├── CatsOrDogsClassification.ipynb
├── Tensorflow101.ipynb
└── Tensorflow2.0.ipynb


/Tensorflow101.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Tensorflow101.ipynb",
  7 |       "version": "0.3.2",
  8 |       "provenance": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "display_name": "Python 3",
 12 |       "language": "python",
 13 |       "name": "python3"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "metadata": {
 19 |         "id": "dsMHujL4WeNP",
 20 |         "colab_type": "code",
 21 |         "colab": {},
 22 |         "outputId": "af8879d1-5071-44c0-d51f-6b12327f55fe"
 23 |       },
 24 |       "cell_type": "code",
 25 |       "source": [
 26 |         "import numpy as np\n",
 27 |         "import random\n",
 28 |         "import matplotlib.pyplot as plt\n",
 29 |         "import tensorflow as tf"
 30 |       ],
 31 |       "execution_count": 0,
 32 |       "outputs": [
 33 |         {
 34 |           "output_type": "stream",
 35 |           "text": [
 36 |             "/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
 37 |             "  from ._conv import register_converters as _register_converters\n"
 38 |           ],
 39 |           "name": "stderr"
 40 |         }
 41 |       ]
 42 |     },
 43 |     {
 44 |       "metadata": {
 45 |         "scrolled": false,
 46 |         "id": "w4nTZthhWeNX",
 47 |         "colab_type": "code",
 48 |         "colab": {},
 49 |         "outputId": "b2d5c1d0-37f6-4b21-d589-56da60be98bc"
 50 |       },
 51 |       "cell_type": "code",
 52 |       "source": [
 53 |         "%matplotlib notebook\n",
 54 |         "random.seed(9)\n",
 55 |         "\n",
 56 |         "x_data = np.array([-3,-1,1,3])\n",
 57 |         "t_data = np.array([3,4.5,4.5,5.5])\n",
 58 |         "\n",
 59 |         "m_init = random.uniform(-1,1)\n",
 60 |         "c_init = 0\n",
 61 |         "\n",
 62 |         "x_input = tf.placeholder(dtype=tf.float32, shape=[None])\n",
 63 |         "m = tf.Variable(m_init, dtype=tf.float32, name=\"m\")\n",
 64 |         "c = tf.Variable(c_init, dtype=tf.float32, name=\"c\")\n",
 65 |         "\n",
 66 |         "print(x_input)\n",
 67 |         "print(m)\n",
 68 |         "print(c)\n",
 69 |         "\n",
 70 |         "mx = tf.multiply(m,x_input)\n",
 71 |         "y_model = tf.add(mx,c)\n",
 72 |         "\n",
 73 |         "with tf.Session() as sess:\n",
 74 |         "    sess.run(tf.global_variables_initializer())\n",
 75 |         "    output = sess.run(y_model, feed_dict={x_input: x_data})\n",
 76 |         "print(output)\n",
 77 |         "\n",
 78 |         "y_init = m_init*x_data + c_init\n",
 79 |         "print(y_init)\n",
 80 |         "\n",
 81 |         "fig = plt.figure()\n",
 82 |         "plt.scatter(x_data, t_data)\n",
 83 |         "plt.plot(x_data, y_init)\n",
 84 |         "fig.canvas.draw()\n",
 85 |         "\n",
 86 |         "target = tf.placeholder(dtype=tf.float32, shape=[None])\n",
 87 |         "error = tf.square(tf.subtract(y_model, target))\n",
 88 |         "loss = tf.reduce_sum(error)\n",
 89 |         "\n",
 90 |         "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)\n",
 91 |         "train_step = optimizer.minimize(loss)\n",
 92 |         "\n",
 93 |         "iterations = 100\n",
 94 |         "with tf.Session() as sess:\n",
 95 |         "    sess.run(tf.global_variables_initializer())\n",
 96 |         "    for i in range(iterations):\n",
 97 |         "        _, y_next = sess.run([train_step, y_model], feed_dict={x_input: x_data, target: t_data})\n",
 98 |         "\n",
 99 |         "#         y_next = sess.run(y_model, feed_dict={x_input: x_data, target: t_data})\n",
100 |         "\n",
101 |         "        line, = plt.plot(x_data, y_next)\n",
102 |         "        fig.canvas.draw()\n",
103 |         "        line.remove()\n",
104 |         "    \n",
105 |         "plt.plot(x_data, y_next)\n",
106 |         "fig.canvas.draw()\n"
107 |       ],
108 |       "execution_count": 0,
109 |       "outputs": [
110 |         {
111 |           "output_type": "stream",
112 |           "text": [
113 |             "Tensor(\"Placeholder:0\", shape=(?,), dtype=float32)\n",
114 |             "<tf.Variable 'm:0' shape=() dtype=float32_ref>\n",
115 |             "<tf.Variable 'c:0' shape=() dtype=float32_ref>\n",
116 |             "[ 0.22195587  0.07398529 -0.07398529 -0.22195587]\n",
117 |             "[ 0.22195585  0.07398528 -0.07398528 -0.22195585]\n"
118 |           ],
119 |           "name": "stdout"
120 |         },
121 |         {
122 |           "output_type": "display_data",
123 |           "data": {
124 |             "application/javascript": [
125 |               "/* Put everything inside the global mpl namespace */\n",
126 |               "window.mpl = {};\n",
127 |               "\n",
128 |               "\n",
129 |               "mpl.get_websocket_type = function() {\n",
130 |               "    if (typeof(WebSocket) !== 'undefined') {\n",
131 |               "        return WebSocket;\n",
132 |               "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
133 |               "        return MozWebSocket;\n",
134 |               "    } else {\n",
135 |               "        alert('Your browser does not have WebSocket support.' +\n",
136 |               "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
137 |               "              'Firefox 4 and 5 are also supported but you ' +\n",
138 |               "              'have to enable WebSockets in about:config.');\n",
139 |               "    };\n",
140 |               "}\n",
141 |               "\n",
142 |               "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
143 |               "    this.id = figure_id;\n",
144 |               "\n",
145 |               "    this.ws = websocket;\n",
146 |               "\n",
147 |               "    this.supports_binary = (this.ws.binaryType != undefined);\n",
148 |               "\n",
149 |               "    if (!this.supports_binary) {\n",
150 |               "        var warnings = document.getElementById(\"mpl-warnings\");\n",
151 |               "        if (warnings) {\n",
152 |               "            warnings.style.display = 'block';\n",
153 |               "            warnings.textContent = (\n",
154 |               "                \"This browser does not support binary websocket messages. \" +\n",
155 |               "                    \"Performance may be slow.\");\n",
156 |               "        }\n",
157 |               "    }\n",
158 |               "\n",
159 |               "    this.imageObj = new Image();\n",
160 |               "\n",
161 |               "    this.context = undefined;\n",
162 |               "    this.message = undefined;\n",
163 |               "    this.canvas = undefined;\n",
164 |               "    this.rubberband_canvas = undefined;\n",
165 |               "    this.rubberband_context = undefined;\n",
166 |               "    this.format_dropdown = undefined;\n",
167 |               "\n",
168 |               "    this.image_mode = 'full';\n",
169 |               "\n",
170 |               "    this.root = $('<div/>');\n",
171 |               "    this._root_extra_style(this.root)\n",
172 |               "    this.root.attr('style', 'display: inline-block');\n",
173 |               "\n",
174 |               "    $(parent_element).append(this.root);\n",
175 |               "\n",
176 |               "    this._init_header(this);\n",
177 |               "    this._init_canvas(this);\n",
178 |               "    this._init_toolbar(this);\n",
179 |               "\n",
180 |               "    var fig = this;\n",
181 |               "\n",
182 |               "    this.waiting = false;\n",
183 |               "\n",
184 |               "    this.ws.onopen =  function () {\n",
185 |               "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
186 |               "            fig.send_message(\"send_image_mode\", {});\n",
187 |               "            if (mpl.ratio != 1) {\n",
188 |               "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
189 |               "            }\n",
190 |               "            fig.send_message(\"refresh\", {});\n",
191 |               "        }\n",
192 |               "\n",
193 |               "    this.imageObj.onload = function() {\n",
194 |               "            if (fig.image_mode == 'full') {\n",
195 |               "                // Full images could contain transparency (where diff images\n",
196 |               "                // almost always do), so we need to clear the canvas so that\n",
197 |               "                // there is no ghosting.\n",
198 |               "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
199 |               "            }\n",
200 |               "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
201 |               "        };\n",
202 |               "\n",
203 |               "    this.imageObj.onunload = function() {\n",
204 |               "        fig.ws.close();\n",
205 |               "    }\n",
206 |               "\n",
207 |               "    this.ws.onmessage = this._make_on_message_function(this);\n",
208 |               "\n",
209 |               "    this.ondownload = ondownload;\n",
210 |               "}\n",
211 |               "\n",
212 |               "mpl.figure.prototype._init_header = function() {\n",
213 |               "    var titlebar = $(\n",
214 |               "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
215 |               "        'ui-helper-clearfix\"/>');\n",
216 |               "    var titletext = $(\n",
217 |               "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
218 |               "        'text-align: center; padding: 3px;\"/>');\n",
219 |               "    titlebar.append(titletext)\n",
220 |               "    this.root.append(titlebar);\n",
221 |               "    this.header = titletext[0];\n",
222 |               "}\n",
223 |               "\n",
224 |               "\n",
225 |               "\n",
226 |               "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
227 |               "\n",
228 |               "}\n",
229 |               "\n",
230 |               "\n",
231 |               "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
232 |               "\n",
233 |               "}\n",
234 |               "\n",
235 |               "mpl.figure.prototype._init_canvas = function() {\n",
236 |               "    var fig = this;\n",
237 |               "\n",
238 |               "    var canvas_div = $('<div/>');\n",
239 |               "\n",
240 |               "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
241 |               "\n",
242 |               "    function canvas_keyboard_event(event) {\n",
243 |               "        return fig.key_event(event, event['data']);\n",
244 |               "    }\n",
245 |               "\n",
246 |               "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
247 |               "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
248 |               "    this.canvas_div = canvas_div\n",
249 |               "    this._canvas_extra_style(canvas_div)\n",
250 |               "    this.root.append(canvas_div);\n",
251 |               "\n",
252 |               "    var canvas = $('<canvas/>');\n",
253 |               "    canvas.addClass('mpl-canvas');\n",
254 |               "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
255 |               "\n",
256 |               "    this.canvas = canvas[0];\n",
257 |               "    this.context = canvas[0].getContext(\"2d\");\n",
258 |               "\n",
259 |               "    var backingStore = this.context.backingStorePixelRatio ||\n",
260 |               "\tthis.context.webkitBackingStorePixelRatio ||\n",
261 |               "\tthis.context.mozBackingStorePixelRatio ||\n",
262 |               "\tthis.context.msBackingStorePixelRatio ||\n",
263 |               "\tthis.context.oBackingStorePixelRatio ||\n",
264 |               "\tthis.context.backingStorePixelRatio || 1;\n",
265 |               "\n",
266 |               "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
267 |               "\n",
268 |               "    var rubberband = $('<canvas/>');\n",
269 |               "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
270 |               "\n",
271 |               "    var pass_mouse_events = true;\n",
272 |               "\n",
273 |               "    canvas_div.resizable({\n",
274 |               "        start: function(event, ui) {\n",
275 |               "            pass_mouse_events = false;\n",
276 |               "        },\n",
277 |               "        resize: function(event, ui) {\n",
278 |               "            fig.request_resize(ui.size.width, ui.size.height);\n",
279 |               "        },\n",
280 |               "        stop: function(event, ui) {\n",
281 |               "            pass_mouse_events = true;\n",
282 |               "            fig.request_resize(ui.size.width, ui.size.height);\n",
283 |               "        },\n",
284 |               "    });\n",
285 |               "\n",
286 |               "    function mouse_event_fn(event) {\n",
287 |               "        if (pass_mouse_events)\n",
288 |               "            return fig.mouse_event(event, event['data']);\n",
289 |               "    }\n",
290 |               "\n",
291 |               "    rubberband.mousedown('button_press', mouse_event_fn);\n",
292 |               "    rubberband.mouseup('button_release', mouse_event_fn);\n",
293 |               "    // Throttle sequential mouse events to 1 every 20ms.\n",
294 |               "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
295 |               "\n",
296 |               "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
297 |               "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
298 |               "\n",
299 |               "    canvas_div.on(\"wheel\", function (event) {\n",
300 |               "        event = event.originalEvent;\n",
301 |               "        event['data'] = 'scroll'\n",
302 |               "        if (event.deltaY < 0) {\n",
303 |               "            event.step = 1;\n",
304 |               "        } else {\n",
305 |               "            event.step = -1;\n",
306 |               "        }\n",
307 |               "        mouse_event_fn(event);\n",
308 |               "    });\n",
309 |               "\n",
310 |               "    canvas_div.append(canvas);\n",
311 |               "    canvas_div.append(rubberband);\n",
312 |               "\n",
313 |               "    this.rubberband = rubberband;\n",
314 |               "    this.rubberband_canvas = rubberband[0];\n",
315 |               "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
316 |               "    this.rubberband_context.strokeStyle = \"#000000\";\n",
317 |               "\n",
318 |               "    this._resize_canvas = function(width, height) {\n",
319 |               "        // Keep the size of the canvas, canvas container, and rubber band\n",
320 |               "        // canvas in synch.\n",
321 |               "        canvas_div.css('width', width)\n",
322 |               "        canvas_div.css('height', height)\n",
323 |               "\n",
324 |               "        canvas.attr('width', width * mpl.ratio);\n",
325 |               "        canvas.attr('height', height * mpl.ratio);\n",
326 |               "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
327 |               "\n",
328 |               "        rubberband.attr('width', width);\n",
329 |               "        rubberband.attr('height', height);\n",
330 |               "    }\n",
331 |               "\n",
332 |               "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
333 |               "    // upon first draw.\n",
334 |               "    this._resize_canvas(600, 600);\n",
335 |               "\n",
336 |               "    // Disable right mouse context menu.\n",
337 |               "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
338 |               "        return false;\n",
339 |               "    });\n",
340 |               "\n",
341 |               "    function set_focus () {\n",
342 |               "        canvas.focus();\n",
343 |               "        canvas_div.focus();\n",
344 |               "    }\n",
345 |               "\n",
346 |               "    window.setTimeout(set_focus, 100);\n",
347 |               "}\n",
348 |               "\n",
349 |               "mpl.figure.prototype._init_toolbar = function() {\n",
350 |               "    var fig = this;\n",
351 |               "\n",
352 |               "    var nav_element = $('<div/>')\n",
353 |               "    nav_element.attr('style', 'width: 100%');\n",
354 |               "    this.root.append(nav_element);\n",
355 |               "\n",
356 |               "    // Define a callback function for later on.\n",
357 |               "    function toolbar_event(event) {\n",
358 |               "        return fig.toolbar_button_onclick(event['data']);\n",
359 |               "    }\n",
360 |               "    function toolbar_mouse_event(event) {\n",
361 |               "        return fig.toolbar_button_onmouseover(event['data']);\n",
362 |               "    }\n",
363 |               "\n",
364 |               "    for(var toolbar_ind in mpl.toolbar_items) {\n",
365 |               "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
366 |               "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
367 |               "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
368 |               "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
369 |               "\n",
370 |               "        if (!name) {\n",
371 |               "            // put a spacer in here.\n",
372 |               "            continue;\n",
373 |               "        }\n",
374 |               "        var button = $('<button/>');\n",
375 |               "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
376 |               "                        'ui-button-icon-only');\n",
377 |               "        button.attr('role', 'button');\n",
378 |               "        button.attr('aria-disabled', 'false');\n",
379 |               "        button.click(method_name, toolbar_event);\n",
380 |               "        button.mouseover(tooltip, toolbar_mouse_event);\n",
381 |               "\n",
382 |               "        var icon_img = $('<span/>');\n",
383 |               "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
384 |               "        icon_img.addClass(image);\n",
385 |               "        icon_img.addClass('ui-corner-all');\n",
386 |               "\n",
387 |               "        var tooltip_span = $('<span/>');\n",
388 |               "        tooltip_span.addClass('ui-button-text');\n",
389 |               "        tooltip_span.html(tooltip);\n",
390 |               "\n",
391 |               "        button.append(icon_img);\n",
392 |               "        button.append(tooltip_span);\n",
393 |               "\n",
394 |               "        nav_element.append(button);\n",
395 |               "    }\n",
396 |               "\n",
397 |               "    var fmt_picker_span = $('<span/>');\n",
398 |               "\n",
399 |               "    var fmt_picker = $('<select/>');\n",
400 |               "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
401 |               "    fmt_picker_span.append(fmt_picker);\n",
402 |               "    nav_element.append(fmt_picker_span);\n",
403 |               "    this.format_dropdown = fmt_picker[0];\n",
404 |               "\n",
405 |               "    for (var ind in mpl.extensions) {\n",
406 |               "        var fmt = mpl.extensions[ind];\n",
407 |               "        var option = $(\n",
408 |               "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
409 |               "        fmt_picker.append(option)\n",
410 |               "    }\n",
411 |               "\n",
412 |               "    // Add hover states to the ui-buttons\n",
413 |               "    $( \".ui-button\" ).hover(\n",
414 |               "        function() { $(this).addClass(\"ui-state-hover\");},\n",
415 |               "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
416 |               "    );\n",
417 |               "\n",
418 |               "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
419 |               "    nav_element.append(status_bar);\n",
420 |               "    this.message = status_bar[0];\n",
421 |               "}\n",
422 |               "\n",
423 |               "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
424 |               "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
425 |               "    // which will in turn request a refresh of the image.\n",
426 |               "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
427 |               "}\n",
428 |               "\n",
429 |               "mpl.figure.prototype.send_message = function(type, properties) {\n",
430 |               "    properties['type'] = type;\n",
431 |               "    properties['figure_id'] = this.id;\n",
432 |               "    this.ws.send(JSON.stringify(properties));\n",
433 |               "}\n",
434 |               "\n",
435 |               "mpl.figure.prototype.send_draw_message = function() {\n",
436 |               "    if (!this.waiting) {\n",
437 |               "        this.waiting = true;\n",
438 |               "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
439 |               "    }\n",
440 |               "}\n",
441 |               "\n",
442 |               "\n",
443 |               "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
444 |               "    var format_dropdown = fig.format_dropdown;\n",
445 |               "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
446 |               "    fig.ondownload(fig, format);\n",
447 |               "}\n",
448 |               "\n",
449 |               "\n",
450 |               "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
451 |               "    var size = msg['size'];\n",
452 |               "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
453 |               "        fig._resize_canvas(size[0], size[1]);\n",
454 |               "        fig.send_message(\"refresh\", {});\n",
455 |               "    };\n",
456 |               "}\n",
457 |               "\n",
458 |               "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
459 |               "    var x0 = msg['x0'] / mpl.ratio;\n",
460 |               "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
461 |               "    var x1 = msg['x1'] / mpl.ratio;\n",
462 |               "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
463 |               "    x0 = Math.floor(x0) + 0.5;\n",
464 |               "    y0 = Math.floor(y0) + 0.5;\n",
465 |               "    x1 = Math.floor(x1) + 0.5;\n",
466 |               "    y1 = Math.floor(y1) + 0.5;\n",
467 |               "    var min_x = Math.min(x0, x1);\n",
468 |               "    var min_y = Math.min(y0, y1);\n",
469 |               "    var width = Math.abs(x1 - x0);\n",
470 |               "    var height = Math.abs(y1 - y0);\n",
471 |               "\n",
472 |               "    fig.rubberband_context.clearRect(\n",
473 |               "        0, 0, fig.canvas.width, fig.canvas.height);\n",
474 |               "\n",
475 |               "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
476 |               "}\n",
477 |               "\n",
478 |               "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
479 |               "    // Updates the figure title.\n",
480 |               "    fig.header.textContent = msg['label'];\n",
481 |               "}\n",
482 |               "\n",
483 |               "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
484 |               "    var cursor = msg['cursor'];\n",
485 |               "    switch(cursor)\n",
486 |               "    {\n",
487 |               "    case 0:\n",
488 |               "        cursor = 'pointer';\n",
489 |               "        break;\n",
490 |               "    case 1:\n",
491 |               "        cursor = 'default';\n",
492 |               "        break;\n",
493 |               "    case 2:\n",
494 |               "        cursor = 'crosshair';\n",
495 |               "        break;\n",
496 |               "    case 3:\n",
497 |               "        cursor = 'move';\n",
498 |               "        break;\n",
499 |               "    }\n",
500 |               "    fig.rubberband_canvas.style.cursor = cursor;\n",
501 |               "}\n",
502 |               "\n",
503 |               "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
504 |               "    fig.message.textContent = msg['message'];\n",
505 |               "}\n",
506 |               "\n",
507 |               "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
508 |               "    // Request the server to send over a new figure.\n",
509 |               "    fig.send_draw_message();\n",
510 |               "}\n",
511 |               "\n",
512 |               "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
513 |               "    fig.image_mode = msg['mode'];\n",
514 |               "}\n",
515 |               "\n",
516 |               "mpl.figure.prototype.updated_canvas_event = function() {\n",
517 |               "    // Called whenever the canvas gets updated.\n",
518 |               "    this.send_message(\"ack\", {});\n",
519 |               "}\n",
520 |               "\n",
521 |               "// A function to construct a web socket function for onmessage handling.\n",
522 |               "// Called in the figure constructor.\n",
523 |               "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
524 |               "    return function socket_on_message(evt) {\n",
525 |               "        if (evt.data instanceof Blob) {\n",
526 |               "            /* FIXME: We get \"Resource interpreted as Image but\n",
527 |               "             * transferred with MIME type text/plain:\" errors on\n",
528 |               "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
529 |               "             * to be part of the websocket stream */\n",
530 |               "            evt.data.type = \"image/png\";\n",
531 |               "\n",
532 |               "            /* Free the memory for the previous frames */\n",
533 |               "            if (fig.imageObj.src) {\n",
534 |               "                (window.URL || window.webkitURL).revokeObjectURL(\n",
535 |               "                    fig.imageObj.src);\n",
536 |               "            }\n",
537 |               "\n",
538 |               "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
539 |               "                evt.data);\n",
540 |               "            fig.updated_canvas_event();\n",
541 |               "            fig.waiting = false;\n",
542 |               "            return;\n",
543 |               "        }\n",
544 |               "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
545 |               "            fig.imageObj.src = evt.data;\n",
546 |               "            fig.updated_canvas_event();\n",
547 |               "            fig.waiting = false;\n",
548 |               "            return;\n",
549 |               "        }\n",
550 |               "\n",
551 |               "        var msg = JSON.parse(evt.data);\n",
552 |               "        var msg_type = msg['type'];\n",
553 |               "\n",
554 |               "        // Call the  \"handle_{type}\" callback, which takes\n",
555 |               "        // the figure and JSON message as its only arguments.\n",
556 |               "        try {\n",
557 |               "            var callback = fig[\"handle_\" + msg_type];\n",
558 |               "        } catch (e) {\n",
559 |               "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
560 |               "            return;\n",
561 |               "        }\n",
562 |               "\n",
563 |               "        if (callback) {\n",
564 |               "            try {\n",
565 |               "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
566 |               "                callback(fig, msg);\n",
567 |               "            } catch (e) {\n",
568 |               "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
569 |               "            }\n",
570 |               "        }\n",
571 |               "    };\n",
572 |               "}\n",
573 |               "\n",
574 |               "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
575 |               "mpl.findpos = function(e) {\n",
576 |               "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
577 |               "    var targ;\n",
578 |               "    if (!e)\n",
579 |               "        e = window.event;\n",
580 |               "    if (e.target)\n",
581 |               "        targ = e.target;\n",
582 |               "    else if (e.srcElement)\n",
583 |               "        targ = e.srcElement;\n",
584 |               "    if (targ.nodeType == 3) // defeat Safari bug\n",
585 |               "        targ = targ.parentNode;\n",
586 |               "\n",
587 |               "    // jQuery normalizes the pageX and pageY\n",
588 |               "    // pageX,Y are the mouse positions relative to the document\n",
589 |               "    // offset() returns the position of the element relative to the document\n",
590 |               "    var x = e.pageX - $(targ).offset().left;\n",
591 |               "    var y = e.pageY - $(targ).offset().top;\n",
592 |               "\n",
593 |               "    return {\"x\": x, \"y\": y};\n",
594 |               "};\n",
595 |               "\n",
596 |               "/*\n",
597 |               " * return a copy of an object with only non-object keys\n",
598 |               " * we need this to avoid circular references\n",
599 |               " * http://stackoverflow.com/a/24161582/3208463\n",
600 |               " */\n",
601 |               "function simpleKeys (original) {\n",
602 |               "  return Object.keys(original).reduce(function (obj, key) {\n",
603 |               "    if (typeof original[key] !== 'object')\n",
604 |               "        obj[key] = original[key]\n",
605 |               "    return obj;\n",
606 |               "  }, {});\n",
607 |               "}\n",
608 |               "\n",
609 |               "mpl.figure.prototype.mouse_event = function(event, name) {\n",
610 |               "    var canvas_pos = mpl.findpos(event)\n",
611 |               "\n",
612 |               "    if (name === 'button_press')\n",
613 |               "    {\n",
614 |               "        this.canvas.focus();\n",
615 |               "        this.canvas_div.focus();\n",
616 |               "    }\n",
617 |               "\n",
618 |               "    var x = canvas_pos.x * mpl.ratio;\n",
619 |               "    var y = canvas_pos.y * mpl.ratio;\n",
620 |               "\n",
621 |               "    this.send_message(name, {x: x, y: y, button: event.button,\n",
622 |               "                             step: event.step,\n",
623 |               "                             guiEvent: simpleKeys(event)});\n",
624 |               "\n",
625 |               "    /* This prevents the web browser from automatically changing to\n",
626 |               "     * the text insertion cursor when the button is pressed.  We want\n",
627 |               "     * to control all of the cursor setting manually through the\n",
628 |               "     * 'cursor' event from matplotlib */\n",
629 |               "    event.preventDefault();\n",
630 |               "    return false;\n",
631 |               "}\n",
632 |               "\n",
633 |               "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
634 |               "    // Handle any extra behaviour associated with a key event\n",
635 |               "}\n",
636 |               "\n",
637 |               "mpl.figure.prototype.key_event = function(event, name) {\n",
638 |               "\n",
639 |               "    // Prevent repeat events\n",
640 |               "    if (name == 'key_press')\n",
641 |               "    {\n",
642 |               "        if (event.which === this._key)\n",
643 |               "            return;\n",
644 |               "        else\n",
645 |               "            this._key = event.which;\n",
646 |               "    }\n",
647 |               "    if (name == 'key_release')\n",
648 |               "        this._key = null;\n",
649 |               "\n",
650 |               "    var value = '';\n",
651 |               "    if (event.ctrlKey && event.which != 17)\n",
652 |               "        value += \"ctrl+\";\n",
653 |               "    if (event.altKey && event.which != 18)\n",
654 |               "        value += \"alt+\";\n",
655 |               "    if (event.shiftKey && event.which != 16)\n",
656 |               "        value += \"shift+\";\n",
657 |               "\n",
658 |               "    value += 'k';\n",
659 |               "    value += event.which.toString();\n",
660 |               "\n",
661 |               "    this._key_event_extra(event, name);\n",
662 |               "\n",
663 |               "    this.send_message(name, {key: value,\n",
664 |               "                             guiEvent: simpleKeys(event)});\n",
665 |               "    return false;\n",
666 |               "}\n",
667 |               "\n",
668 |               "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
669 |               "    if (name == 'download') {\n",
670 |               "        this.handle_save(this, null);\n",
671 |               "    } else {\n",
672 |               "        this.send_message(\"toolbar_button\", {name: name});\n",
673 |               "    }\n",
674 |               "};\n",
675 |               "\n",
676 |               "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
677 |               "    this.message.textContent = tooltip;\n",
678 |               "};\n",
679 |               "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
680 |               "\n",
681 |               "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
682 |               "\n",
683 |               "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
684 |               "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
685 |               "    // object with the appropriate methods. Currently this is a non binary\n",
686 |               "    // socket, so there is still some room for performance tuning.\n",
687 |               "    var ws = {};\n",
688 |               "\n",
689 |               "    ws.close = function() {\n",
690 |               "        comm.close()\n",
691 |               "    };\n",
692 |               "    ws.send = function(m) {\n",
693 |               "        //console.log('sending', m);\n",
694 |               "        comm.send(m);\n",
695 |               "    };\n",
696 |               "    // Register the callback with on_msg.\n",
697 |               "    comm.on_msg(function(msg) {\n",
698 |               "        //console.log('receiving', msg['content']['data'], msg);\n",
699 |               "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
700 |               "        ws.onmessage(msg['content']['data'])\n",
701 |               "    });\n",
702 |               "    return ws;\n",
703 |               "}\n",
704 |               "\n",
705 |               "mpl.mpl_figure_comm = function(comm, msg) {\n",
706 |               "    // This is the function which gets called when the mpl process\n",
707 |               "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
708 |               "\n",
709 |               "    var id = msg.content.data.id;\n",
710 |               "    // Get hold of the div created by the display call when the Comm\n",
711 |               "    // socket was opened in Python.\n",
712 |               "    var element = $(\"#\" + id);\n",
713 |               "    var ws_proxy = comm_websocket_adapter(comm)\n",
714 |               "\n",
715 |               "    function ondownload(figure, format) {\n",
716 |               "        window.open(figure.imageObj.src);\n",
717 |               "    }\n",
718 |               "\n",
719 |               "    var fig = new mpl.figure(id, ws_proxy,\n",
720 |               "                           ondownload,\n",
721 |               "                           element.get(0));\n",
722 |               "\n",
723 |               "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
724 |               "    // web socket which is closed, not our websocket->open comm proxy.\n",
725 |               "    ws_proxy.onopen();\n",
726 |               "\n",
727 |               "    fig.parent_element = element.get(0);\n",
728 |               "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
729 |               "    if (!fig.cell_info) {\n",
730 |               "        console.error(\"Failed to find cell for figure\", id, fig);\n",
731 |               "        return;\n",
732 |               "    }\n",
733 |               "\n",
734 |               "    var output_index = fig.cell_info[2]\n",
735 |               "    var cell = fig.cell_info[0];\n",
736 |               "\n",
737 |               "};\n",
738 |               "\n",
739 |               "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
740 |               "    var width = fig.canvas.width/mpl.ratio\n",
741 |               "    fig.root.unbind('remove')\n",
742 |               "\n",
743 |               "    // Update the output cell to use the data from the current canvas.\n",
744 |               "    fig.push_to_output();\n",
745 |               "    var dataURL = fig.canvas.toDataURL();\n",
746 |               "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
747 |               "    // the notebook keyboard shortcuts fail.\n",
748 |               "    IPython.keyboard_manager.enable()\n",
749 |               "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
750 |               "    fig.close_ws(fig, msg);\n",
751 |               "}\n",
752 |               "\n",
753 |               "mpl.figure.prototype.close_ws = function(fig, msg){\n",
754 |               "    fig.send_message('closing', msg);\n",
755 |               "    // fig.ws.close()\n",
756 |               "}\n",
757 |               "\n",
758 |               "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
759 |               "    // Turn the data on the canvas into data in the output cell.\n",
760 |               "    var width = this.canvas.width/mpl.ratio\n",
761 |               "    var dataURL = this.canvas.toDataURL();\n",
762 |               "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
763 |               "}\n",
764 |               "\n",
765 |               "mpl.figure.prototype.updated_canvas_event = function() {\n",
766 |               "    // Tell IPython that the notebook contents must change.\n",
767 |               "    IPython.notebook.set_dirty(true);\n",
768 |               "    this.send_message(\"ack\", {});\n",
769 |               "    var fig = this;\n",
770 |               "    // Wait a second, then push the new image to the DOM so\n",
771 |               "    // that it is saved nicely (might be nice to debounce this).\n",
772 |               "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
773 |               "}\n",
774 |               "\n",
775 |               "mpl.figure.prototype._init_toolbar = function() {\n",
776 |               "    var fig = this;\n",
777 |               "\n",
778 |               "    var nav_element = $('<div/>')\n",
779 |               "    nav_element.attr('style', 'width: 100%');\n",
780 |               "    this.root.append(nav_element);\n",
781 |               "\n",
782 |               "    // Define a callback function for later on.\n",
783 |               "    function toolbar_event(event) {\n",
784 |               "        return fig.toolbar_button_onclick(event['data']);\n",
785 |               "    }\n",
786 |               "    function toolbar_mouse_event(event) {\n",
787 |               "        return fig.toolbar_button_onmouseover(event['data']);\n",
788 |               "    }\n",
789 |               "\n",
790 |               "    for(var toolbar_ind in mpl.toolbar_items){\n",
791 |               "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
792 |               "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
793 |               "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
794 |               "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
795 |               "\n",
796 |               "        if (!name) { continue; };\n",
797 |               "\n",
798 |               "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
799 |               "        button.click(method_name, toolbar_event);\n",
800 |               "        button.mouseover(tooltip, toolbar_mouse_event);\n",
801 |               "        nav_element.append(button);\n",
802 |               "    }\n",
803 |               "\n",
804 |               "    // Add the status bar.\n",
805 |               "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
806 |               "    nav_element.append(status_bar);\n",
807 |               "    this.message = status_bar[0];\n",
808 |               "\n",
809 |               "    // Add the close button to the window.\n",
810 |               "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
811 |               "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
812 |               "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
813 |               "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
814 |               "    buttongrp.append(button);\n",
815 |               "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
816 |               "    titlebar.prepend(buttongrp);\n",
817 |               "}\n",
818 |               "\n",
819 |               "mpl.figure.prototype._root_extra_style = function(el){\n",
820 |               "    var fig = this\n",
821 |               "    el.on(\"remove\", function(){\n",
822 |               "\tfig.close_ws(fig, {});\n",
823 |               "    });\n",
824 |               "}\n",
825 |               "\n",
826 |               "mpl.figure.prototype._canvas_extra_style = function(el){\n",
827 |               "    // this is important to make the div 'focusable\n",
828 |               "    el.attr('tabindex', 0)\n",
829 |               "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
830 |               "    // off when our div gets focus\n",
831 |               "\n",
832 |               "    // location in version 3\n",
833 |               "    if (IPython.notebook.keyboard_manager) {\n",
834 |               "        IPython.notebook.keyboard_manager.register_events(el);\n",
835 |               "    }\n",
836 |               "    else {\n",
837 |               "        // location in version 2\n",
838 |               "        IPython.keyboard_manager.register_events(el);\n",
839 |               "    }\n",
840 |               "\n",
841 |               "}\n",
842 |               "\n",
843 |               "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
844 |               "    var manager = IPython.notebook.keyboard_manager;\n",
845 |               "    if (!manager)\n",
846 |               "        manager = IPython.keyboard_manager;\n",
847 |               "\n",
848 |               "    // Check for shift+enter\n",
849 |               "    if (event.shiftKey && event.which == 13) {\n",
850 |               "        this.canvas_div.blur();\n",
851 |               "        event.shiftKey = false;\n",
852 |               "        // Send a \"J\" for go to next cell\n",
853 |               "        event.which = 74;\n",
854 |               "        event.keyCode = 74;\n",
855 |               "        manager.command_mode();\n",
856 |               "        manager.handle_keydown(event);\n",
857 |               "    }\n",
858 |               "}\n",
859 |               "\n",
860 |               "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
861 |               "    fig.ondownload(fig, null);\n",
862 |               "}\n",
863 |               "\n",
864 |               "\n",
865 |               "mpl.find_output_cell = function(html_output) {\n",
866 |               "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
867 |               "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
868 |               "    // IPython event is triggered only after the cells have been serialised, which for\n",
869 |               "    // our purposes (turning an active figure into a static one), is too late.\n",
870 |               "    var cells = IPython.notebook.get_cells();\n",
871 |               "    var ncells = cells.length;\n",
872 |               "    for (var i=0; i<ncells; i++) {\n",
873 |               "        var cell = cells[i];\n",
874 |               "        if (cell.cell_type === 'code'){\n",
875 |               "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
876 |               "                var data = cell.output_area.outputs[j];\n",
877 |               "                if (data.data) {\n",
878 |               "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
879 |               "                    data = data.data;\n",
880 |               "                }\n",
881 |               "                if (data['text/html'] == html_output) {\n",
882 |               "                    return [cell, data, j];\n",
883 |               "                }\n",
884 |               "            }\n",
885 |               "        }\n",
886 |               "    }\n",
887 |               "}\n",
888 |               "\n",
889 |               "// Register the function which deals with the matplotlib target/channel.\n",
890 |               "// The kernel may be null if the page has been refreshed.\n",
891 |               "if (IPython.notebook.kernel != null) {\n",
892 |               "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
893 |               "}\n"
894 |             ],
895 |             "text/plain": [
896 |               "<IPython.core.display.Javascript object>"
897 |             ]
898 |           },
899 |           "metadata": {
900 |             "tags": []
901 |           }
902 |         },
903 |         {
904 |           "output_type": "display_data",
905 |           "data": {
906 |             "text/html": [
907 |               "<img 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\" width=\"640\">"
908 |             ],
909 |             "text/plain": [
910 |               "<IPython.core.display.HTML object>"
911 |             ]
912 |           },
913 |           "metadata": {
914 |             "tags": []
915 |           }
916 |         }
917 |       ]
918 |     },
919 |     {
920 |       "metadata": {
921 |         "id": "oo5k3IGsWeNb",
922 |         "colab_type": "code",
923 |         "colab": {}
924 |       },
925 |       "cell_type": "code",
926 |       "source": [
927 |         ""
928 |       ],
929 |       "execution_count": 0,
930 |       "outputs": []
931 |     }
932 |   ]
933 | }


--------------------------------------------------------------------------------
/Tensorflow2.0.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Tensorflow2.0.ipynb",
  7 |       "version": "0.3.2",
  8 |       "provenance": []
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     }
 14 |   },
 15 |   "cells": [
 16 |     {
 17 |       "metadata": {
 18 |         "id": "cRA5-FJx0mx0",
 19 |         "colab_type": "code",
 20 |         "colab": {}
 21 |       },
 22 |       "cell_type": "code",
 23 |       "source": [
 24 |         "# https://www.tensorflow.org/install\n",
 25 |         "!pip install tensorflow==2.0.0-alpha0 "
 26 |       ],
 27 |       "execution_count": 0,
 28 |       "outputs": []
 29 |     },
 30 |     {
 31 |       "metadata": {
 32 |         "id": "fzXmobFW1gaR",
 33 |         "colab_type": "code",
 34 |         "colab": {
 35 |           "base_uri": "https://localhost:8080/",
 36 |           "height": 34
 37 |         },
 38 |         "outputId": "957214a7-6d4d-44a0-f322-c3f92be0fbe9"
 39 |       },
 40 |       "cell_type": "code",
 41 |       "source": [
 42 |         "import numpy as np\n",
 43 |         "import tensorflow as tf\n",
 44 |         "print(\"Tensorflow:\", tf.__version__)"
 45 |       ],
 46 |       "execution_count": 3,
 47 |       "outputs": [
 48 |         {
 49 |           "output_type": "stream",
 50 |           "text": [
 51 |             "Tensorflow: 2.0.0-alpha0\n"
 52 |           ],
 53 |           "name": "stdout"
 54 |         }
 55 |       ]
 56 |     },
 57 |     {
 58 |       "metadata": {
 59 |         "id": "8PTv_P7y7Ihf",
 60 |         "colab_type": "code",
 61 |         "colab": {
 62 |           "base_uri": "https://localhost:8080/",
 63 |           "height": 119
 64 |         },
 65 |         "outputId": "856e61e0-137b-4077-cfc7-e8bd1bc1b8c3"
 66 |       },
 67 |       "cell_type": "code",
 68 |       "source": [
 69 |         "# https://www.tensorflow.org/overview\n",
 70 |         "mnist = tf.keras.datasets.mnist\n",
 71 |         "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
 72 |         "print(x_train.shape)\n",
 73 |         "print(y_train.shape)\n",
 74 |         "print(x_test.shape)\n",
 75 |         "print(y_test.shape)"
 76 |       ],
 77 |       "execution_count": 16,
 78 |       "outputs": [
 79 |         {
 80 |           "output_type": "stream",
 81 |           "text": [
 82 |             "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
 83 |             "11493376/11490434 [==============================] - 0s 0us/step\n",
 84 |             "(60000, 28, 28)\n",
 85 |             "(60000,)\n",
 86 |             "(10000, 28, 28)\n",
 87 |             "(10000,)\n"
 88 |           ],
 89 |           "name": "stdout"
 90 |         }
 91 |       ]
 92 |     },
 93 |     {
 94 |       "metadata": {
 95 |         "id": "VrBzRUA_7Iop",
 96 |         "colab_type": "code",
 97 |         "colab": {
 98 |           "base_uri": "https://localhost:8080/",
 99 |           "height": 364
100 |         },
101 |         "outputId": "b8d34d38-04c1-47a0-b298-8b251c4038ef"
102 |       },
103 |       "cell_type": "code",
104 |       "source": [
105 |         "import matplotlib.pyplot as plt\n",
106 |         "plt.imshow(x_train[0])\n",
107 |         "print(y_train[0])"
108 |       ],
109 |       "execution_count": 18,
110 |       "outputs": [
111 |         {
112 |           "output_type": "stream",
113 |           "text": [
114 |             "5\n"
115 |           ],
116 |           "name": "stdout"
117 |         },
118 |         {
119 |           "output_type": "display_data",
120 |           "data": {
121 |             "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFKCAYAAACU6307AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
122 |             "text/plain": [
123 |               "<Figure size 576x396 with 1 Axes>"
124 |             ]
125 |           },
126 |           "metadata": {
127 |             "tags": []
128 |           }
129 |         }
130 |       ]
131 |     },
132 |     {
133 |       "metadata": {
134 |         "id": "vyBTfMcq7IuF",
135 |         "colab_type": "code",
136 |         "colab": {}
137 |       },
138 |       "cell_type": "code",
139 |       "source": [
140 |         "x_train = x_train / 255.0 \n",
141 |         "x_test = x_test / 255.0"
142 |       ],
143 |       "execution_count": 0,
144 |       "outputs": []
145 |     },
146 |     {
147 |       "metadata": {
148 |         "id": "tf67b-Hn7Izv",
149 |         "colab_type": "code",
150 |         "colab": {
151 |           "base_uri": "https://localhost:8080/",
152 |           "height": 204
153 |         },
154 |         "outputId": "12963b45-7b3f-4dcd-c6d0-5feaa17747d4"
155 |       },
156 |       "cell_type": "code",
157 |       "source": [
158 |         "model = tf.keras.models.Sequential([\n",
159 |         "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
160 |         "  tf.keras.layers.Dense(128, activation='relu'),\n",
161 |         "  tf.keras.layers.Dense(10, activation='softmax')\n",
162 |         "])\n",
163 |         "\n",
164 |         "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
165 |         "model.fit(x_train, y_train, batch_size=50, epochs=5)"
166 |       ],
167 |       "execution_count": 20,
168 |       "outputs": [
169 |         {
170 |           "output_type": "stream",
171 |           "text": [
172 |             "Epoch 1/5\n",
173 |             "60000/60000 [==============================] - 4s 63us/sample - loss: 0.2835 - accuracy: 0.9202\n",
174 |             "Epoch 2/5\n",
175 |             "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1236 - accuracy: 0.9641\n",
176 |             "Epoch 3/5\n",
177 |             "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0872 - accuracy: 0.9743\n",
178 |             "Epoch 4/5\n",
179 |             "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0651 - accuracy: 0.9806\n",
180 |             "Epoch 5/5\n",
181 |             "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0519 - accuracy: 0.9842\n"
182 |           ],
183 |           "name": "stdout"
184 |         },
185 |         {
186 |           "output_type": "execute_result",
187 |           "data": {
188 |             "text/plain": [
189 |               "<tensorflow.python.keras.callbacks.History at 0x7fe07b3dcd30>"
190 |             ]
191 |           },
192 |           "metadata": {
193 |             "tags": []
194 |           },
195 |           "execution_count": 20
196 |         }
197 |       ]
198 |     },
199 |     {
200 |       "metadata": {
201 |         "id": "zlJOBxkk7I44",
202 |         "colab_type": "code",
203 |         "colab": {
204 |           "base_uri": "https://localhost:8080/",
205 |           "height": 51
206 |         },
207 |         "outputId": "969d6aa6-0ef2-484e-f12c-579b240f4f31"
208 |       },
209 |       "cell_type": "code",
210 |       "source": [
211 |         "model.evaluate(x_test, y_test)"
212 |       ],
213 |       "execution_count": 21,
214 |       "outputs": [
215 |         {
216 |           "output_type": "stream",
217 |           "text": [
218 |             "10000/10000 [==============================] - 0s 43us/sample - loss: 0.0808 - accuracy: 0.9747\n"
219 |           ],
220 |           "name": "stdout"
221 |         },
222 |         {
223 |           "output_type": "execute_result",
224 |           "data": {
225 |             "text/plain": [
226 |               "[0.08076499402970076, 0.9747]"
227 |             ]
228 |           },
229 |           "metadata": {
230 |             "tags": []
231 |           },
232 |           "execution_count": 21
233 |         }
234 |       ]
235 |     },
236 |     {
237 |       "metadata": {
238 |         "id": "BlkVuyeU7I9r",
239 |         "colab_type": "code",
240 |         "colab": {
241 |           "base_uri": "https://localhost:8080/",
242 |           "height": 34
243 |         },
244 |         "outputId": "5d405b31-9c7a-406a-f79d-7dd4c3eb9849"
245 |       },
246 |       "cell_type": "code",
247 |       "source": [
248 |         "np.argmax(model(x_train[:1]))"
249 |       ],
250 |       "execution_count": 23,
251 |       "outputs": [
252 |         {
253 |           "output_type": "execute_result",
254 |           "data": {
255 |             "text/plain": [
256 |               "5"
257 |             ]
258 |           },
259 |           "metadata": {
260 |             "tags": []
261 |           },
262 |           "execution_count": 23
263 |         }
264 |       ]
265 |     },
266 |     {
267 |       "metadata": {
268 |         "id": "2NhNqnkY7JCs",
269 |         "colab_type": "code",
270 |         "colab": {}
271 |       },
272 |       "cell_type": "code",
273 |       "source": [
274 |         "# https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model\n",
275 |         "class MyModel(tf.keras.Model):\n",
276 |         "    def __init__(self):\n",
277 |         "        super().__init__()\n",
278 |         "        self.flat = tf.keras.layers.Flatten(input_shape=(28, 28))\n",
279 |         "        self.dense1 = tf.keras.layers.Dense(128, activation=tf.nn.relu)\n",
280 |         "        self.dense2 = tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
281 |         "        \n",
282 |         "        # https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/losses/SparseCategoricalCrossentropy\n",
283 |         "        self.loss = tf.keras.losses.SparseCategoricalCrossentropy()\n",
284 |         "        # https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/metrics/Accuracy\n",
285 |         "        self.accuracy = tf.keras.metrics.Accuracy()\n",
286 |         "        # https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/optimizers/Adam\n",
287 |         "        self.optimizer = tf.optimizers.Adam()\n",
288 |         "\n",
289 |         "    def call(self, inputs):\n",
290 |         "        flat = self.flat(inputs)\n",
291 |         "        dense1 = self.dense1(flat)\n",
292 |         "        return self.dense2(dense1)\n",
293 |         "    \n",
294 |         "    def get_loss(self, inputs, expected):\n",
295 |         "        return self.loss(expected, self(inputs))\n",
296 |         "    \n",
297 |         "    def get_accuracy(self, inputs, expected):\n",
298 |         "        return self.accuracy(expected, tf.argmax(self(inputs), axis=1))\n",
299 |         "    \n",
300 |         "    def optimize(self, inputs, expected):\n",
301 |         "        loss = lambda: self.get_loss(inputs, expected)\n",
302 |         "        self.optimizer.minimize(loss=loss, var_list=self.trainable_weights)\n",
303 |         "        \n",
304 |         "    def fit(self, x, y, batch_size, epochs):\n",
305 |         "        num_samples = x.shape[0]\n",
306 |         "        for ep in range(epochs):\n",
307 |         "            for i_start in range(0, num_samples, batch_size):\n",
308 |         "                x_batch = x[i_start : i_start+batch_size]\n",
309 |         "                y_batch = y[i_start : i_start+batch_size]\n",
310 |         "                self.optimize(x_batch, y_batch)\n",
311 |         "            loss = self.get_loss(x,y)\n",
312 |         "            acc = self.get_accuracy(x,y)\n",
313 |         "            print(\"Epoch: {}, loss: {}, accuracy: {}\".format(ep, loss, acc))"
314 |       ],
315 |       "execution_count": 0,
316 |       "outputs": []
317 |     },
318 |     {
319 |       "metadata": {
320 |         "id": "Vq4zjxu37JIZ",
321 |         "colab_type": "code",
322 |         "colab": {
323 |           "base_uri": "https://localhost:8080/",
324 |           "height": 102
325 |         },
326 |         "outputId": "13e54c1b-7676-4677-cd9c-8d8a3b721ee0"
327 |       },
328 |       "cell_type": "code",
329 |       "source": [
330 |         "my_model = MyModel()\n",
331 |         "my_model.fit(x_train, y_train, batch_size=50, epochs=5)"
332 |       ],
333 |       "execution_count": 25,
334 |       "outputs": [
335 |         {
336 |           "output_type": "stream",
337 |           "text": [
338 |             "Epoch: 0, loss: 0.17014266550540924, accuracy: 0.9492999911308289\n",
339 |             "Epoch: 1, loss: 0.11693110316991806, accuracy: 0.9563249945640564\n",
340 |             "Epoch: 2, loss: 0.08534319698810577, accuracy: 0.9618499875068665\n",
341 |             "Epoch: 3, loss: 0.07103550434112549, accuracy: 0.9656083583831787\n",
342 |             "Epoch: 4, loss: 0.0587860606610775, accuracy: 0.9685999751091003\n"
343 |           ],
344 |           "name": "stdout"
345 |         }
346 |       ]
347 |     },
348 |     {
349 |       "metadata": {
350 |         "id": "ohcmk04A7JPw",
351 |         "colab_type": "code",
352 |         "colab": {
353 |           "base_uri": "https://localhost:8080/",
354 |           "height": 34
355 |         },
356 |         "outputId": "8e86f726-0f88-428d-9b45-5ed096a716a0"
357 |       },
358 |       "cell_type": "code",
359 |       "source": [
360 |         "np.argmax(my_model(x_train[:1]))"
361 |       ],
362 |       "execution_count": 26,
363 |       "outputs": [
364 |         {
365 |           "output_type": "execute_result",
366 |           "data": {
367 |             "text/plain": [
368 |               "5"
369 |             ]
370 |           },
371 |           "metadata": {
372 |             "tags": []
373 |           },
374 |           "execution_count": 26
375 |         }
376 |       ]
377 |     },
378 |     {
379 |       "metadata": {
380 |         "id": "y6pSN3Ma18tX",
381 |         "colab_type": "code",
382 |         "colab": {
383 |           "base_uri": "https://localhost:8080/",
384 |           "height": 34
385 |         },
386 |         "outputId": "5fe67c3a-ee9c-4ad6-889f-0793bdcc354b"
387 |       },
388 |       "cell_type": "code",
389 |       "source": [
390 |         "a = tf.Variable(4.0)\n",
391 |         "print(a.numpy())"
392 |       ],
393 |       "execution_count": 5,
394 |       "outputs": [
395 |         {
396 |           "output_type": "stream",
397 |           "text": [
398 |             "4.0\n"
399 |           ],
400 |           "name": "stdout"
401 |         }
402 |       ]
403 |     },
404 |     {
405 |       "metadata": {
406 |         "id": "i7A99VFd2ghV",
407 |         "colab_type": "code",
408 |         "colab": {
409 |           "base_uri": "https://localhost:8080/",
410 |           "height": 34
411 |         },
412 |         "outputId": "3b190deb-22d7-450d-e92c-819402372bde"
413 |       },
414 |       "cell_type": "code",
415 |       "source": [
416 |         "def f(x):\n",
417 |         "    return a * x\n",
418 |         "print(f(5))"
419 |       ],
420 |       "execution_count": 6,
421 |       "outputs": [
422 |         {
423 |           "output_type": "stream",
424 |           "text": [
425 |             "tf.Tensor(20.0, shape=(), dtype=float32)\n"
426 |           ],
427 |           "name": "stdout"
428 |         }
429 |       ]
430 |     },
431 |     {
432 |       "metadata": {
433 |         "id": "QtAZwDlX29Qe",
434 |         "colab_type": "code",
435 |         "colab": {
436 |           "base_uri": "https://localhost:8080/",
437 |           "height": 34
438 |         },
439 |         "outputId": "d3076286-03f2-4f16-e143-84373485cb43"
440 |       },
441 |       "cell_type": "code",
442 |       "source": [
443 |         "loss = lambda: abs(f(5) - 5)\n",
444 |         "print(loss)"
445 |       ],
446 |       "execution_count": 13,
447 |       "outputs": [
448 |         {
449 |           "output_type": "stream",
450 |           "text": [
451 |             "<function <lambda> at 0x7fe07b932268>\n"
452 |           ],
453 |           "name": "stdout"
454 |         }
455 |       ]
456 |     },
457 |     {
458 |       "metadata": {
459 |         "id": "rnv3khHF4TpC",
460 |         "colab_type": "code",
461 |         "colab": {
462 |           "base_uri": "https://localhost:8080/",
463 |           "height": 187
464 |         },
465 |         "outputId": "92e0396d-0f4e-4533-86f9-b96a4d0c9690"
466 |       },
467 |       "cell_type": "code",
468 |       "source": [
469 |         "optimizer = tf.optimizers.Adam(learning_rate=1)\n",
470 |         "for _ in range(10):\n",
471 |         "    optimizer.minimize(loss, var_list=[a])\n",
472 |         "    print(a.numpy(), f(5))"
473 |       ],
474 |       "execution_count": 15,
475 |       "outputs": [
476 |         {
477 |           "output_type": "stream",
478 |           "text": [
479 |             "2.0000014 tf.Tensor(10.000008, shape=(), dtype=float32)\n",
480 |             "1.0000052 tf.Tensor(5.000026, shape=(), dtype=float32)\n",
481 |             "2.7418137e-06 tf.Tensor(1.3709068e-05, shape=(), dtype=float32)\n",
482 |             "-0.41843143 tf.Tensor(-2.0921571, shape=(), dtype=float32)\n",
483 |             "-0.4904929 tf.Tensor(-2.4524646, shape=(), dtype=float32)\n",
484 |             "-0.33375463 tf.Tensor(-1.6687732, shape=(), dtype=float32)\n",
485 |             "-0.015380621 tf.Tensor(-0.076903105, shape=(), dtype=float32)\n",
486 |             "0.42267495 tf.Tensor(2.1133747, shape=(), dtype=float32)\n",
487 |             "0.95246416 tf.Tensor(4.762321, shape=(), dtype=float32)\n",
488 |             "1.5544472 tf.Tensor(7.772236, shape=(), dtype=float32)\n"
489 |           ],
490 |           "name": "stdout"
491 |         }
492 |       ]
493 |     },
494 |     {
495 |       "metadata": {
496 |         "id": "MR4-3Eng5CaJ",
497 |         "colab_type": "code",
498 |         "colab": {}
499 |       },
500 |       "cell_type": "code",
501 |       "source": [
502 |         ""
503 |       ],
504 |       "execution_count": 0,
505 |       "outputs": []
506 |     }
507 |   ]
508 | }


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