From a48cc2eb02c1c6b9f7266eb6a3b982b580a227eb Mon Sep 17 00:00:00 2001 From: Tombenpotter Date: Wed, 28 Nov 2018 14:27:00 +0100 Subject: [PATCH 1/3] Update answers.md --- answers.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/answers.md b/answers.md index bb3b84e..345c081 100644 --- a/answers.md +++ b/answers.md @@ -1,10 +1,10 @@ # Answers -Nom: -Prénom: -NB: +Nom: Benguigui +Prénom: Thomas +NB: 3 -## 1.3 +## 1.3 Définir les objets suivants: graph: tensor: From fd2a7e46515e86e623fde082e3ee47f1aff3b080 Mon Sep 17 00:00:00 2001 From: Tombenpotter Date: Sat, 1 Dec 2018 18:35:29 +0100 Subject: [PATCH 2/3] Answer 1.3 --- answers.md | 14 +- tensforflow_base.ipynb | 531 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 542 insertions(+), 3 deletions(-) create mode 100644 tensforflow_base.ipynb diff --git a/answers.md b/answers.md index 345c081..99cdc83 100644 --- a/answers.md +++ b/answers.md @@ -6,9 +6,17 @@ NB: 3 ## 1.3 Définir les objets suivants: -graph: -tensor: -layer: + +graph: un graph correspond à un ensemble de layers de neurones qui enregistre et exécute les calculs à réaliser. +Les calculs ne sont pas exécutés dès qu'ils sont ajoutés au graphe, mais dès que toutes les opérations sont définies + +tensor: un tensor est un objet qui a plusieurs propriétés : +- un type de données +- une forme +Tous les éléments du tensor ont le même type, mais la forme du tensor n'est pas toujours connue. +Un tensor peut etre comparé à une matrice d'objets. + +layer: un layer est un niveau du réseau de neurones ## 3 answer: diff --git a/tensforflow_base.ipynb b/tensforflow_base.ipynb new file mode 100644 index 0000000..90b8f1a --- /dev/null +++ b/tensforflow_base.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.12.0\n" + ] + } + ], + "source": [ + "# TensorFlow and tf.keras\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "\n", + "# Helper libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "print(tf.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n", + "32768/29515 [=================================] - 0s 0us/step\n", + "40960/29515 [=========================================] - 0s 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n", + "26427392/26421880 [==============================] - 4s 0us/step\n", + "26435584/26421880 [==============================] - 4s 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n", + "16384/5148 [===============================================================================================] - 0s 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n", + "4423680/4422102 [==============================] - 1s 0us/step\n", + "4431872/4422102 [==============================] - 1s 0us/step\n" + ] + } + ], + "source": [ + "fashion_mnist = keras.datasets.fashion_mnist\n", + "\n", + "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n", + " 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.imshow(train_images[0])\n", + "plt.colorbar()\n", + "plt.grid(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "train_images = train_images / 255.0\n", + "\n", + "test_images = test_images / 255.0" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(train_images[i], cmap=plt.cm.binary)\n", + " plt.xlabel(class_names[train_labels[i]])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model = keras.Sequential([\n", + " keras.layers.Flatten(input_shape=(28, 28)),\n", + " keras.layers.Dense(128, activation=tf.nn.relu),\n", + " keras.layers.Dense(10, activation=tf.nn.softmax)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer=tf.train.AdamOptimizer(), \n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "60000/60000 [==============================] - 6s 95us/step - loss: 0.4949 - acc: 0.8267\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 5s 90us/step - loss: 0.3730 - acc: 0.8663\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 5s 86us/step - loss: 0.3342 - acc: 0.8789\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 5s 87us/step - loss: 0.3104 - acc: 0.8864\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 5s 90us/step - loss: 0.2942 - acc: 0.8920\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(train_images, train_labels, epochs=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10000/10000 [==============================] - 0s 40us/step\n", + "('Test accuracy:', 0.8711)\n" + ] + } + ], + "source": [ + "test_loss, test_acc = model.evaluate(test_images, test_labels)\n", + "\n", + "print('Test accuracy:', test_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(test_images)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4.9018186e-06, 1.3108597e-08, 1.6637746e-07, 1.5751093e-08,\n", + " 2.9460344e-07, 1.6195383e-02, 1.3281990e-06, 9.1950968e-03,\n", + " 1.0181271e-05, 9.7459263e-01], dtype=float32)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.argmax(predictions[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_labels[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_image(i, predictions_array, true_label, img):\n", + " predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n", + " plt.grid(False)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " \n", + " plt.imshow(img, cmap=plt.cm.binary)\n", + "\n", + " predicted_label = np.argmax(predictions_array)\n", + " if predicted_label == true_label:\n", + " color = 'blue'\n", + " else:\n", + " color = 'red'\n", + " \n", + " plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n", + " 100*np.max(predictions_array),\n", + " class_names[true_label]),\n", + " color=color)\n", + "\n", + "def plot_value_array(i, predictions_array, true_label):\n", + " predictions_array, true_label = predictions_array[i], true_label[i]\n", + " plt.grid(False)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n", + " plt.ylim([0, 1]) \n", + " predicted_label = np.argmax(predictions_array)\n", + " \n", + " thisplot[predicted_label].set_color('red')\n", + " thisplot[true_label].set_color('blue')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(6,3))\n", + "plt.subplot(1,2,1)\n", + "plot_image(i, predictions, test_labels, test_images)\n", + "plt.subplot(1,2,2)\n", + "plot_value_array(i, predictions, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 12\n", + "plt.figure(figsize=(6,3))\n", + "plt.subplot(1,2,1)\n", + "plot_image(i, predictions, test_labels, test_images)\n", + "plt.subplot(1,2,2)\n", + "plot_value_array(i, predictions, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the first X test images, their predicted label, and the true label\n", + "# Color correct predictions in blue, incorrect predictions in red\n", + "num_rows = 5\n", + "num_cols = 3\n", + "num_images = num_rows*num_cols\n", + "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n", + "for i in range(num_images):\n", + " plt.subplot(num_rows, 2*num_cols, 2*i+1)\n", + " plot_image(i, predictions, test_labels, test_images)\n", + " plt.subplot(num_rows, 2*num_cols, 2*i+2)\n", + " plot_value_array(i, predictions, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(28, 28)\n" + ] + } + ], + "source": [ + "# Grab an image from the test dataset\n", + "img = test_images[0]\n", + "\n", + "print(img.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 28, 28)\n" + ] + } + ], + "source": [ + "# Add the image to a batch where it's the only member.\n", + "img = (np.expand_dims(img,0))\n", + "\n", + "print(img.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4.9018186e-06 1.3108597e-08 1.6637746e-07 1.5751063e-08 2.9460287e-07\n", + " 1.6195366e-02 1.3281990e-06 9.1950838e-03 1.0181252e-05 9.7459263e-01]]\n" + ] + } + ], + "source": [ + "predictions_single = model.predict(img)\n", + "\n", + "print(predictions_single)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_value_array(0, predictions_single, test_labels)\n", + "_ = plt.xticks(range(10), class_names, rotation=45)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.argmax(predictions_single[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 7def2c2982d013c963a294559fed5810649d8a6a Mon Sep 17 00:00:00 2001 From: Tombenpotter Date: Wed, 12 Dec 2018 11:28:32 +0100 Subject: [PATCH 3/3] End of tp3 --- answers.md | 1 + serving | 1 + 2 files changed, 2 insertions(+) create mode 160000 serving diff --git a/answers.md b/answers.md index 99cdc83..c3140f4 100644 --- a/answers.md +++ b/answers.md @@ -20,3 +20,4 @@ layer: un layer est un niveau du réseau de neurones ## 3 answer: +Les modèles doivent être entraînés de manière régulière, et être tenus à jour. C'est pour ça qu'on peut utiliser Docker ou Torus pour avoir des routines automatiques d'entraînement. diff --git a/serving b/serving new file mode 160000 index 0000000..d51ce82 --- /dev/null +++ b/serving @@ -0,0 +1 @@ +Subproject commit d51ce823fcfe81044048878898a437d9f5c91284