Prepare for DAIL03

.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

.ipynb_checkpoints/hol-ai-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tech"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "import io\n",
+    "url=\"https://raw.githubusercontent.com/HaoHoo/HOL-AI/master/data/MNIST Test 10k 28x28 dense.csv\"\n",
+    "stream=requests.get(url).content\n",
+    "frame=pd.read_csv(io.StringIO(stream.decode('utf-8')), sep='\\t', header=0)\n",
+    "frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_np_array=frame.values\n",
+    "print(data_np_array)\n",
+    "print(data_np_array.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "select_img_array=data_np_array[999,1:]\n",
+    "print(select_img_array)\n",
+    "print(select_img_array.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_data=select_img_array.reshape((28,28))\n",
+    "print(img_data)\n",
+    "print(img_data.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.gray()\n",
+    "plt.imshow(img_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arrdat=np.loadtxt(\"https://raw.githubusercontent.com/HaoHoo/HOL-AI/master/data/HOL_MNIST_test.csv\", 'i2', delimiter=\",\")\n",
+    "imgdat=arrdat.reshape(10,28,28)\n",
+    "fig, axs = plt.subplots(2, 5)\n",
+    "fig.subplots_adjust(left=0.06, right=0.68, top=0.42, bottom=0.06, hspace=0.1, wspace=0)\n",
+    "images = []\n",
+    "k=0\n",
+    "for i in range(2):\n",
+    "    for j in range(5):\n",
+    "        images.append(axs[i,j].imshow(imgdat[k,]))\n",
+    "        axs[i,j].set_xticklabels('')\n",
+    "        axs[i,j].set_yticklabels('')\n",
+    "        axs[i,j].set_xticks([0,13,27])\n",
+    "        axs[i,j].set_yticks([0,13,27])\n",
+    "        k=k+1      \n",
+    "axs[1,0].set_xticklabels((0,13,27))\n",
+    "axs[1,0].set_yticklabels((27,13,0))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml import Workspace\n",
+    "ws = Workspace(\n",
+    "    workspace_id='0bbb5768107c4bc5a688ad6f5d865259',\n",
+    "    authorization_token='iX2G/38shEF1b3FBw9Kw3F1rqsfVsBAezskvc2NHn1bXGLbUL2mtdlmIFA0CVPifdfRHbV4AKCkfqrWlWFVNAQ==',\n",
+    "    endpoint='https://studioapi.azureml.net'\n",
+    ")\n",
+    "ds = ws.datasets['MNIST Test 10k 28x28 dense']\n",
+    "fm = ds.to_dataframe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}