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EE369.md

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+# For EE369 students from SJTU
+
+## 作业要求：
+1. star并fork当前的repository，完成以下notebook（至少完成两个）中的空白：
+    * [`numpy_matplotlib_sklearn.ipynb`](numpy_matplotlib_sklearn.ipynb)
+    * [OPTIONAL] [`pytorch.ipynb`](pytorch.ipynb)
+    * [OPTIONAL] [`keras.ipynb`](keras.ipynb)
+2. 将完成notebook的公开在你fork的GitHub的repository上；
+3. 完成作业回收问卷（需要填写一些分类精度和代码链接）。
+
+## 通过这个项目，你将收获：
+1. 学会如何使用numpy和matplotlib来处理数据；
+2. 熟悉mnist手写数据集的形式和处理方式；
+3. 初步了解pytorch或keras等深度学习框架的使用。

README.md

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-# mnist_tutorial
+# mnist_tutorial
+A tutorial for mnist hand writen digit classification using sklearn, pytorch and keras.
+
+# Code structure
+* [`numpy_matplotlib_sklearn.ipynb`](numpy_matplotlib_sklearn.ipynb): for numpy, matplotlib and sklearn.
+* [`pytorch.ipynb`](pytorch.ipynb): for pytorch.
+* [`keras.ipynb`](keras.ipynb): for keras.
+* Reference solution: (not published yet)
+    * [`numpy_matplotlib_sklearn_solution.ipynb`](numpy_matplotlib_sklearn_solution.ipynb)
+    * [`pytorch_solution.ipynb`](pytorch_solution.ipynb)
+    * [`keras_solution.ipynb`](keras_solution.ipynb)
+
+# Requirements
+Code tested on following environments, other version should also work:
+* linux system (ubuntu 16.04) 
+* python 3.6.3
+* numpy 1.13.3
+* matplotlib 2.1.0
+* sklearn 0.19.1
+* pytorch 0.4.1
+* keras 2.1.2
+
+# For EE369 students from SJTU
+Please read [HEAR](EE369.md).

keras.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Keras Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keras is a popular deep learning framework and it's easy to get started."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "from keras.datasets import mnist\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
+    "\n",
+    "BATCH_SIZE = 128\n",
+    "NUM_CLASSES = 10\n",
+    "NUM_EPOCHS = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we read the mnist data and preprocess them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# download and load the data (split them between train and test sets)\n",
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "# expand the channel dimension\n",
+    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
+    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
+    "input_shape = (28, 28, 1)\n",
+    "\n",
+    "# make the value of pixels from [0, 255] to [0, 1] for further process\n",
+    "x_train = x_train.astype('float32') / 255.\n",
+    "x_test = x_test.astype('float32') / 255.\n",
+    "\n",
+    "# convert class vectors to binary class matrics\n",
+    "y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)\n",
+    "y_test = keras.utils.to_categorical(y_test, NUM_CLASSES)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we define the model, object function and optimizer that we use to classify."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# define the model\n",
+    "model = Sequential()\n",
+    "model.add(...)\n",
+    "model.add(...)\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "model.add(...)\n",
+    "\n",
+    "# define the object function, optimizer and metrics\n",
+    "model.compile(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we can start to train and evaluate!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# train\n",
+    "model.fit(...)\n",
+    "\n",
+    "# evaluate\n",
+    "score_train = \n",
+    "print('Training loss: %.4f, Training accuracy: %.2f%%' % (...))\n",
+    "score_test = \n",
+    "print('Testing loss: %.4f, Testing accuracy: %.2f%%' % (...))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Q6:\n",
+    "Please print the training and testing accuracy."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}