|
2 | 2 | "cells": [ |
3 | 3 | { |
4 | 4 | "cell_type": "markdown", |
5 | | - "metadata": { |
6 | | - "deletable": true, |
7 | | - "editable": true |
8 | | - }, |
| 5 | + "metadata": {}, |
9 | 6 | "source": [ |
10 | 7 | "# Ch `02`: Concept `08`" |
11 | 8 | ] |
12 | 9 | }, |
13 | 10 | { |
14 | 11 | "cell_type": "markdown", |
15 | | - "metadata": { |
16 | | - "deletable": true, |
17 | | - "editable": true |
18 | | - }, |
| 12 | + "metadata": {}, |
19 | 13 | "source": [ |
20 | 14 | "## Using TensorBoard" |
21 | 15 | ] |
22 | 16 | }, |
23 | 17 | { |
24 | 18 | "cell_type": "markdown", |
25 | | - "metadata": { |
26 | | - "deletable": true, |
27 | | - "editable": true |
28 | | - }, |
| 19 | + "metadata": {}, |
29 | 20 | "source": [ |
30 | 21 | "TensorBoard is a great way to visualize what's happening behind the code. \n", |
31 | 22 | "\n", |
|
37 | 28 | { |
38 | 29 | "cell_type": "code", |
39 | 30 | "execution_count": 1, |
40 | | - "metadata": { |
41 | | - "collapsed": false, |
42 | | - "deletable": true, |
43 | | - "editable": true |
44 | | - }, |
| 31 | + "metadata": {}, |
45 | 32 | "outputs": [], |
46 | 33 | "source": [ |
47 | 34 | "import tensorflow as tf\n", |
|
52 | 39 | }, |
53 | 40 | { |
54 | 41 | "cell_type": "markdown", |
55 | | - "metadata": { |
56 | | - "deletable": true, |
57 | | - "editable": true |
58 | | - }, |
| 42 | + "metadata": {}, |
59 | 43 | "source": [ |
60 | 44 | "The moving average is defined as follows:" |
61 | 45 | ] |
62 | 46 | }, |
63 | 47 | { |
64 | 48 | "cell_type": "code", |
65 | 49 | "execution_count": 2, |
66 | | - "metadata": { |
67 | | - "collapsed": true, |
68 | | - "deletable": true, |
69 | | - "editable": true |
70 | | - }, |
| 50 | + "metadata": {}, |
71 | 51 | "outputs": [], |
72 | 52 | "source": [ |
73 | 53 | "alpha = tf.constant(0.05)\n", |
|
79 | 59 | }, |
80 | 60 | { |
81 | 61 | "cell_type": "markdown", |
82 | | - "metadata": { |
83 | | - "deletable": true, |
84 | | - "editable": true |
85 | | - }, |
| 62 | + "metadata": {}, |
86 | 63 | "source": [ |
87 | 64 | "Here's what we care to visualize:" |
88 | 65 | ] |
89 | 66 | }, |
90 | 67 | { |
91 | 68 | "cell_type": "code", |
92 | 69 | "execution_count": 3, |
93 | | - "metadata": { |
94 | | - "collapsed": false, |
95 | | - "deletable": true, |
96 | | - "editable": true |
97 | | - }, |
| 70 | + "metadata": {}, |
98 | 71 | "outputs": [], |
99 | 72 | "source": [ |
100 | 73 | "avg_hist = tf.summary.scalar(\"running_average\", update_avg)\n", |
|
106 | 79 | }, |
107 | 80 | { |
108 | 81 | "cell_type": "markdown", |
109 | | - "metadata": { |
110 | | - "deletable": true, |
111 | | - "editable": true |
112 | | - }, |
| 82 | + "metadata": {}, |
113 | 83 | "source": [ |
114 | 84 | "Time to compute the moving averages. We'll also run the `merged` op to track how the values change:" |
115 | 85 | ] |
116 | 86 | }, |
117 | 87 | { |
118 | 88 | "cell_type": "code", |
119 | 89 | "execution_count": 4, |
120 | | - "metadata": { |
121 | | - "collapsed": false, |
122 | | - "deletable": true, |
123 | | - "editable": true |
124 | | - }, |
| 90 | + "metadata": {}, |
125 | 91 | "outputs": [ |
126 | 92 | { |
127 | 93 | "name": "stdout", |
128 | 94 | "output_type": "stream", |
129 | 95 | "text": [ |
130 | | - "10.7201648998 0.536008\n", |
131 | | - "8.48030241652 0.933223\n", |
132 | | - "8.30247090956 1.30169\n", |
133 | | - "9.24097542488 1.69865\n", |
134 | | - "10.2088633934 2.12416\n", |
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| 185 | + "10.889420930759462 9.831183\n", |
| 186 | + "9.253518007206916 9.8023\n", |
| 187 | + "11.114827470151916 9.867927\n", |
| 188 | + "9.378996323459113 9.843479\n", |
| 189 | + "9.864306640072803 9.844521\n", |
| 190 | + "11.803316169037448 9.94246\n", |
| 191 | + "10.103049011008196 9.95049\n", |
| 192 | + "8.723187258083906 9.889125\n", |
| 193 | + "8.985505621881307 9.843944\n", |
| 194 | + "10.690261212066178 9.88626\n", |
| 195 | + "8.426969249944442 9.813295\n" |
230 | 196 | ] |
231 | 197 | } |
232 | 198 | ], |
|
244 | 210 | }, |
245 | 211 | { |
246 | 212 | "cell_type": "markdown", |
247 | | - "metadata": { |
248 | | - "deletable": true, |
249 | | - "editable": true |
250 | | - }, |
| 213 | + "metadata": {}, |
251 | 214 | "source": [ |
252 | 215 | "Check out the visualization by running TensorBoard from the terminal:\n", |
253 | 216 | "\n", |
254 | 217 | " $ tensorboard --logdir=path/to/logs" |
255 | 218 | ] |
| 219 | + }, |
| 220 | + { |
| 221 | + "cell_type": "code", |
| 222 | + "execution_count": 5, |
| 223 | + "metadata": {}, |
| 224 | + "outputs": [], |
| 225 | + "source": [ |
| 226 | + "#made the logs be written successfully\n", |
| 227 | + "writer.close()" |
| 228 | + ] |
| 229 | + }, |
| 230 | + { |
| 231 | + "cell_type": "code", |
| 232 | + "execution_count": null, |
| 233 | + "metadata": {}, |
| 234 | + "outputs": [], |
| 235 | + "source": [] |
256 | 236 | } |
257 | 237 | ], |
258 | 238 | "metadata": { |
|
271 | 251 | "name": "python", |
272 | 252 | "nbconvert_exporter": "python", |
273 | 253 | "pygments_lexer": "ipython3", |
274 | | - "version": "3.5.2" |
| 254 | + "version": "3.6.4" |
275 | 255 | } |
276 | 256 | }, |
277 | 257 | "nbformat": 4, |
278 | | - "nbformat_minor": 0 |
| 258 | + "nbformat_minor": 1 |
279 | 259 | } |
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