IMDB/02-Prediction.ipynb

+%% Cell type:markdown id: tags:
+
+Text Embedding - IMDB dataset
+=============================
+---
+Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+
+## Reviews analysis :
+
+The objective is to guess whether our new and personals films reviews are **positive or negative** .
+For this, we will use our previously saved model.
+
+What we're going to do:
+
+ - Preparing the data
+ - Retrieve our saved model
+ - Evaluate the result
+
+%% Cell type:markdown id: tags:
+
+## Step 1 - Init python stuff
+
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+
+import tensorflow as tf
+import tensorflow.keras as keras
+import tensorflow.keras.datasets.imdb as imdb
+
+import matplotlib.pyplot as plt
+import matplotlib
+import seaborn as sns
+import pandas as pd
+
+import os,sys,h5py,json,re
+
+from importlib import reload
+
+sys.path.append('..')
+import fidle.pwk as ooo
+
+ooo.init()
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    ModuleNotFoundError                       Traceback (most recent call last)
+    <ipython-input-1-94e372328354> in <module>
+          7 import matplotlib.pyplot as plt
+          8 import matplotlib
+    ----> 9 import seaborn as sns
+         10 import pandas as pd
+         11
+    ModuleNotFoundError: No module named 'seaborn'
+
+%% Cell type:markdown id: tags:
+
+## Step 2 : Preparing the data
+### 2.1 - Our reviews :
+
+%% Cell type:code id: tags:
+
+``` python
+reviews = [ "This film is particularly nice, a must see.",
+             "Some films are classics and cannot be ignored.",
+             "This movie is just abominable and doesn't deserve to be seen!"]
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.2 - Retrieve dictionaries
+
+%% Cell type:code id: tags:
+
+``` python
+with open('./data/word_index.json', 'r') as fp:
+    word_index = json.load(fp)
+    index_word = {index:word for word,index in word_index.items()}
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.3 - Clean, index and padd
+
+%% Cell type:code id: tags:
+
+``` python
+max_len    = 256
+vocab_size = 10000
+
+
+nb_reviews = len(reviews)
+x_data     = []
+
+# ---- For all reviews
+for review in reviews:
+    # ---- First index must be <start>
+    index_review=[1]
+    # ---- For all words
+    for w in review.split(' '):
+        # ---- Clean it
+        w_clean = re.sub(r"[^a-zA-Z0-9]", "", w)
+        # ---- Not empty ?
+        if len(w_clean)>0:
+            # ---- Get the index
+            w_index = word_index.get(w,2)
+            if w_index>vocab_size : w_index=2
+            # ---- Add the index if < vocab_size
+            index_review.append(w_index)
+    # ---- Add the indexed review
+    x_data.append(index_review)
+
+# ---- Padding
+x_data = keras.preprocessing.sequence.pad_sequences(x_data, value   = 0, padding = 'post', maxlen  = max_len)
+```
+
+%% Cell type:markdown id: tags:
+
+### 2.4 - Have a look
+
+%% Cell type:code id: tags:
+
+``` python
+def translate(x):
+    return ' '.join( [index_word.get(i,'?') for i in x] )
+
+for i in range(nb_reviews):
+    imax=np.where(x_data[i]==0)[0][0]+5
+    print(f'\nText review      :',    reviews[i])
+    print(  f'x_train[{i:}]       :', list(x_data[i][:imax]), '(...)')
+    print(  'Translation      :', translate(x_data[i][:imax]), '(...)')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 2 - Bring back the model
+
+%% Cell type:code id: tags:
+
+``` python
+model = keras.models.load_model('./run/models/best_model.h5')
+```
+
+%% Cell type:markdown id: tags:
+
+## Step 4 - Predict
+
+%% Cell type:code id: tags:
+
+``` python
+y_pred   = model.predict(x_data)
+```
+
+%% Cell type:markdown id: tags:
+
+#### And the winner is :
+
+%% Cell type:code id: tags:
+
+``` python
+for i in range(nb_reviews):
+    print(f'\n{reviews[i]:<70} =>',('NEGATIVE' if y_pred[i][0]<0.5 else 'POSITIVE'),f'({y_pred[i][0]:.2f})')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+a=[1]+[i for i in range(3)]
+a
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

README.md

+
+![](fidle/img/00-Fidle-titre-01_m.png)
+
+## 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 :
+   - **[Configuration SSH](../-/wikis/howto-ssh)**
+
+
+
+## Récupération de ce dépot et installation
+To run this examples, you need an environment with the following packages :
+ - Python 3.6
+ - numpy
+ - Tensorflow 2.0
+ - scikit-image
+ - scikit-learn
+ - Matplotlib
+ - seaborn
+ - pyplot
+
+You can install such a predefined environment :
+```
+conda env create -f environment.yml
+```
+
+To manage conda environment see [there](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)  
+
+
+
+## Misc
+...

VAE/batch-oar.sh

+#!/bin/bash
+#OAR -n VAE with CelebA
+#OAR -t gpu
+#OAR -l /nodes=1/gpudevice=1,walltime=01:00:00
+#OAR --stdout _batch/VAE_CelebA_%jobid%.out
+#OAR --stderr _batch/VAE_CelebA_%jobid%.err
+#OAR --project fidle
+
+#---- For cpu
+# use :
+# OAR -l /nodes=1/core=32,walltime=01:00:00
+# and add a 2>/dev/null to ipython xxx
+
+# -----------------------------------------------
+#         _           _       _
+#        | |__   __ _| |_ ___| |__
+#        | '_ \ / _` | __/ __| '_ \
+#        | |_) | (_| | || (__| | | |
+#        |_.__/ \__,_|\__\___|_| |_|
+#                       VAE CelebA at GRICAD
+# -----------------------------------------------
+#
+
+CONDA_ENV=deeplearning2
+RUN_DIR=~/fidle/VAE
+RUN_IPYNB=05.1-Batch-01.ipynb
+
+# ---- Cuda Conda initialization
+#
+echo '------------------------------------------------------------'
+echo "Start : $0"
+echo '------------------------------------------------------------'
+#
+source /applis/environments/cuda_env.sh dahu 10.0
+source /applis/environments/conda.sh
+#
+conda activate "$CONDA_ENV"
+
+# ---- Run it...
+#
+cd $RUN_DIR
+
+jupyter nbconvert --to notebook --execute "$RUN_IPYNB"

VAE/batch-slurm.sh

+#!/bin/bash
+
+#SBATCH --job-name="VAE_bizness"                       # nom du job
+#SBATCH --ntasks=1                                     # nombre de tâche (un unique processus ici)
+#SBATCH --gres=gpu:1                                   # nombre de GPU à réserver (un unique GPU ici)
+#SBATCH --cpus-per-task=10                             # nombre de coeurs à réserver (un quart du noeud)
+#SBATCH --hint=nomultithread                           # on réserve des coeurs physiques et non logiques
+#SBATCH --time=02:00:00                                # temps exécution maximum demande (HH:MM:SS)
+#SBATCH --output="_batch/VAE_%j.out"                   # nom du fichier de sortie
+#SBATCH --error="_batch/VAE_%j.err"                    # nom du fichier d'erreur (ici commun avec la sortie)
+#SBATCH --mail-user=Jean-Luc.Parouty@grenoble-inp.fr
+#SBATCH --mail-type=ALL
+
+# -----------------------------------------------
+#         _           _       _
+#        | |__   __ _| |_ ___| |__
+#        | '_ \ / _` | __/ __| '_ \
+#        | |_) | (_| | || (__| | | |
+#        |_.__/ \__,_|\__\___|_| |_|
+#                       VAE CelebA at IDRIS
+# -----------------------------------------------
+#
+
+MODULE_ENV="tensorflow-gpu/py3/2.0.0"
+RUN_DIR="$WORK/fidle/VAE"
+RUN_IPYNB="05.2-Variant.ipynb"
+
+# ---- Welcome...
+
+echo '------------------------------------------------------------'
+echo "Start : $0"
+echo '------------------------------------------------------------'
+echo "Job id        : $SLURM_JOB_ID"
+echo "Job name      : $SLURM_JOB_NAME"
+echo "Job node list : $SLURM_JOB_NODELIST"
+echo '------------------------------------------------------------'
+echo "Notebook      : $RUN_IPYNB"
+echo "Run in        : $RUN_DIR"
+echo "With env.     : $MODULE_ENV"
+echo '------------------------------------------------------------'
+
+
+# ---- Module
+
+module load $MODULE_ENV
+
+# ---- Run it...
+
+cd $RUN_DIR
+
+jupyter nbconvert --ExecutePreprocessor.timeout=-1 --to notebook --execute "$RUN_IPYNB"