",
+    "### 4.4 -  Introduction to Pandas: Python Data Analysis Library\n",
     "\n",
     "Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for Python.\n",
     "\n",
@@ -817,6 +830,14 @@
     "[Grenoble Python Working Session](https://github.com/iutzeler/Pres_Pandas/)\n",
     "[Pandas for SQL Users](https://hackernoon.com/pandas-cheatsheet-for-sql-people-part-1-2976894acd0)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "![](../fidle/img/00-Fidle-logo-01_s.png)"
+   ]
   }
  ],
  "metadata": {
@@ -836,9 +857,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/README.md b/README.md
index e3dcebf..4c3043c 100644
--- a/README.md
+++ b/README.md
@@ -3,27 +3,27 @@
 
 ## A propos
 
-Ce dépot contient l'ensemble des documents et liens de la **formation Fidle**.  
-
-Les objectifs de cette formations, co-organisée par la formation continue du CNRS et les réseaux SARI et DEVLOG, sont :
- - Comprendre les **bases** des réseaux de neurones profonds (Deep Learning)
- - Développer une **première expérience** à travers des exemples simples et représentatifs
- - Comprendre les différents types de réseaux, leurs **architectures** et leurs **cas d'usages**
- - Appréhender les technologies **Tensorflow/Kera**s et **Jupyter lab**, sur GPU
- - Appréhender les **environnements de calcul académiques** tier-2 (méso) et/ou tier-1 (nationaux)
-
-## Disposibles dans ce dépot :
-Vous trouverez ici :
- - le support des présentations
- - l'ensemble des travaux pratiques, sous forme de notebooks Jupyter
- - des fiches et informations pratiques :
+This repository contains all the documents and links of the **Fidle Training**.  
+
+The objectives of this training, co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks, are :
+ - Understanding the **bases** of deep learning neural networks (Deep Learning)
+ - Develop a **first experience** through simple and representative examples
+ - Understand the different types of networks, their **architectures** and their **use cases**.
+ - Understanding Tensorflow/Kera**s and Jupyter lab** technologies on the GPU
+ - Apprehend the **academic computing environments** Tier-2 (meso) and/or Tier-1 (national)
+
+## Available at this depot:
+You will find here :
+ - the support of the presentations
+ - all the practical work, in the form of Jupyter notebooks
+ - sheets and practical information :
    - **[Configuration SSH](../-/wikis/howto-ssh)**
 
 
 
-## Récupération de ce dépot et installation
+## Installation
 To run this examples, you need an environment with the following packages :
- - Python 3.6
+ - Python >3.5
  - numpy
  - Tensorflow 2.0
  - scikit-image
@@ -41,5 +41,9 @@ To manage conda environment see [there](https://docs.conda.io/projects/conda/en/
 
 
 
-## Misc
-...
+## Licence
+
+\[en\] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0)  
+\[Fr\] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International
+See [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).  
+See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#).
\ No newline at end of file
diff --git a/VAE/06-VAE-withCelebA-post.ipynb b/VAE/06-VAE-withCelebA-post.ipynb
index 0ad2224..6dcc958 100644
--- a/VAE/06-VAE-withCelebA-post.ipynb
+++ b/VAE/06-VAE-withCelebA-post.ipynb
@@ -723,7 +723,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
-- 
GitLab