Add Wine regression (v2.2.1)

e9c99429 · Jean-Luc Parouty · f6e9925e · e9c99429 · e9c99429 · e9c99429
Commit e9c99429 authored 2 years ago by Jean-Luc Parouty
--- a/BHPD/03-DNN-Wine-Regression.ipynb
+++ b/BHPD/03-DNN-Wine-Regression.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [WINE1] - Wine quality prediction with a Dense Network (DNN)\n",
+    "  <!-- DESC -->  Another example of regression, with a wine quality prediction!\n",
+    "  <!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Predict the **quality of wines**, based on their analysis\n",
+    " - Understanding the principle and the architecture of a regression with a dense neural network with backup and restore of the trained model. \n",
+    "\n",
+    "The **[Wine Quality datasets](https://archive.ics.uci.edu/ml/datasets/wine+Quality)** are made up of analyses of a large number of wines, with an associated quality (between 0 and 10)  \n",
+    "This dataset is provide by :  \n",
+    "Paulo Cortez, University of Minho, Guimarães, Portugal, http://www3.dsi.uminho.pt/pcortez  \n",
+    "A. Cerdeira, F. Almeida, T. Matos and J. Reis, Viticulture Commission of the Vinho Verde Region(CVRVV), Porto, Portugal, @2009  \n",
+    "This dataset can be retreive at [University of California Irvine (UCI)](https://archive-beta.ics.uci.edu/ml/datasets/wine+quality)\n",
+    "\n",
+    "\n",
+    "Due to privacy and logistic issues, only physicochemical and sensory variables are available  \n",
+    "There is no data about grape types, wine brand, wine selling price, etc.\n",
+    "\n",
+    "- fixed acidity\n",
+    "- volatile acidity\n",
+    "- citric acid\n",
+    "- residual sugar\n",
+    "- chlorides\n",
+    "- free sulfur dioxide\n",
+    "- total sulfur dioxide\n",
+    "- density\n",
+    "- pH\n",
+    "- sulphates\n",
+    "- alcohol\n",
+    "- quality (score between 0 and 10)\n",
+    "\n",
+    "## What we're going to do :\n",
+    "\n",
+    " - (Retrieve data)\n",
+    " - (Preparing the data)\n",
+    " - (Build a model)\n",
+    " - Train and save the model\n",
+    " - Restore saved model\n",
+    " - Evaluate the model\n",
+    " - Make some predictions\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Import and init\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import os\n",
+    "# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import os,sys\n",
+    "\n",
+    "from IPython.display import Markdown\n",
+    "from importlib import reload\n",
+    "\n",
+    "import fidle\n",
+    "\n",
+    "# Init Fidle environment\n",
+    "run_id, run_dir, datasets_dir = fidle.init('WINE1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Verbosity during training : \n",
+    "- 0 = silent\n",
+    "- 1 = progress bar\n",
+    "- 2 = one line per epoch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_verbosity = 1\n",
+    "dataset_name  = 'winequality-red.csv'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Override parameters (batch mode) - Just forget this cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fidle.override('fit_verbosity', 'dataset_name')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Retrieve data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(f'{datasets_dir}/WineQuality/origine/{dataset_name}', header=0,sep=';')\n",
+    "\n",
+    "display(data.head(5).style.format(\"{0:.2f}\"))\n",
+    "print('Missing Data : ',data.isna().sum().sum(), '  Shape is : ', data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Preparing the data\n",
+    "### 3.1 - Split data\n",
+    "We will use 80% of the data for training and 20% for validation.  \n",
+    "x will be the data of the analysis and y the quality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Split => train, test\n",
+    "#\n",
+    "data       = data.sample(frac=1., axis=0)     # Shuffle\n",
+    "data_train = data.sample(frac=0.8, axis=0)    # get 80 %\n",
+    "data_test  = data.drop(data_train.index)      # test = all - train\n",
+    "\n",
+    "# ---- Split => x,y (medv is price)\n",
+    "#\n",
+    "x_train = data_train.drop('quality',  axis=1)\n",
+    "y_train = data_train['quality']\n",
+    "x_test  = data_test.drop('quality',   axis=1)\n",
+    "y_test  = data_test['quality']\n",
+    "\n",
+    "print('Original data shape was : ',data.shape)\n",
+    "print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)\n",
+    "print('x_test  : ',x_test.shape,  'y_test  : ',y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 - Data normalization\n",
+    "**Note :** \n",
+    " - All input data must be normalized, train and test.  \n",
+    " - To do this we will subtract the mean and divide by the standard deviation.  \n",
+    " - But test data should not be used in any way, even for normalization.  \n",
+    " - The mean and the standard deviation will therefore only be calculated with the train data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"Before normalization :\"))\n",
+    "\n",
+    "mean = x_train.mean()\n",
+    "std  = x_train.std()\n",
+    "x_train = (x_train - mean) / std\n",
+    "x_test  = (x_test  - mean) / std\n",
+    "\n",
+    "display(x_train.describe().style.format(\"{0:.2f}\").set_caption(\"After normalization :\"))\n",
+    "\n",
+    "# Convert ou DataFrame to numpy array\n",
+    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
+    "x_test,  y_test  = np.array(x_test),  np.array(y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Build a model\n",
+    "More informations about : \n",
+    " - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
+    " - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
+    " - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
+    " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_model_v1(shape):\n",
+    "  \n",
+    "  model = keras.models.Sequential()\n",
+    "  model.add(keras.layers.Input(shape, name=\"InputLayer\"))\n",
+    "  model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))\n",
+    "  model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))\n",
+    "  model.add(keras.layers.Dense(1, name='Output'))\n",
+    "\n",
+    "  model.compile(optimizer = 'rmsprop',\n",
+    "                loss      = 'mse',\n",
+    "                metrics   = ['mae', 'mse'] )\n",
+    "  return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5 - Train the model\n",
+    "### 5.1 - Get it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model=get_model_v1( (11,) )\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2 - Add callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.makedirs('./run/models',   mode=0o750, exist_ok=True)\n",
+    "save_dir = \"./run/models/best_model.h5\"\n",
+    "\n",
+    "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.3 - Train it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history = model.fit(x_train,\n",
+    "                    y_train,\n",
+    "                    epochs          = 100,\n",
+    "                    batch_size      = 10,\n",
+    "                    verbose         = fit_verbosity,\n",
+    "                    validation_data = (x_test, y_test),\n",
+    "                    callbacks       = [savemodel_callback])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - Evaluate\n",
+    "### 6.1 - Model evaluation\n",
+    "MAE =  Mean Absolute Error (between the labels and predictions)  \n",
+    "A mae equal to 3 represents an average error in prediction of $3k."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score = model.evaluate(x_test, y_test, verbose=0)\n",
+    "\n",
+    "print('x_test / loss      : {:5.4f}'.format(score[0]))\n",
+    "print('x_test / mae       : {:5.4f}'.format(score[1]))\n",
+    "print('x_test / mse       : {:5.4f}'.format(score[2]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.2 - Training history\n",
+    "What was the best result during our training ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"min( val_mae ) : {:.4f}\".format( min(history.history[\"val_mae\"]) ) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fidle.scrawler.history( history, plot={'MSE' :['mse', 'val_mse'],\n",
+    "                        'MAE' :['mae', 'val_mae'],\n",
+    "                        'LOSS':['loss','val_loss']}, save_as='01-history')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7 - Restore a model :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.1 - Reload model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')\n",
+    "loaded_model.summary()\n",
+    "print(\"Loaded.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.2 - Evaluate it :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
+    "\n",
+    "print('x_test / loss      : {:5.4f}'.format(score[0]))\n",
+    "print('x_test / mae       : {:5.4f}'.format(score[1]))\n",
+    "print('x_test / mse       : {:5.4f}'.format(score[2]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.3 - Make a prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Pick n entries from our test set\n",
+    "n = 200\n",
+    "ii = np.random.randint(1,len(x_test),n)\n",
+    "x_sample = x_test[ii]\n",
+    "y_sample = y_test[ii]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Make a predictions\n",
+    "y_pred = loaded_model.predict( x_sample, verbose=2 )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---- Show it\n",
+    "print('Wine    Prediction   Real   Delta')\n",
+    "for i in range(n):\n",
+    "    pred   = y_pred[i][0]\n",
+    "    real   = y_sample[i]\n",
+    "    delta  = real-pred\n",
+    "    print(f'{i:03d}        {pred:.2f}       {real}      {delta:+.2f} ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fidle.end()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.2 ('fidle-env')",
+   "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": "3.9.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
+%% Cell type:markdown id: tags:
+
+<img width="800px" src="../fidle/img/00-Fidle-header-01.svg"></img>
+
+# <!-- TITLE --> [WINE1] - Wine quality prediction with a Dense Network (DNN)
+  <!-- DESC -->  Another example of regression, with a wine quality prediction!
+  <!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
+
+## Objectives :
+ - Predict the **quality of wines**, based on their analysis
+ - Understanding the principle and the architecture of a regression with a dense neural network with backup and restore of the trained model.
+
+The **[Wine Quality datasets](https://archive.ics.uci.edu/ml/datasets/wine+Quality)** are made up of analyses of a large number of wines, with an associated quality (between 0 and 10)
+This dataset is provide by :
+Paulo Cortez, University of Minho, Guimarães, Portugal, http://www3.dsi.uminho.pt/pcortez
+A. Cerdeira, F. Almeida, T. Matos and J. Reis, Viticulture Commission of the Vinho Verde Region(CVRVV), Porto, Portugal, @2009
+This dataset can be retreive at [University of California Irvine (UCI)](https://archive-beta.ics.uci.edu/ml/datasets/wine+quality)
+
+
+Due to privacy and logistic issues, only physicochemical and sensory variables are available
+There is no data about grape types, wine brand, wine selling price, etc.
+
+- fixed acidity
+- volatile acidity
+- citric acid
+- residual sugar
+- chlorides
+- free sulfur dioxide
+- total sulfur dioxide
+- density
+- pH
+- sulphates
+- alcohol
+- quality (score between 0 and 10)
+
+## What we're going to do :
+
+ - (Retrieve data)
+ - (Preparing the data)
+ - (Build a model)
+ - Train and save the model
+ - Restore saved model
+ - Evaluate the model
+ - Make some predictions
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Import and init
+
+%% Cell type:code id: tags:
+
+``` python
+# import os
+# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+import tensorflow as tf
+from tensorflow import keras
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import os,sys
+
+from IPython.display import Markdown
+from importlib import reload
+
+import fidle
+
+# Init Fidle environment
+run_id, run_dir, datasets_dir = fidle.init('WINE1')
+```
+
+%% Cell type:markdown id: tags:
+
+Verbosity during training :
+- 0 = silent
+- 1 = progress bar
+- 2 = one line per epoch
+
+%% Cell type:code id: tags:
+
+``` python
+fit_verbosity = 1
+dataset_name  = 'winequality-red.csv'
+```
+
+%% Cell type:markdown id: tags:
+
+Override parameters (batch mode) - Just forget this cell
+
+%% Cell type:code id: tags:
+
+``` python
+fidle.override('fit_verbosity', 'dataset_name')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Retrieve data
+
+%% Cell type:code id: tags:
+
+``` python
+data = pd.read_csv(f'{datasets_dir}/WineQuality/origine/{dataset_name}', header=0,sep=';')
+
+display(data.head(5).style.format("{0:.2f}"))
+print('Missing Data : ',data.isna().sum().sum(), '  Shape is : ', data.shape)
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 3 - Preparing the data
+### 3.1 - Split data
+We will use 80% of the data for training and 20% for validation.
+x will be the data of the analysis and y the quality
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Split => train, test
+#
+data       = data.sample(frac=1., axis=0)     # Shuffle
+data_train = data.sample(frac=0.8, axis=0)    # get 80 %
+data_test  = data.drop(data_train.index)      # test = all - train
+
+# ---- Split => x,y (medv is price)
+#
+x_train = data_train.drop('quality',  axis=1)
+y_train = data_train['quality']
+x_test  = data_test.drop('quality',   axis=1)
+y_test  = data_test['quality']
+
+print('Original data shape was : ',data.shape)
+print('x_train : ',x_train.shape, 'y_train : ',y_train.shape)
+print('x_test  : ',x_test.shape,  'y_test  : ',y_test.shape)
+```
+
+%% Cell type:markdown id: tags:
+
+### 3.2 - Data normalization
+**Note :**
+ - All input data must be normalized, train and test.
+ - To do this we will subtract the mean and divide by the standard deviation.
+ - But test data should not be used in any way, even for normalization.
+ - The mean and the standard deviation will therefore only be calculated with the train data.
+
+%% Cell type:code id: tags:
+
+``` python
+display(x_train.describe().style.format("{0:.2f}").set_caption("Before normalization :"))
+
+mean = x_train.mean()
+std  = x_train.std()
+x_train = (x_train - mean) / std
+x_test  = (x_test  - mean) / std
+
+display(x_train.describe().style.format("{0:.2f}").set_caption("After normalization :"))
+
+# Convert ou DataFrame to numpy array
+x_train, y_train = np.array(x_train), np.array(y_train)
+x_test,  y_test  = np.array(x_test),  np.array(y_test)
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 4 - Build a model
+More informations about :
+ - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)
+ - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)
+ - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)
+ - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)
+
+%% Cell type:code id: tags:
+
+``` python
+def get_model_v1(shape):
+
+  model = keras.models.Sequential()
+  model.add(keras.layers.Input(shape, name="InputLayer"))
+  model.add(keras.layers.Dense(64, activation='relu', name='Dense_n1'))
+  model.add(keras.layers.Dense(64, activation='relu', name='Dense_n2'))
+  model.add(keras.layers.Dense(1, name='Output'))
+
+  model.compile(optimizer = 'rmsprop',
+                loss      = 'mse',
+                metrics   = ['mae', 'mse'] )
+  return model
+```
+
+%% Cell type:markdown id: tags:
+
+## 5 - Train the model
+### 5.1 - Get it
+
+%% Cell type:code id: tags:
+
+``` python
+model=get_model_v1( (11,) )
+
+model.summary()
+```
+
+%% Cell type:markdown id: tags:
+
+### 5.2 - Add callback
+
+%% Cell type:code id: tags:
+
+``` python
+os.makedirs('./run/models',   mode=0o750, exist_ok=True)
+save_dir = "./run/models/best_model.h5"
+
+savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)
+```
+
+%% Cell type:markdown id: tags:
+
+### 5.3 - Train it
+
+%% Cell type:code id: tags:
+
+``` python
+history = model.fit(x_train,
+                    y_train,
+                    epochs          = 100,
+                    batch_size      = 10,
+                    verbose         = fit_verbosity,
+                    validation_data = (x_test, y_test),
+                    callbacks       = [savemodel_callback])
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 6 - Evaluate
+### 6.1 - Model evaluation
+MAE =  Mean Absolute Error (between the labels and predictions)
+A mae equal to 3 represents an average error in prediction of $3k.
+
+%% Cell type:code id: tags:
+
+``` python
+score = model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / mae       : {:5.4f}'.format(score[1]))
+print('x_test / mse       : {:5.4f}'.format(score[2]))
+```
+
+%% Cell type:markdown id: tags:
+
+### 6.2 - Training history
+What was the best result during our training ?
+
+%% Cell type:code id: tags:
+
+``` python
+print("min( val_mae ) : {:.4f}".format( min(history.history["val_mae"]) ) )
+```
+
+%% Cell type:code id: tags:
+
+``` python
+fidle.scrawler.history( history, plot={'MSE' :['mse', 'val_mse'],
+                        'MAE' :['mae', 'val_mae'],
+                        'LOSS':['loss','val_loss']}, save_as='01-history')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 7 - Restore a model :
+
+%% Cell type:markdown id: tags:
+
+### 7.1 - Reload model
+
+%% Cell type:code id: tags:
+
+``` python
+loaded_model = tf.keras.models.load_model('./run/models/best_model.h5')
+loaded_model.summary()
+print("Loaded.")
+```
+
+%% Cell type:markdown id: tags:
+
+### 7.2 - Evaluate it :
+
+%% Cell type:code id: tags:
+
+``` python
+score = loaded_model.evaluate(x_test, y_test, verbose=0)
+
+print('x_test / loss      : {:5.4f}'.format(score[0]))
+print('x_test / mae       : {:5.4f}'.format(score[1]))
+print('x_test / mse       : {:5.4f}'.format(score[2]))
+```
+
+%% Cell type:markdown id: tags:
+
+### 7.3 - Make a prediction
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Pick n entries from our test set
+n = 200
+ii = np.random.randint(1,len(x_test),n)
+x_sample = x_test[ii]
+y_sample = y_test[ii]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Make a predictions
+y_pred = loaded_model.predict( x_sample, verbose=2 )
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# ---- Show it
+print('Wine    Prediction   Real   Delta')
+for i in range(n):
+    pred   = y_pred[i][0]
+    real   = y_sample[i]
+    delta  = real-pred
+    print(f'{i:03d}        {pred:.2f}       {real}      {delta:+.2f} ')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+fidle.end()
+```
+
+%% Cell type:markdown id: tags:
+
+---
+<img width="80px" src="../fidle/img/00-Fidle-logo-01.svg"></img>
--- a/Misc/Using-pandas.ipynb
+++ b/Misc/Using-pandas.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+    "\n",
+    "# <!-- TITLE --> [PANDAS1] - Quelques exemples avec Pandas\n",
+    "<!-- DESC --> pandas is another essential tool for the Scientific Python.\n",
+    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+    "\n",
+    "## Objectives :\n",
+    " - Understand how to slice a dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - A little cooking with datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy  as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get some data\n",
+    "a = np.arange(50).reshape(10,5)\n",
+    "print('Starting data: \\n',a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a DataFrame\n",
+    "df_all = pd.DataFrame(a, columns=['A','B','C','D','E'])\n",
+    "print('\\nDataFrame :')\n",
+    "display(df_all)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Shuffle data\n",
+    "df_all = df_all.sample(frac=1, axis=0)\n",
+    "print('\\nDataFrame randomly shuffled :')\n",
+    "display(df_all)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get a train part\n",
+    "df_train = df_all.sample(frac=0.8, axis=0)\n",
+    "print('\\nTrain set (80%) :')\n",
+    "display(df_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Get test set as all - train\n",
+    "df_test = df_all.drop(df_train.index)\n",
+    "print('\\nTest set (all - train) :')\n",
+    "display(df_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = df_train.drop('E',  axis=1)\n",
+    "y_train = df_train['E']\n",
+    "x_test  = df_test.drop('E',   axis=1)\n",
+    "y_test  = df_test['E']\n",
+    "display(x_train)\n",
+    "display(y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.2 ('fidle-env')",
+   "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": "3.9.2"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:markdown id: tags:
+
+<img width="800px" src="../fidle/img/00-Fidle-header-01.svg"></img>
+
+# <!-- TITLE --> [PANDAS1] - Quelques exemples avec Pandas
+<!-- DESC --> pandas is another essential tool for the Scientific Python.
+<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->
+
+## Objectives :
+ - Understand how to slice a dataset
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - A little cooking with datasets
+
+%% Cell type:code id: tags:
+
+``` python
+import pandas as pd
+import numpy  as np
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Get some data
+a = np.arange(50).reshape(10,5)
+print('Starting data: \n',a)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Create a DataFrame
+df_all = pd.DataFrame(a, columns=['A','B','C','D','E'])
+print('\nDataFrame :')
+display(df_all)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Shuffle data
+df_all = df_all.sample(frac=1, axis=0)
+print('\nDataFrame randomly shuffled :')
+display(df_all)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Get a train part
+df_train = df_all.sample(frac=0.8, axis=0)
+print('\nTrain set (80%) :')
+display(df_train)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+
+# Get test set as all - train
+df_test = df_all.drop(df_train.index)
+print('\nTest set (all - train) :')
+display(df_test)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+x_train = df_train.drop('E',  axis=1)
+y_train = df_train['E']
+x_test  = df_test.drop('E',   axis=1)
+y_test  = df_test['E']
+display(x_train)
+display(y_train)
+```
+
+%% Cell type:markdown id: tags:
+
+---
+<img width="80px" src="../fidle/img/00-Fidle-logo-01.svg"></img>
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
  {
   "cell_type": "code",
   "execution_count": 1,
-   "id": "a097c5d3",
+   "id": "c0502242",
   "metadata": {
    "execution": {
-     "iopub.execute_input": "2022-10-13T16:40:09.834885Z",
-     "iopub.status.busy": "2022-10-13T16:40:09.834097Z",
-     "iopub.status.idle": "2022-10-13T16:40:09.845618Z",
-     "shell.execute_reply": "2022-10-13T16:40:09.844753Z"
+     "iopub.execute_input": "2022-10-16T19:33:52.178836Z",
+     "iopub.status.busy": "2022-10-16T19:33:52.178369Z",
+     "iopub.status.idle": "2022-10-16T19:33:52.190555Z",
+     "shell.execute_reply": "2022-10-16T19:33:52.189704Z"
    },
    "jupyter": {
     "source_hidden": true
@@ -52,7 +52,7 @@
       "For more information, you can contact us at :  \n",
       "[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top)\n",
       "\n",
-       "Current Version : <!-- VERSION_BEGIN -->2.2.0<!-- VERSION_END -->\n",
+       "Current Version : <!-- VERSION_BEGIN -->2.2.1<!-- VERSION_END -->\n",
       "\n",
       "\n",
       "## Course materials\n",
@@ -67,7 +67,7 @@
       "## Jupyter notebooks\n",
       "\n",
       "<!-- TOC_BEGIN -->\n",
-       "<!-- Automatically generated on : 13/10/22 18:40:08 -->\n",
+       "<!-- Automatically generated on : 16/10/22 21:33:51 -->\n",
       "\n",
       "### Linear and logistic regression\n",
       "- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)  \n",
@@ -88,6 +88,8 @@
       "Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)\n",
       "- **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)  \n",
       "A more advanced implementation of the precedent example\n",
+       "- **[WINE1](BHPD/03-DNN-Wine-Regression.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](BHPD/03-DNN-Wine-Regression.ipynb)  \n",
+       "Another example of regression, with a wine quality prediction!\n",
       "\n",
       "### Basic classification using a DN\n",
       "- **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)  \n",
@@ -198,6 +200,8 @@
       "A scratchbook for small examples\n",
       "- **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)  \n",
       "4 ways to use Tensorboard from the Jupyter environment\n",
+       "- **[PANDAS1](Misc/Using-pandas.ipynb)** - [Quelques exemples avec Pandas](Misc/Using-pandas.ipynb)  \n",
+       "pandas is another essential tool for the Scientific Python.\n",
       "<!-- TOC_END -->\n",
       "\n",
       "\n",
@@ -229,7 +233,7 @@
    "from IPython.display import display,Markdown\n",
    "display(Markdown(open('README.md', 'r').read()))\n",
    "#\n",
-    "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 13/10/22 18:40:09"
+    "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 16/10/22 21:33:51"
   ]
  }
 ],

-%% Cell type:code id:a097c5d3 tags:
+%% Cell type:code id:c0502242 tags:

 ``` python
 from IPython.display import display,Markdown
 display(Markdown(open('README.md', 'r').read()))
 #
-# This README is visible under Jupiter Lab ;-)# Automatically generated on : 13/10/22 18:40:09
+# This README is visible under Jupiter Lab ;-)# Automatically generated on : 16/10/22 21:33:51
 ```

 %% Output

    <a name="top"></a>
    
    [<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#top)
    
    <!-- --------------------------------------------------- -->
    <!-- To correctly view this README under Jupyter Lab     -->
    <!-- Open the notebook: README.ipynb!                    -->
    <!-- --------------------------------------------------- -->
    
    ## About Fidle
    
    This repository contains all the documents and links of the **Fidle Training** .
    Fidle (for Formation Introduction au Deep Learning) is a 3-day training session co-organized
    by the 3IA MIAI institute, the CNRS, via the Mission for Transversal and Interdisciplinary
    Initiatives (MITI) and the University of Grenoble Alpes (UGA).
    
    The objectives of this training are :
     - Understanding the **bases of Deep Learning** neural networks
     - Develop a **first experience** through simple and representative examples
     - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
     - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
    
    For more information, see **https://fidle.cnrs.fr** :
    - **[Fidle site](https://fidle.cnrs.fr)**
    - **[Presentation of the training](https://fidle.cnrs.fr/presentation)**
    - **[Program 2021/2022](https://fidle.cnrs.fr/programme)**
    - [Subscribe to the list](https://fidle.cnrs.fr/listeinfo), to stay informed !
    - [Find us on youtube](https://fidle.cnrs.fr/youtube)
    - [Corrected notebooks](https://fidle.cnrs.fr/done)
    
    For more information, you can contact us at :
    [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
    
-    Current Version : <!-- VERSION_BEGIN -->2.2.0<!-- VERSION_END -->
+    Current Version : <!-- VERSION_BEGIN -->2.2.1<!-- VERSION_END -->
    
    
    ## Course materials
    
    | | | | |
    |:--:|:--:|:--:|:--:|
    | **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img><br>Course slides](https://fidle.cnrs.fr/supports)**<br>The course in pdf format<br>(12 Mo)| **[<img width="50px" src="fidle/img/00-Notebooks.svg"></img><br>Notebooks](https://fidle.cnrs.fr/notebooks)**<br> &nbsp;&nbsp;&nbsp;&nbsp;Get a Zip or clone this repository &nbsp;&nbsp;&nbsp;&nbsp;<br>(40 Mo)| **[<img width="50px" src="fidle/img/00-Datasets-tar.svg"></img><br>Datasets](https://fidle.cnrs.fr/fidle-datasets.tar)**<br>All the needed datasets<br>(1.2 Go)|**[<img width="50px" src="fidle/img/00-Videos.svg"></img><br>Videos](https://fidle.cnrs.fr/youtube)**<br>&nbsp;&nbsp;&nbsp;&nbsp;Our Youtube channel&nbsp;&nbsp;&nbsp;&nbsp;<br>&nbsp;|
    
    Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
    
    
    ## Jupyter notebooks
    
    <!-- TOC_BEGIN -->
-    <!-- Automatically generated on : 13/10/22 18:40:08 -->
+    <!-- Automatically generated on : 16/10/22 21:33:51 -->
    
    ### Linear and logistic regression
    - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
    Low-level implementation, using numpy, of a direct resolution for a linear regression
    - **[GRAD1](LinearReg/02-Gradient-descent.ipynb)** - [Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)
    Low level implementation of a solution by gradient descent. Basic and stochastic approach.
    - **[POLR1](LinearReg/03-Polynomial-Regression.ipynb)** - [Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
    Illustration of the problem of complexity with the polynomial regression
    - **[LOGR1](LinearReg/04-Logistic-Regression.ipynb)** - [Logistic regression](LinearReg/04-Logistic-Regression.ipynb)
    Simple example of logistic regression with a sklearn solution
    
    ### Perceptron Model 1957
    - **[PER57](IRIS/01-Simple-Perceptron.ipynb)** - [Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)
    Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !
    
    ### Basic regression using DN
    - **[BHPD1](BHPD/01-DNN-Regression.ipynb)** - [Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)
    Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
    - **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)
    A more advanced implementation of the precedent example
+    - **[WINE1](BHPD/03-DNN-Wine-Regression.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](BHPD/03-DNN-Wine-Regression.ipynb)
+    Another example of regression, with a wine quality prediction!
    
    ### Basic classification using a DN
    - **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)
    An example of classification using a dense neural network for the famous MNIST dataset
    - **[MNIST2](MNIST/02-CNN-MNIST.ipynb)** - [Simple classification with CNN](MNIST/02-CNN-MNIST.ipynb)
    An example of classification using a convolutional neural network for the famous MNIST dataset
    
    ### Images classification with Convolutional Neural Networks (CNN
    - **[GTSRB1](GTSRB/01-Preparation-of-data.ipynb)** - [Dataset analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
    Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
    - **[GTSRB2](GTSRB/02-First-convolutions.ipynb)** - [First convolutions](GTSRB/02-First-convolutions.ipynb)
    Episode 2 : First convolutions and first classification of our traffic signs
    - **[GTSRB3](GTSRB/03-Tracking-and-visualizing.ipynb)** - [Training monitoring](GTSRB/03-Tracking-and-visualizing.ipynb)
    Episode 3 : Monitoring, analysis and check points during a training session
    - **[GTSRB4](GTSRB/04-Data-augmentation.ipynb)** - [Data augmentation ](GTSRB/04-Data-augmentation.ipynb)
    Episode 4 : Adding data by data augmentation when we lack it, to improve our results
    - **[GTSRB5](GTSRB/05-Full-convolutions.ipynb)** - [Full convolutions](GTSRB/05-Full-convolutions.ipynb)
    Episode 5 : A lot of models, a lot of datasets and a lot of results.
    - **[GTSRB6](GTSRB/06-Notebook-as-a-batch.ipynb)** - [Full convolutions as a batch](GTSRB/06-Notebook-as-a-batch.ipynb)
    Episode 6 : To compute bigger, use your notebook in batch mode
    - **[GTSRB7](GTSRB/07-Show-report.ipynb)** - [Batch reports](GTSRB/07-Show-report.ipynb)
    Episode 7 : Displaying our jobs report, and the winner is...
    - **[GTSRB10](GTSRB/batch_oar.sh)** - [OAR batch script submission](GTSRB/batch_oar.sh)
    Bash script for an OAR batch submission of an ipython code
    - **[GTSRB11](GTSRB/batch_slurm.sh)** - [SLURM batch script](GTSRB/batch_slurm.sh)
    Bash script for a Slurm batch submission of an ipython code
    
    ### Sentiment analysis with word embeddin
    - **[IMDB1](IMDB/01-One-hot-encoding.ipynb)** - [Sentiment analysis with hot-one encoding](IMDB/01-One-hot-encoding.ipynb)
    A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB)
    - **[IMDB2](IMDB/02-Keras-embedding.ipynb)** - [Sentiment analysis with text embedding](IMDB/02-Keras-embedding.ipynb)
    A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)
    - **[IMDB3](IMDB/03-Prediction.ipynb)** - [Reload and reuse a saved model](IMDB/03-Prediction.ipynb)
    Retrieving a saved model to perform a sentiment analysis (movie review)
    - **[IMDB4](IMDB/04-Show-vectors.ipynb)** - [Reload embedded vectors](IMDB/04-Show-vectors.ipynb)
    Retrieving embedded vectors from our trained model
    - **[IMDB5](IMDB/05-LSTM-Keras.ipynb)** - [Sentiment analysis with a RNN network](IMDB/05-LSTM-Keras.ipynb)
    Still the same problem, but with a network combining embedding and RNN
    
    ### Time series with Recurrent Neural Network (RNN
    - **[LADYB1](SYNOP/LADYB1-Ladybug.ipynb)** - [Prediction of a 2D trajectory via RNN](SYNOP/LADYB1-Ladybug.ipynb)
    Artificial dataset generation and prediction attempt via a recurrent network
    - **[SYNOP1](SYNOP/SYNOP1-Preparation-of-data.ipynb)** - [Preparation of data](SYNOP/SYNOP1-Preparation-of-data.ipynb)
    Episode 1 : Data analysis and preparation of a usuable meteorological dataset (SYNOP)
    - **[SYNOP2](SYNOP/SYNOP2-First-predictions.ipynb)** - [First predictions at 3h](SYNOP/SYNOP2-First-predictions.ipynb)
    Episode 2 : RNN training session for weather prediction attempt at 3h
    - **[SYNOP3](SYNOP/SYNOP3-12h-predictions.ipynb)** - [12h predictions](SYNOP/SYNOP3-12h-predictions.ipynb)
    Episode 3: Attempt to predict in a more longer term
    
    ### Sentiment analysis with transformer
    - **[TRANS1](Transformers/01-Distilbert.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers/01-Distilbert.ipynb)
    Using a Tranformer to perform a sentiment analysis (IMDB) - Jean Zay version
    - **[TRANS2](Transformers/02-distilbert_colab.ipynb)** - [IMDB, Sentiment analysis with Transformers ](Transformers/02-distilbert_colab.ipynb)
    Using a Tranformer to perform a sentiment analysis (IMDB) - Colab version
    
    ### Unsupervised learning with an autoencoder neural network (AE
    - **[AE1](AE/01-Prepare-MNIST-dataset.ipynb)** - [Prepare a noisy MNIST dataset](AE/01-Prepare-MNIST-dataset.ipynb)
    Episode 1: Preparation of a noisy MNIST dataset
    - **[AE2](AE/02-AE-with-MNIST.ipynb)** - [Building and training an AE denoiser model](AE/02-AE-with-MNIST.ipynb)
    Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset.
    - **[AE3](AE/03-AE-with-MNIST-post.ipynb)** - [Playing with our denoiser model](AE/03-AE-with-MNIST-post.ipynb)
    Episode 2 : Using the previously trained autoencoder to denoise data
    - **[AE4](AE/04-ExtAE-with-MNIST.ipynb)** - [Denoiser and classifier model](AE/04-ExtAE-with-MNIST.ipynb)
    Episode 4 : Construction of a denoiser and classifier model
    - **[AE5](AE/05-ExtAE-with-MNIST.ipynb)** - [Advanced denoiser and classifier model](AE/05-ExtAE-with-MNIST.ipynb)
    Episode 5 : Construction of an advanced denoiser and classifier model
    
    ### Generative network with Variational Autoencoder (VAE
    - **[VAE1](VAE/01-VAE-with-MNIST.ipynb)** - [First VAE, using functional API (MNIST dataset)](VAE/01-VAE-with-MNIST.ipynb)
    Construction and training of a VAE, using functional APPI, with a latent space of small dimension.
    - **[VAE2](VAE/02-VAE-with-MNIST.ipynb)** - [VAE, using a custom model class  (MNIST dataset)](VAE/02-VAE-with-MNIST.ipynb)
    Construction and training of a VAE, using model subclass, with a latent space of small dimension.
    - **[VAE3](VAE/03-VAE-with-MNIST-post.ipynb)** - [Analysis of the VAE's latent space of MNIST dataset](VAE/03-VAE-with-MNIST-post.ipynb)
    Visualization and analysis of the VAE's latent space of the dataset MNIST
    - **[VAE5](VAE/05-About-CelebA.ipynb)** - [Another game play : About the CelebA dataset](VAE/05-About-CelebA.ipynb)
    Episode 1 : Presentation of the CelebA dataset and problems related to its size
    - **[VAE6](VAE/06-Prepare-CelebA-datasets.ipynb)** - [Generation of a clustered dataset](VAE/06-Prepare-CelebA-datasets.ipynb)
    Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset
    - **[VAE7](VAE/07-Check-CelebA.ipynb)** - [Checking the clustered dataset](VAE/07-Check-CelebA.ipynb)
    Episode : 3 Clustered dataset verification and testing of our datagenerator
    - **[VAE8](VAE/08-VAE-with-CelebA-128x128.ipynb)** - [Training session for our VAE with 128x128 images](VAE/08-VAE-with-CelebA-128x128.ipynb)
    Episode 4 : Training with our clustered datasets in notebook or batch mode
    - **[VAE9](VAE/09-VAE-with-CelebA-192x160.ipynb)** - [Training session for our VAE with 192x160 images](VAE/09-VAE-with-CelebA-192x160.ipynb)
    Episode 4 : Training with our clustered datasets in notebook or batch mode
    - **[VAE10](VAE/10-VAE-with-CelebA-post.ipynb)** - [Data generation from latent space](VAE/10-VAE-with-CelebA-post.ipynb)
    Episode 5 : Exploring latent space to generate new data
    - **[VAE10](VAE/batch_slurm.sh)** - [SLURM batch script](VAE/batch_slurm.sh)
    Bash script for SLURM batch submission of VAE8 notebooks
    
    ### Generative Adversarial Networks (GANs
    - **[SHEEP1](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)** - [A first DCGAN to Draw a Sheep](DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb)
    Episode 1 : Draw me a sheep, revisited with a DCGAN
    - **[SHEEP2](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)** - [A WGAN-GP to Draw a Sheep](DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb)
    Episode 2 : Draw me a sheep, revisited with a WGAN-GP
    
    ### Deep Reinforcement Learning (DRL
    - **[DRL1](DRL/FIDLE_DQNfromScratch.ipynb)** - [Solving CartPole with DQN](DRL/FIDLE_DQNfromScratch.ipynb)
    Using a a Deep Q-Network to play CartPole - an inverted pendulum problem (PyTorch)
    - **[DRL2](DRL/FIDLE_rl_baselines_zoo.ipynb)** - [RL Baselines3 Zoo: Training in Colab](DRL/FIDLE_rl_baselines_zoo.ipynb)
    Demo of Stable baseline3 with Colab
    
    ### Miscellaneous
    - **[ACTF1](Misc/Activation-Functions.ipynb)** - [Activation functions](Misc/Activation-Functions.ipynb)
    Some activation functions, with their derivatives.
    - **[NP1](Misc/Numpy.ipynb)** - [A short introduction to Numpy](Misc/Numpy.ipynb)
    Numpy is an essential tool for the Scientific Python.
    - **[SCRATCH1](Misc/Scratchbook.ipynb)** - [Scratchbook](Misc/Scratchbook.ipynb)
    A scratchbook for small examples
    - **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)
    4 ways to use Tensorboard from the Jupyter environment
+    - **[PANDAS1](Misc/Using-pandas.ipynb)** - [Quelques exemples avec Pandas](Misc/Using-pandas.ipynb)
+    pandas is another essential tool for the Scientific Python.
    <!-- TOC_END -->
    
    
    ## Installation
    
    Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)** these notebooks and datasets.
    
    ## Licence
    
    [<img width="100px" src="fidle/img/00-fidle-CC BY-NC-SA.svg"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/)
    \[en\] 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/#).
    
    
    ----
    [<img width="80px" src="fidle/img/00-Fidle-logo-01.svg"></img>](#top)

--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@ For more information, see **https://fidle.cnrs.fr** :
 For more information, you can contact us at :  
 [<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)

-Current Version : <!-- VERSION_BEGIN -->2.2.0<!-- VERSION_END -->
+Current Version : <!-- VERSION_BEGIN -->2.2.1<!-- VERSION_END -->


 ## Course materials
@@ -46,7 +46,7 @@ Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)
 ## Jupyter notebooks

 <!-- TOC_BEGIN -->
-<!-- Automatically generated on : 13/10/22 18:40:08 -->
+<!-- Automatically generated on : 16/10/22 21:33:51 -->

 ### Linear and logistic regression
 - **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)  
@@ -67,6 +67,8 @@ Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !
 Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
 - **[BHPD2](BHPD/02-DNN-Regression-Premium.ipynb)** - [Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)  
 A more advanced implementation of the precedent example
+- **[WINE1](BHPD/03-DNN-Wine-Regression.ipynb)** - [Wine quality prediction with a Dense Network (DNN)](BHPD/03-DNN-Wine-Regression.ipynb)  
+Another example of regression, with a wine quality prediction!

 ### Basic classification using a DN
 - **[MNIST1](MNIST/01-DNN-MNIST.ipynb)** - [Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)  
@@ -177,6 +179,8 @@ Numpy is an essential tool for the Scientific Python.
 A scratchbook for small examples
 - **[TSB1](Misc/Using-Tensorboard.ipynb)** - [Tensorboard with/from Jupyter ](Misc/Using-Tensorboard.ipynb)  
 4 ways to use Tensorboard from the Jupyter environment
+- **[PANDAS1](Misc/Using-pandas.ipynb)** - [Quelques exemples avec Pandas](Misc/Using-pandas.ipynb)  
+pandas is another essential tool for the Scientific Python.
 <!-- TOC_END -->



--- a/fidle/about.yml
+++ b/fidle/about.yml
@@ -13,7 +13,7 @@
 #
 # This file describes the notebooks used by the Fidle training.

-version:          2.2.0
+version:          2.2.1
 content:          notebooks
 name:             Notebooks Fidle
 description:      All notebooks used by the Fidle training

--- a/fidle/ci/OLD-full_ci.yml
+++ b/fidle/ci/OLD-full_ci.yml
-_metadata_:
-  version: '1.0'
-  output_tag: ==ci==
-  save_figs: true
-  description: Full profile for CI 
-#
-# ------ LinearReg -------------------------------------------------
-#
-LINR1:
-  notebook_id: LINR1
-  notebook_dir: LinearReg
-  notebook_src: 01-Linear-Regression.ipynb
-  notebook_tag: default
-GRAD1:
-  notebook_id: GRAD1
-  notebook_dir: LinearReg
-  notebook_src: 02-Gradient-descent.ipynb
-  notebook_tag: default
-POLR1:
-  notebook_id: POLR1
-  notebook_dir: LinearReg
-  notebook_src: 03-Polynomial-Regression.ipynb
-  notebook_tag: default
-LOGR1:
-  notebook_id: LOGR1
-  notebook_dir: LinearReg
-  notebook_src: 04-Logistic-Regression.ipynb
-  notebook_tag: default
-PER57:
-  notebook_id: PER57
-  notebook_dir: IRIS
-  notebook_src: 01-Simple-Perceptron.ipynb
-  notebook_tag: default
-#
-# ------ BHPD ------------------------------------------------------
-#
-BHPD1:
-  notebook_id: BHPD1
-  notebook_dir: BHPD
-  notebook_src: 01-DNN-Regression.ipynb
-  notebook_tag: default
-BHPD2:
-  notebook_id: BHPD2
-  notebook_dir: BHPD
-  notebook_src: 02-DNN-Regression-Premium.ipynb
-  notebook_tag: default
-#
-# ------ MNIST -----------------------------------------------------
-#
-MNIST1:
-  notebook_id: MNIST1
-  notebook_dir: MNIST
-  notebook_src: 01-DNN-MNIST.ipynb
-  notebook_tag: default
-#
-# ------ GTSRB -----------------------------------------------------
-#
-GTSRB1:
-  notebook_id: GTSRB1
-  notebook_dir: GTSRB
-  notebook_src: 01-Preparation-of-data.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.01
-    output_dir: './data'
-GTSRB2:
-  notebook_id: GTSRB2
-  notebook_dir: GTSRB
-  notebook_src: 02-First-convolutions.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB2_ci
-    enhanced_dir: './data'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-GTSRB3:
-  notebook_id: GTSRB3
-  notebook_dir: GTSRB
-  notebook_src: 03-Tracking-and-visualizing.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB3_ci
-    enhanced_dir: './data'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-GTSRB4:
-  notebook_id: GTSRB4
-  notebook_dir: GTSRB
-  notebook_src: 04-Data-augmentation.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB4_ci
-    enhanced_dir: './data'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-GTSRB5_r1:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =1==ci==
-  overrides:
-    run_dir: ./run/GTSRB5_ci
-    enhanced_dir: './data'
-    datasets: "['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']"
-    models: "{'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}"
-    batch_size: 64
-    epochs: 16
-    scale: 1
-    with_datagen: False
-    verbose: 0
-GTSRB5_r2:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =2==ci==
-  overrides:
-    run_dir: ./run/GTSRB5_ci
-    enhanced_dir: './data'
-    datasets: "['set-48x48-L', 'set-48x48-RGB']"
-    models: "{'v2':'get_model_v2', 'v3':'get_model_v3'}"
-    batch_size: 64
-    epochs: 16
-    scale: 1
-    with_datagen: False
-    verbose: 0
-GTSRB6:
-  notebook_id: GTSRB6
-  notebook_dir: GTSRB
-  notebook_src: 06-Notebook-as-a-batch.ipynb
-  notebook_tag: default
-GTSRB7:
-  notebook_id: GTSRB7
-  notebook_dir: GTSRB
-  notebook_src: 07-Show-report.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB7_ci
-    report_dir: ./run/GTSRB5_ci
-#
-# ------ IMDB ------------------------------------------------------
-#
-IMDB1:
-  notebook_id: IMDB1
-  notebook_dir: IMDB
-  notebook_src: 01-Embedding-Keras.ipynb
-  notebook_tag: default
-IMDB2:
-  notebook_id: IMDB1
-  notebook_dir: IMDB
-  notebook_src: 02-Prediction.ipynb
-  notebook_tag: default
-IMDB3:
-  notebook_id: IMDB1
-  notebook_dir: IMDB
-  notebook_src: 03-LSTM-Keras.ipynb
-  notebook_tag: default
-#
-# ------ SYNOP -----------------------------------------------------
-#
-SYNOP1:
-  notebook_id: SYNOP1
-  notebook_dir: SYNOP
-  notebook_src: 01-Preparation-of-data.ipynb
-  notebook_tag: default
-SYNOP2:
-  notebook_id: SYNOP2
-  notebook_dir: SYNOP
-  notebook_src: 02-First-predictions.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.5
-    train_prop: 0.8
-    sequence_len: 16
-    batch_size: 32
-    epochs: 10
-SYNOP3:
-  notebook_id: SYNOP3
-  notebook_dir: SYNOP
-  notebook_src: 03-12h-predictions.ipynb
-  notebook_tag: default
-  overrides:
-    iterations: 4
-    scale: 1
-    train_prop: 0.8
-    sequence_len: 16
-    batch_size: 32
-    epochs: 10
-#
-# ------ AE --------------------------------------------------------
-#
-AE1:
-  notebook_id: AE1
-  notebook_dir: AE
-  notebook_src: 01-AE-with-MNIST.ipynb
-  notebook_tag: default
-AE2:
-  notebook_id: AE2
-  notebook_dir: AE
-  notebook_src: 02-AE-with-MNIST-post.ipynb
-  notebook_tag: default
-#
-# ------ VAE -------------------------------------------------------
-#
-VAE1:
-  notebook_id: VAE1
-  notebook_dir: VAE
-  notebook_src: 01-VAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE1_ci
-    scale: 1
-    latent_dim: 2
-    r_loss_factor: 0.994
-    batch_size: 64
-    epochs: 10
-VAE2:
-  notebook_id: VAE2
-  notebook_dir: VAE
-  notebook_src: 02-VAE-with-MNIST-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE1_ci
-VAE5:
-  notebook_id: VAE5
-  notebook_dir: VAE
-  notebook_src: 05-About-CelebA.ipynb
-  notebook_tag: default
-VAE6:
-  notebook_id: VAE6
-  notebook_dir: VAE
-  notebook_src: 06-Prepare-CelebA-datasets.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.02
-    image_size: '(192,160)'
-    output_dir: ./data
-    exit_if_exist: False
-VAE7:
-  notebook_id: VAE7
-  notebook_dir: VAE
-  notebook_src: 07-Check-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    image_size: '(192,160)'
-    enhanced_dir: './data'
-VAE8:
-  notebook_id: VAE8
-  notebook_dir: VAE
-  notebook_src: 08-VAE-with-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE8_ci
-    scale: 1
-    image_size: '(192,160)'
-    enhanced_dir: './data'
-    latent_dim: 300
-    r_loss_factor: 0.6
-    batch_size: 64
-    epochs: 15
-VAE9:
-  notebook_id: VAE9
-  notebook_dir: VAE
-  notebook_src: 09-VAE-with-CelebA-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE8_ci
-    image_size: '(192,160)'
-    enhanced_dir: './data'
-#
-# ------ Misc ------------------------------------------------------
-#
-ACTF1:
-  notebook_id: ACTF1
-  notebook_dir: Misc
-  notebook_src: Activation-Functions.ipynb
-  notebook_tag: default
-NP1:
-  notebook_id: NP1
-  notebook_dir: Misc
-  notebook_src: Numpy.ipynb
-  notebook_tag: default
-TSB1:
-  notebook_id: TSB1
-  notebook_dir: Misc
-  notebook_src: Using-Tensorboard.ipynb
-  notebook_tag: default
--- a/fidle/ci/OLD-full_gpu.yml
+++ b/fidle/ci/OLD-full_gpu.yml
-_metadata_:
-  version: '1.0'
-  output_tag: ==done==
-  save_figs: true
-  description: Full profile for GPU
-#
-# ------ LinearReg -------------------------------------------------
-#
-Nb_LINR1:
-  notebook_id: LINR1
-  notebook_dir: LinearReg
-  notebook_src: 01-Linear-Regression.ipynb
-  notebook_tag: default
-Nb_GRAD1:
-  notebook_id: GRAD1
-  notebook_dir: LinearReg
-  notebook_src: 02-Gradient-descent.ipynb
-  notebook_tag: default
-Nb_POLR1:
-  notebook_id: POLR1
-  notebook_dir: LinearReg
-  notebook_src: 03-Polynomial-Regression.ipynb
-  notebook_tag: default
-Nb_LOGR1:
-  notebook_id: LOGR1
-  notebook_dir: LinearReg
-  notebook_src: 04-Logistic-Regression.ipynb
-  notebook_tag: default
-Nb_PER57:
-  notebook_id: PER57
-  notebook_dir: IRIS
-  notebook_src: 01-Simple-Perceptron.ipynb
-  notebook_tag: default
-#
-# ------ BHPD ------------------------------------------------------
-#
-Nb_BHPD1:
-  notebook_id: BHPD1
-  notebook_dir: BHPD
-  notebook_src: 01-DNN-Regression.ipynb
-  notebook_tag: default
-Nb_BHPD2:
-  notebook_id: BHPD2
-  notebook_dir: BHPD
-  notebook_src: 02-DNN-Regression-Premium.ipynb
-  notebook_tag: default
-#
-# ------ MNIST -----------------------------------------------------
-#
-Nb_MNIST1:
-  notebook_id: MNIST1
-  notebook_dir: MNIST
-  notebook_src: 01-DNN-MNIST.ipynb
-  notebook_tag: default
-Nb_MNIST2:
-  notebook_id: MNIST2
-  notebook_dir: MNIST
-  notebook_src: 02-CNN-MNIST.ipynb
-  notebook_tag: default
-#
-# ------ GTSRB -----------------------------------------------------
-#
-Nb_GTSRB1:
-  notebook_id: GTSRB1
-  notebook_dir: GTSRB
-  notebook_src: 01-Preparation-of-data.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.05
-    output_dir: ./data
-Nb_GTSRB2:
-  notebook_id: GTSRB2
-  notebook_dir: GTSRB
-  notebook_src: 02-First-convolutions.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB2_done
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-Nb_GTSRB3:
-  notebook_id: GTSRB3
-  notebook_dir: GTSRB
-  notebook_src: 03-Tracking-and-visualizing.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB3_done
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-Nb_GTSRB4:
-  notebook_id: GTSRB4
-  notebook_dir: GTSRB
-  notebook_src: 04-Data-augmentation.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB4_done
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-Nb_GTSRB5_r1:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =1==done==
-  overrides:
-    run_dir: ./run/GTSRB5_done
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    datasets: "['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']"
-    models: "{'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}"
-    batch_size: 64
-    epochs: 16
-    scale: 1
-    with_datagen: False
-    verbose: 0
-Nb_GTSRB5_r2:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =2==done==
-  overrides:
-    run_dir: ./run/GTSRB5_done
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    datasets: "['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']"
-    models: "{'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}"
-    batch_size: 64
-    epochs: 16
-    scale: 1
-    with_datagen: False
-    verbose: 0
-Nb_GTSRB5_r3:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =3==done==
-  overrides:
-    run_dir: ./run/GTSRB5_done
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    datasets: "['set-48x48-L', 'set-48x48-RGB']"
-    models: "{'v2':'get_model_v2', 'v3':'get_model_v3'}"
-    batch_size: 64
-    epochs: 16
-    scale: 1
-    with_datagen: True
-    verbose: 0
-Nb_GTSRB6:
-  notebook_id: GTSRB6
-  notebook_dir: GTSRB
-  notebook_src: 06-Notebook-as-a-batch.ipynb
-  notebook_tag: default
-Nb_GTSRB7:
-  notebook_id: GTSRB7
-  notebook_dir: GTSRB
-  notebook_src: 07-Show-report.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB7_done
-    report_dir: ./run/GTSRB5_done
-#
-# ------ IMDB ------------------------------------------------------
-#
-Nb_IMDB1:
-  notebook_id: IMDB1
-  notebook_dir: IMDB
-  notebook_src: 01-One-hot-encoding.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    hide_most_frequently: default
-    batch_size: default
-    epochs: default
-Nb_IMDB2:
-  notebook_id: IMDB2
-  notebook_dir: IMDB
-  notebook_src: 02-Keras-embedding.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    hide_most_frequently: default
-    review_len: default
-    dense_vector_size: default
-    batch_size: default
-    epochs: default
-    output_dir: default
-Nb_IMDB3:
-  notebook_id: IMDB3
-  notebook_dir: IMDB
-  notebook_src: 03-Prediction.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    review_len: default
-    dictionaries_dir: default
-Nb_IMDB4:
-  notebook_id: IMDB4
-  notebook_dir: IMDB
-  notebook_src: 04-Show-vectors.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    review_len: default
-    dictionaries_dir: default
-Nb_IMDB5:
-  notebook_id: IMDB5
-  notebook_dir: IMDB
-  notebook_src: 05-LSTM-Keras.ipynb
-  notebook_tag: default
-#
-# ------ SYNOP -----------------------------------------------------
-#
-Nb_LADYB1:
-  notebook_id: LADYB1
-  notebook_dir: SYNOP
-  notebook_src: LADYB1-Ladybug.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: default
-    train_prop: default
-    sequence_len: default
-    predict_len: default
-    batch_size: default
-    epochs: default
-Nb_SYNOP1:
-  notebook_id: SYNOP1
-  notebook_dir: SYNOP
-  notebook_src: SYNOP1-Preparation-of-data.ipynb
-  notebook_tag: default
-  overrides:
-    output_dir: default
-Nb_SYNOP2:
-  notebook_id: SYNOP2
-  notebook_dir: SYNOP
-  notebook_src: SYNOP2-First-predictions.ipynb
-  notebook_tag: default
-  overrides:
-    scale: default
-    train_prop: default
-    sequence_len: default
-    batch_size: default
-    epochs: default
-Nb_SYNOP3:
-  notebook_id: SYNOP3
-  notebook_dir: SYNOP
-  notebook_src: SYNOP3-12h-predictions.ipynb
-  notebook_tag: default
-  overrides:
-    iterations: default
-    scale: default
-    train_prop: default
-    sequence_len: default
-    batch_size: default
-    epochs: default
-#
-# ------ AE --------------------------------------------------------
-#
-Nb_AE1:
-  notebook_id: AE1
-  notebook_dir: AE
-  notebook_src: 01-AE-with-MNIST.ipynb
-  notebook_tag: default
-Nb_AE2:
-  notebook_id: AE2
-  notebook_dir: AE
-  notebook_src: 02-AE-with-MNIST-post.ipynb
-  notebook_tag: default
-#
-# ------ VAE -------------------------------------------------------
-#
-Nb_VAE1:
-  notebook_id: VAE1
-  notebook_dir: VAE
-  notebook_src: 01-VAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE1_done
-    scale: 1
-    latent_dim: 2
-    r_loss_factor: 0.994
-    batch_size: 64
-    epochs: 10
-Nb_VAE2:
-  notebook_id: VAE2
-  notebook_dir: VAE
-  notebook_src: 02-VAE-with-MNIST-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE1_done
-Nb_VAE5:
-  notebook_id: VAE5
-  notebook_dir: VAE
-  notebook_src: 05-About-CelebA.ipynb
-  notebook_tag: default
-Nb_VAE6:
-  notebook_id: VAE6
-  notebook_dir: VAE
-  notebook_src: 06-Prepare-CelebA-datasets.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.01
-    image_size: '(192,160)'
-    output_dir: ./data
-    exit_if_exist: False
-Nb_VAE7:
-  notebook_id: VAE7
-  notebook_dir: VAE
-  notebook_src: 07-Check-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    image_size: '(192,160)'
-    enhanced_dir: '{datasets_dir}/celeba/enhanced'
-Nb_VAE8:
-  notebook_id: VAE8
-  notebook_dir: VAE
-  notebook_src: 08-VAE-with-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE8_done
-    scale: 1
-    image_size: '(192,160)'
-    enhanced_dir: '{datasets_dir}/celeba/enhanced'
-    latent_dim: 300
-    r_loss_factor: 0.6
-    batch_size: 64
-    epochs: 15
-Nb_VAE9:
-  notebook_id: VAE9
-  notebook_dir: VAE
-  notebook_src: 09-VAE-with-CelebA-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE8_done
-    image_size: '(192,160)'
-    enhanced_dir: '{datasets_dir}/celeba/enhanced'
-#
-# ------ Misc ------------------------------------------------------
-#
-Nb_ACTF1:
-  notebook_id: ACTF1
-  notebook_dir: Misc
-  notebook_src: Activation-Functions.ipynb
-  notebook_tag: default
-Nb_NP1:
-  notebook_id: NP1
-  notebook_dir: Misc
-  notebook_src: Numpy.ipynb
-  notebook_tag: default
-Nb_TSB1:
-  notebook_id: TSB1
-  notebook_dir: Misc
-  notebook_src: Using-Tensorboard.ipynb
-  notebook_tag: default
--- a/fidle/ci/OLD-small_cpu.yml
+++ b/fidle/ci/OLD-small_cpu.yml
-_metadata_:
-  version: '1.0'
-  description: Full run on a small cpu
-  output_tag: ==ci==
-  output_ipynb: ./fidle/run/ci/ipynb
-  output_html:  ./fidle/run/ci/html
-  report_json:  ./fidle/run/ci/report.json
-  report_error: ./fidle/run/ci/error.txt
-  environment_vars:
-    FIDLE_SAVE_FIGS: true
-    TF_CPP_MIN_LOG_LEVEL: 2
-#
-# ------ LinearReg -------------------------------------------------
-#
-Nb_LINR1:
-  notebook_id: LINR1
-  notebook_dir: LinearReg
-  notebook_src: 01-Linear-Regression.ipynb
-  notebook_tag: default
-Nb_GRAD1:
-  notebook_id: GRAD1
-  notebook_dir: LinearReg
-  notebook_src: 02-Gradient-descent.ipynb
-  notebook_tag: default
-Nb_POLR1:
-  notebook_id: POLR1
-  notebook_dir: LinearReg
-  notebook_src: 03-Polynomial-Regression.ipynb
-  notebook_tag: default
-Nb_LOGR1:
-  notebook_id: LOGR1
-  notebook_dir: LinearReg
-  notebook_src: 04-Logistic-Regression.ipynb
-  notebook_tag: default
-Nb_PER57:
-  notebook_id: PER57
-  notebook_dir: IRIS
-  notebook_src: 01-Simple-Perceptron.ipynb
-  notebook_tag: default
-#
-# ------ BHPD ------------------------------------------------------
-#
-Nb_BHPD1:
-  notebook_id: BHPD1
-  notebook_dir: BHPD
-  notebook_src: 01-DNN-Regression.ipynb
-  notebook_tag: default
-  overrides:
-    fit_verbosity: 2
-Nb_BHPD2:
-  notebook_id: BHPD2
-  notebook_dir: BHPD
-  notebook_src: 02-DNN-Regression-Premium.ipynb
-  notebook_tag: default
-  overrides:
-    fit_verbosity: 2
-#
-# ------ MNIST -----------------------------------------------------
-#
-Nb_MNIST1:
-  notebook_id: MNIST1
-  notebook_dir: MNIST
-  notebook_src: 01-DNN-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    fit_verbosity: 2
-Nb_MNIST2:
-  notebook_id: MNIST2
-  notebook_dir: MNIST
-  notebook_src: 02-CNN-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    fit_verbosity: 2
-#
-# ------ GTSRB -----------------------------------------------------
-#
-Nb_GTSRB1:
-  notebook_id: GTSRB1
-  notebook_dir: GTSRB
-  notebook_src: 01-Preparation-of-data.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.01
-    output_dir: ./data
-    progress_verbosity: 2
-
-Nb_GTSRB2:
-  notebook_id: GTSRB2
-  notebook_dir: GTSRB
-  notebook_src: 02-First-convolutions.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB2_done
-    enhanced_dir: './data'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-    fit_verbosity: 2
-
-Nb_GTSRB3:
-  notebook_id: GTSRB3
-  notebook_dir: GTSRB
-  notebook_src: 03-Tracking-and-visualizing.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB3_done
-    enhanced_dir: './data'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-    fit_verbosity: 2
-
-Nb_GTSRB4:
-  notebook_id: GTSRB4
-  notebook_dir: GTSRB
-  notebook_src: 04-Data-augmentation.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB4_done
-    enhanced_dir: './data'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-    fit_verbosity: 2
-
-Nb_GTSRB5_r1:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =1==ci==
-  overrides:
-    run_dir: ./run/GTSRB5_done
-    enhanced_dir: './data'
-    datasets: "['set-24x24-L', 'set-24x24-RGB']"
-    models: "{'v1':'get_model_v1', 'v2':'get_model_v2'}"
-    batch_size: 64
-    epochs: 5
-    scale: 1
-    with_datagen: False
-    fit_verbosity: 0
-
-Nb_GTSRB6:
-  notebook_id: GTSRB6
-  notebook_dir: GTSRB
-  notebook_src: 06-Notebook-as-a-batch.ipynb
-  notebook_tag: default
-
-Nb_GTSRB7:
-  notebook_id: GTSRB7
-  notebook_dir: GTSRB
-  notebook_src: 07-Show-report.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/GTSRB7_done
-    report_dir: ./run/GTSRB5_done
-#
-# ------ IMDB ------------------------------------------------------
-#
-Nb_IMDB1:
-  notebook_id: IMDB1
-  notebook_dir: IMDB
-  notebook_src: 01-One-hot-encoding.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    hide_most_frequently: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-
-Nb_IMDB2:
-  notebook_id: IMDB2
-  notebook_dir: IMDB
-  notebook_src: 02-Keras-embedding.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    hide_most_frequently: default
-    review_len: default
-    dense_vector_size: default
-    batch_size: default
-    epochs: default
-    output_dir: default
-
-Nb_IMDB3:
-  notebook_id: IMDB3
-  notebook_dir: IMDB
-  notebook_src: 03-Prediction.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    review_len: default
-    dictionaries_dir: default
-
-Nb_IMDB4:
-  notebook_id: IMDB4
-  notebook_dir: IMDB
-  notebook_src: 04-Show-vectors.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    review_len: default
-    dictionaries_dir: default
-
-Nb_IMDB5:
-  notebook_id: IMDB5
-  notebook_dir: IMDB
-  notebook_src: 05-LSTM-Keras.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    hide_most_frequently: default
-    review_len: default
-    dense_vector_size: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-    scale: .1
-#
-# ------ SYNOP -----------------------------------------------------
-#
-Nb_LADYB1:
-  notebook_id: LADYB1
-  notebook_dir: SYNOP
-  notebook_src: LADYB1-Ladybug.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 0.1
-    train_prop: default
-    sequence_len: default
-    predict_len: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-
-Nb_SYNOP1:
-  notebook_id: SYNOP1
-  notebook_dir: SYNOP
-  notebook_src: SYNOP1-Preparation-of-data.ipynb
-  notebook_tag: default
-  overrides:
-    output_dir: default
-
-Nb_SYNOP2:
-  notebook_id: SYNOP2
-  notebook_dir: SYNOP
-  notebook_src: SYNOP2-First-predictions.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.1
-    train_prop: default
-    sequence_len: default
-    batch_size: default
-    epochs: default
-
-Nb_SYNOP3:
-  notebook_id: SYNOP3
-  notebook_dir: SYNOP
-  notebook_src: SYNOP3-12h-predictions.ipynb
-  notebook_tag: default
-  overrides:
-    iterations: default
-    scale: default
-    train_prop: default
-    sequence_len: default
-#
-# ------ AE --------------------------------------------------------
-#
-Nb_AE1:
-  notebook_id: AE1
-  notebook_dir: AE
-  notebook_src: 01-Prepare-MNIST-dataset.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 0.02
-    prepared_dataset: default
-    progress_verbosity: 2
-
-Nb_AE2:
-  notebook_id: AE2
-  notebook_dir: AE
-  notebook_src: 02-AE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    prepared_dataset: default
-    dataset_seed: default
-    scale: default
-    latent_dim: default
-    train_prop: default
-    batch_size: default
-    epochs: default
-
-Nb_AE3:
-  notebook_id: AE3
-  notebook_dir: AE
-  notebook_src: 03-AE-with-MNIST-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    prepared_dataset: default
-    dataset_seed: default
-    scale: default
-    train_prop: default
-
-Nb_AE4:
-  notebook_id: AE4
-  notebook_dir: AE
-  notebook_src: 04-ExtAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    prepared_dataset: default
-    dataset_seed: default
-    scale: default
-    latent_dim: default
-    train_prop: default
-    batch_size: default
-    epochs: default
-
-Nb_AE5:
-  notebook_id: AE5
-  notebook_dir: AE
-  notebook_src: 05-ExtAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    prepared_dataset: default
-    dataset_seed: default
-    scale: default
-    latent_dim: default
-    train_prop: default
-    batch_size: default
-    epochs: default
-#
-# ------ VAE -------------------------------------------------------
-#
-Nb_VAE1:
-  notebook_id: VAE1
-  notebook_dir: VAE
-  notebook_src: 01-VAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    latent_dim: default
-    loss_weights: default
-    scale: 0.01
-    seed: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-
-Nb_VAE2:
-  notebook_id: VAE2
-  notebook_dir: VAE
-  notebook_src: 02-VAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE2.000
-    latent_dim: default
-    loss_weights: default
-    scale: 0.01
-    seed: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-
-Nb_VAE3:
-  notebook_id: VAE3
-  notebook_dir: VAE
-  notebook_src: 03-VAE-with-MNIST-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/VAE2.000
-    scale: default
-    seed: default
-
-Nb_VAE5:
-  notebook_id: VAE5
-  notebook_dir: VAE
-  notebook_src: 05-About-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    progress_verbosity: 2
-    
-Nb_VAE6:
-  notebook_id: VAE6
-  notebook_dir: VAE
-  notebook_src: 06-Prepare-CelebA-datasets.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 0.01
-    seed: default
-    cluster_size: default
-    image_size: default
-    output_dir: ./data
-    exit_if_exist: False
-    progress_verbosity: 2
-
-Nb_VAE7:
-  notebook_id: VAE7
-  notebook_dir: VAE
-  notebook_src: 07-Check-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    image_size: default
-    enhanced_dir: ./data
-    progress_verbosity: 2
-
-Nb_VAE8:
-  notebook_id: VAE8
-  notebook_dir: VAE
-  notebook_src: 08-VAE-with-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 0.1
-    image_size: default
-    enhanced_dir: ./data
-    latent_dim: default
-    loss_weights: default
-    batch_size: default
-    epochs: default
-    progress_verbosity: 2
-
-Nb_VAE9:
-  notebook_id: VAE9
-  notebook_dir: VAE
-  notebook_src: 09-VAE-with-CelebA-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    image_size: default
-    enhanced_dir: ./data
-
-# ------ DCGAN -----------------------------------------------------
-#
-Nb_SHEEP1:
-  notebook_id: SHEEP1
-  notebook_dir: DCGAN
-  notebook_src: 01-DCGAN-Draw-me-a-sheep.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.005
-    run_dir: default
-    latent_dim: default
-    epochs: 5
-    batch_size: default
-    num_img: default
-    fit_verbosity: 2
-#
-# ------ Misc ------------------------------------------------------
-#
-Nb_ACTF1:
-  notebook_id: ACTF1
-  notebook_dir: Misc
-  notebook_src: Activation-Functions.ipynb
-  notebook_tag: default
-
-Nb_NP1:
-  notebook_id: NP1
-  notebook_dir: Misc
-  notebook_src: Numpy.ipynb
-  notebook_tag: default
--- a/fidle/ci/OLD-smart_gpu.yml
+++ b/fidle/ci/OLD-smart_gpu.yml
-_metadata_:
-  version: '1.0'
-  description: Full run on a smart gpu
-  output_tag: ==done==
-  output_ipynb: ./fidle/run/done/ipynb
-  output_html:  ./fidle/run/done/html
-  report_json:  ./fidle/run/done/report.json
-  report_error: ./fidle/run/done/error.txt
-  environment_vars:
-    FIDLE_SAVE_FIGS: true
-    TF_CPP_MIN_LOG_LEVEL: 2
-#
-# ------ LinearReg -------------------------------------------------
-#
-Nb_LINR1:
-  notebook_id: LINR1
-  notebook_dir: LinearReg
-  notebook_src: 01-Linear-Regression.ipynb
-  notebook_tag: default
-Nb_GRAD1:
-  notebook_id: GRAD1
-  notebook_dir: LinearReg
-  notebook_src: 02-Gradient-descent.ipynb
-  notebook_tag: default
-Nb_POLR1:
-  notebook_id: POLR1
-  notebook_dir: LinearReg
-  notebook_src: 03-Polynomial-Regression.ipynb
-  notebook_tag: default
-Nb_LOGR1:
-  notebook_id: LOGR1
-  notebook_dir: LinearReg
-  notebook_src: 04-Logistic-Regression.ipynb
-  notebook_tag: default
-Nb_PER57:
-  notebook_id: PER57
-  notebook_dir: IRIS
-  notebook_src: 01-Simple-Perceptron.ipynb
-  notebook_tag: default
-#
-# ------ BHPD ------------------------------------------------------
-#
-Nb_BHPD1:
-  notebook_id: BHPD1
-  notebook_dir: BHPD
-  notebook_src: 01-DNN-Regression.ipynb
-  notebook_tag: default
-  overrides:
-    fit_verbosity: 2
-Nb_BHPD2:
-  notebook_id: BHPD2
-  notebook_dir: BHPD
-  notebook_src: 02-DNN-Regression-Premium.ipynb
-  notebook_tag: default
-  overrides:
-    fit_verbosity: 2
-#
-# ------ MNIST -----------------------------------------------------
-#
-Nb_MNIST1:
-  notebook_id: MNIST1
-  notebook_dir: MNIST
-  notebook_src: 01-DNN-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    fit_verbosity: 2
-Nb_MNIST2:
-  notebook_id: MNIST2
-  notebook_dir: MNIST
-  notebook_src: 02-CNN-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    fit_verbosity: 2
-#
-# ------ GTSRB -----------------------------------------------------
-#
-Nb_GTSRB1:
-  notebook_id: GTSRB1
-  notebook_dir: GTSRB
-  notebook_src: 01-Preparation-of-data.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 0.01
-    output_dir: ./data
-    progress_verbosity: 2
-
-Nb_GTSRB2:
-  notebook_id: GTSRB2
-  notebook_dir: GTSRB
-  notebook_src: 02-First-convolutions.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-    fit_verbosity: 2
-
-Nb_GTSRB3:
-  notebook_id: GTSRB3
-  notebook_dir: GTSRB
-  notebook_src: 03-Tracking-and-visualizing.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-    fit_verbosity: 2
-
-Nb_GTSRB4:
-  notebook_id: GTSRB4
-  notebook_dir: GTSRB
-  notebook_src: 04-Data-augmentation.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    dataset_name: set-24x24-L
-    batch_size: 64
-    epochs: 5
-    scale: 1
-    fit_verbosity: 2
-
-Nb_GTSRB5_r1:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =1==done==
-  overrides:
-    run_dir: default
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    datasets: "['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']"
-    models: "{'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}"
-    batch_size: 64
-    epochs: 16
-    scale: 1
-    with_datagen: False
-    fit_verbosity: 0
-
-Nb_GTSRB5_r2:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =2==done==
-  overrides:
-    run_dir: default
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    datasets: "['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']"
-    models: "{'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}"
-    batch_size: 64
-    epochs: 16
-    scale: 1
-    with_datagen: False
-    fit_verbosity: 0
-
-Nb_GTSRB5_r3:
-  notebook_id: GTSRB5
-  notebook_dir: GTSRB
-  notebook_src: 05-Full-convolutions.ipynb
-  notebook_tag: =3==done==
-  overrides:
-    run_dir: default
-    enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
-    datasets: "['set-48x48-L', 'set-48x48-RGB']"
-    models: "{'v2':'get_model_v2', 'v3':'get_model_v3'}"
-    batch_size: 64
-    epochs: 16
-    scale: 1
-    with_datagen: True
-    fit_verbosity: 0
-
-Nb_GTSRB6:
-  notebook_id: GTSRB6
-  notebook_dir: GTSRB
-  notebook_src: 06-Notebook-as-a-batch.ipynb
-  notebook_tag: default
-
-Nb_GTSRB7:
-  notebook_id: GTSRB7
-  notebook_dir: GTSRB
-  notebook_src: 07-Show-report.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    report_dir: ./run/GTSRB5
-#
-# ------ IMDB ------------------------------------------------------
-#
-Nb_IMDB1:
-  notebook_id: IMDB1
-  notebook_dir: IMDB
-  notebook_src: 01-One-hot-encoding.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    hide_most_frequently: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-
-Nb_IMDB2:
-  notebook_id: IMDB2
-  notebook_dir: IMDB
-  notebook_src: 02-Keras-embedding.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    hide_most_frequently: default
-    review_len: default
-    dense_vector_size: default
-    batch_size: default
-    epochs: default
-    output_dir: default
-
-Nb_IMDB3:
-  notebook_id: IMDB3
-  notebook_dir: IMDB
-  notebook_src: 03-Prediction.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    review_len: default
-    dictionaries_dir: default
-
-Nb_IMDB4:
-  notebook_id: IMDB4
-  notebook_dir: IMDB
-  notebook_src: 04-Show-vectors.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    review_len: default
-    dictionaries_dir: default
-
-Nb_IMDB5:
-  notebook_id: IMDB5
-  notebook_dir: IMDB
-  notebook_src: 05-LSTM-Keras.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    vocab_size: default
-    hide_most_frequently: default
-    review_len: default
-    dense_vector_size: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-    scale: .5
-#
-# ------ SYNOP -----------------------------------------------------
-#
-Nb_LADYB1:
-  notebook_id: LADYB1
-  notebook_dir: SYNOP
-  notebook_src: LADYB1-Ladybug.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 1
-    train_prop: default
-    sequence_len: default
-    predict_len: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-
-Nb_SYNOP1:
-  notebook_id: SYNOP1
-  notebook_dir: SYNOP
-  notebook_src: SYNOP1-Preparation-of-data.ipynb
-  notebook_tag: default
-  overrides:
-    output_dir: default
-
-Nb_SYNOP2:
-  notebook_id: SYNOP2
-  notebook_dir: SYNOP
-  notebook_src: SYNOP2-First-predictions.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 1
-    train_prop: default
-    sequence_len: default
-    batch_size: default
-    epochs: default
-
-Nb_SYNOP3:
-  notebook_id: SYNOP3
-  notebook_dir: SYNOP
-  notebook_src: SYNOP3-12h-predictions.ipynb
-  notebook_tag: default
-  overrides:
-    iterations: default
-    scale: default
-    train_prop: default
-    sequence_len: default
-#
-# ------ AE --------------------------------------------------------
-#
-Nb_AE1:
-  notebook_id: AE1
-  notebook_dir: AE
-  notebook_src: 01-Prepare-MNIST-dataset.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 1
-    prepared_dataset: default
-    progress_verbosity: 2
-
-Nb_AE2:
-  notebook_id: AE2
-  notebook_dir: AE
-  notebook_src: 02-AE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    prepared_dataset: default
-    dataset_seed: default
-    scale: 1
-    latent_dim: default
-    train_prop: default
-    batch_size: default
-    epochs: default
-
-Nb_AE3:
-  notebook_id: AE3
-  notebook_dir: AE
-  notebook_src: 03-AE-with-MNIST-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: ./run/AE2
-    prepared_dataset: default
-    dataset_seed: default
-    scale: 1
-    train_prop: default
-
-Nb_AE4:
-  notebook_id: AE4
-  notebook_dir: AE
-  notebook_src: 04-ExtAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    prepared_dataset: default
-    dataset_seed: default
-    scale: 1
-    latent_dim: default
-    train_prop: default
-    batch_size: default
-    epochs: default
-
-Nb_AE5:
-  notebook_id: AE5
-  notebook_dir: AE
-  notebook_src: 05-ExtAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    prepared_dataset: default
-    dataset_seed: default
-    scale: 1
-    latent_dim: default
-    train_prop: default
-    batch_size: default
-    epochs: default
-#
-# ------ VAE -------------------------------------------------------
-#
-Nb_VAE1:
-  notebook_id: VAE1
-  notebook_dir: VAE
-  notebook_src: 01-VAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    latent_dim: 2
-    loss_weights: default
-    scale: 1
-    seed: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-
-Nb_VAE2:
-  notebook_id: VAE2
-  notebook_dir: VAE
-  notebook_src: 02-VAE-with-MNIST.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    latent_dim: 2
-    loss_weights: default
-    scale: 1
-    seed: default
-    batch_size: default
-    epochs: default
-    fit_verbosity: 2
-
-Nb_VAE3:
-  notebook_id: VAE3
-  notebook_dir: VAE
-  notebook_src: 03-VAE-with-MNIST-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 1
-    seed: default
-
-Nb_VAE5:
-  notebook_id: VAE5
-  notebook_dir: VAE
-  notebook_src: 05-About-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    progress_verbosity: 2
-    
-Nb_VAE6:
-  notebook_id: VAE6
-  notebook_dir: VAE
-  notebook_src: 06-Prepare-CelebA-datasets.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 0.05
-    seed: default
-    cluster_size: default
-    image_size: default
-    output_dir: ./data
-    exit_if_exist: False
-    progress_verbosity: 2
-
-Nb_VAE7:
-  notebook_id: VAE7
-  notebook_dir: VAE
-  notebook_src: 07-Check-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    image_size: default
-    enhanced_dir: ./data
-    progress_verbosity: 2
-
-Nb_VAE8:
-  notebook_id: VAE8
-  notebook_dir: VAE
-  notebook_src: 08-VAE-with-CelebA.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    scale: 1
-    image_size: '(192,160)'
-    enhanced_dir: '{datasets_dir}/celeba/enhanced'
-    latent_dim: 300
-    loss_weights: default
-    batch_size: 64
-    epochs: 15
-    progress_verbosity: 2
-
-Nb_VAE9_r1:
-  notebook_id: VAE9
-  notebook_dir: VAE
-  notebook_src: 09-VAE-with-CelebA-192x160.ipynb
-  notebook_tag: =1==done==
-  overrides:
-    run_dir: ./run/VAE9_r1
-    scale: 1
-    image_size: '(192,160)'
-    enhanced_dir: '{datasets_dir}/celeba/enhanced'
-    latent_dim: 100
-    loss_weights: '[.7,.3]'
-    batch_size: 64
-    epochs: 5
-    progress_verbosity: 2
-
-Nb_VAE9_r2:
-  notebook_id: VAE9
-  notebook_dir: VAE
-  notebook_src: 09-VAE-with-CelebA-192x160.ipynb
-  notebook_tag: =2==done==
-  overrides:
-    run_dir: ./run/VAE9_r2
-    scale: 1
-    image_size: '(192,160)'
-    enhanced_dir: '{datasets_dir}/celeba/enhanced'
-    latent_dim: 100
-    loss_weights: '[.5,.5]'
-    batch_size: 64
-    epochs: 5
-    progress_verbosity: 2
-
-Nb_VAE9_r3:
-  notebook_id: VAE9
-  notebook_dir: VAE
-  notebook_src: 09-VAE-with-CelebA-192x160.ipynb
-  notebook_tag: =3==done==
-  overrides:
-    run_dir: ./run/VAE9_r3
-    scale: 1
-    image_size: '(192,160)'
-    enhanced_dir: '{datasets_dir}/celeba/enhanced'
-    latent_dim: 100
-    loss_weights: '[.3,.7]'
-    batch_size: 64
-    epochs: 5
-    progress_verbosity: 2
-
-Nb_VAE10:
-  notebook_id: VAE10
-  notebook_dir: VAE
-  notebook_src: 10-VAE-with-CelebA-post.ipynb
-  notebook_tag: default
-  overrides:
-    run_dir: default
-    image_size: '(192,160)'
-    enhanced_dir: '{datasets_dir}/celeba/enhanced'
-
-# ------ DCGAN -----------------------------------------------------
-#
-Nb_SHEEP1:
-  notebook_id: SHEEP1
-  notebook_dir: DCGAN
-  notebook_src: 01-DCGAN-Draw-me-a-sheep.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 1
-    run_dir: ./run/SHEEP1
-    latent_dim: default
-    epochs: 10
-    batch_size: 32
-    num_img: 12
-    fit_verbosity: 2
-
-Nb_SHEEP2:
-  notebook_id: SHEEP2
-  notebook_dir: DCGAN
-  notebook_src: 02-WGANGP-Draw-me-a-sheep.ipynb
-  notebook_tag: default
-  overrides:
-    scale: 1
-    run_dir: ./run/SHEEP2
-    latent_dim: 80
-    epochs: 3
-    batch_size: 64
-    num_img: 12
-    fit_verbosity: 2    
-#
-# ------ Misc ------------------------------------------------------
-#
-Nb_ACTF1:
-  notebook_id: ACTF1
-  notebook_dir: Misc
-  notebook_src: Activation-Functions.ipynb
-  notebook_tag: default
-
-Nb_NP1:
-  notebook_id: NP1
-  notebook_dir: Misc
-  notebook_src: Numpy.ipynb
-  notebook_tag: default
--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
 campain:
  version: '1.0'
-  description: Automatically generated ci profile (13/10/22 18:40:08)
+  description: Automatically generated ci profile (16/10/22 21:33:51)
  directory: ./campains/default
  existing_notebook: 'remove    # remove|skip'
  report_template: 'fidle     # fidle|default'
@@ -35,6 +35,11 @@ BHPD2:
  notebook: BHPD/02-DNN-Regression-Premium.ipynb
  overrides:
    fit_verbosity: default
+WINE1:
+  notebook: BHPD/03-DNN-Wine-Regression.ipynb
+  overrides:
+    fit_verbosity: default
+    dataset_name: default

 #
 # ------------ MNIST
@@ -361,3 +366,5 @@ SCRATCH1:
 TSB1:
  notebook: Misc/Using-Tensorboard.ipynb
  overrides: ??
+PANDAS1:
+  notebook: Misc/Using-pandas.ipynb
--- a/fidle/ci/default_settings.yml
+++ b/fidle/ci/default_settings.yml
@@ -15,10 +15,13 @@ campain:
 #
 LINR1:
  notebook: LinearReg/01-Linear-Regression.ipynb
+
 GRAD1:
  notebook: LinearReg/02-Gradient-descent.ipynb
+
 POLR1:
  notebook: LinearReg/03-Polynomial-Regression.ipynb
+
 LOGR1:
  notebook: LinearReg/04-Logistic-Regression.ipynb

@@ -35,11 +38,17 @@ BHPD1:
  notebook: BHPD/01-DNN-Regression.ipynb
  overrides:
    fit_verbosity: 2
+
 BHPD2:
  notebook: BHPD/02-DNN-Regression-Premium.ipynb
  overrides:
    fit_verbosity: 2

+WINE1:
+  notebook: BHPD/03-DNN-Wine-Regression.ipynb
+  overrides:
+    fit_verbosity: 2
+    dataset_name: default
 #
 # ------------ MNIST
 #

--- a/fidle/ci/scale1_settings.yml
+++ b/fidle/ci/scale1_settings.yml
@@ -15,10 +15,13 @@ campain:
 #
 LINR1:
  notebook: LinearReg/01-Linear-Regression.ipynb
+
 GRAD1:
  notebook: LinearReg/02-Gradient-descent.ipynb
+
 POLR1:
  notebook: LinearReg/03-Polynomial-Regression.ipynb
+
 LOGR1:
  notebook: LinearReg/04-Logistic-Regression.ipynb

@@ -35,11 +38,24 @@ BHPD1:
  notebook: BHPD/01-DNN-Regression.ipynb
  overrides:
    fit_verbosity: 2
+
 BHPD2:
  notebook: BHPD/02-DNN-Regression-Premium.ipynb
  overrides:
    fit_verbosity: 2

+WINE1.1:
+  notebook: BHPD/03-DNN-Wine-Regression.ipynb
+  overrides:
+    fit_verbosity: 2
+    dataset_name: winequality-red.csv
+
+WINE1.2:
+  notebook: BHPD/03-DNN-Wine-Regression.ipynb
+  overrides:
+    fit_verbosity: 2
+    dataset_name: winequality-red.csv
+
 #
 # ------------ MNIST
 #