├── README.md
└── Hello,_TensorFlow_bit_ly_ht_colab.ipynb
/README.md:
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1 | # Hello-TensorFlow
2 | Contains accompanying Colab Notebook for my talk Hello, TensorFlow ([bit.ly/gdg-goa-20](bit.ly/gdg-goa-20)). This was presented for [GDG Goa](https://www.gdggoa.com/) in an online session. The objective of the talk is to introduce basic Deep Learning recipes with TensorFlow (2.x) to beginners in a friendly way.
3 |
4 | ## Contents:
5 | - What is TensorFlow
6 | - Tensor
7 | - Flow
8 | - Basic DL recipes
9 | - Data (features, labels)
10 | - Affine transformations
11 | - Loss functions
12 | - Gradient-based learning
13 | - Backpropagation
14 | - Where DL fits within ML?
15 | - Future directions
16 |
17 |
18 |
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/Hello,_TensorFlow_bit_ly_ht_colab.ipynb:
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1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "name": "Hello, TensorFlow - bit.ly/ht-colab.ipynb",
7 | "provenance": [],
8 | "collapsed_sections": [],
9 | "authorship_tag": "ABX9TyMis29a2+/mKY1+HULAtZ8Q",
10 | "include_colab_link": true
11 | },
12 | "kernelspec": {
13 | "name": "python3",
14 | "display_name": "Python 3"
15 | }
16 | },
17 | "cells": [
18 | {
19 | "cell_type": "markdown",
20 | "metadata": {
21 | "id": "view-in-github",
22 | "colab_type": "text"
23 | },
24 | "source": [
25 | "
"
26 | ]
27 | },
28 | {
29 | "cell_type": "markdown",
30 | "metadata": {
31 | "id": "nL04uIaNO2Qt",
32 | "colab_type": "text"
33 | },
34 | "source": [
35 | "This Colab notebook accompanies this talk: http://bit.ly/gdg-goa-20.\n",
36 | "\n",
37 | "If you do not know how to use this particular environment (Colab), please follow these instructions: https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#1. "
38 | ]
39 | },
40 | {
41 | "cell_type": "code",
42 | "metadata": {
43 | "id": "fb50EFQMOyHH",
44 | "colab_type": "code",
45 | "colab": {
46 | "base_uri": "https://localhost:8080/",
47 | "height": 34
48 | },
49 | "outputId": "e5f569fb-9e96-4635-b210-0c79f6c37a3a"
50 | },
51 | "source": [
52 | "# Our big elephant in the room\n",
53 | "import tensorflow as tf\n",
54 | "print(tf.__version__)"
55 | ],
56 | "execution_count": 3,
57 | "outputs": [
58 | {
59 | "output_type": "stream",
60 | "text": [
61 | "2.2.0-rc1\n"
62 | ],
63 | "name": "stdout"
64 | }
65 | ]
66 | },
67 | {
68 | "cell_type": "markdown",
69 | "metadata": {
70 | "id": "w86_BiIrRHKa",
71 | "colab_type": "text"
72 | },
73 | "source": [
74 | "The following code is referred from here: https://colab.research.google.com/drive/1UCJt8EYjlzCs1H1d1X0iDGYJsHKwu-NO."
75 | ]
76 | },
77 | {
78 | "cell_type": "code",
79 | "metadata": {
80 | "id": "oCVdxD2APIhp",
81 | "colab_type": "code",
82 | "outputId": "66724cf0-7661-46dc-9c1c-a2a5ef6e245f",
83 | "colab": {
84 | "base_uri": "https://localhost:8080/",
85 | "height": 282
86 | }
87 | },
88 | "source": [
89 | "# Import other libraries\n",
90 | "import numpy as np\n",
91 | "import random\n",
92 | "import matplotlib.pyplot as plt\n",
93 | "%matplotlib inline\n",
94 | "\n",
95 | "# Prepare a dataset.\n",
96 | "num_samples = 10000\n",
97 | "negative_samples = np.random.multivariate_normal(\n",
98 | " mean=[0, 3], cov=[[1, 0.5],[0.5, 1]], size=num_samples)\n",
99 | "positive_samples = np.random.multivariate_normal(\n",
100 | " mean=[3, 0], cov=[[1, 0.5],[0.5, 1]], size=num_samples)\n",
101 | "features = np.vstack((negative_samples, positive_samples)).astype(np.float32)\n",
102 | "labels = np.vstack((np.zeros((num_samples, 1), dtype='float32'),\n",
103 | " np.ones((num_samples, 1), dtype='float32')))\n",
104 | "\n",
105 | "plt.scatter(features[:, 0], features[:, 1], c=labels[:, 0])"
106 | ],
107 | "execution_count": 4,
108 | "outputs": [
109 | {
110 | "output_type": "execute_result",
111 | "data": {
112 | "text/plain": [
113 | ""
114 | ]
115 | },
116 | "metadata": {
117 | "tags": []
118 | },
119 | "execution_count": 4
120 | },
121 | {
122 | "output_type": "display_data",
123 | "data": {
124 | "image/png": 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fspnxwyax/Z9UqtZJof/zfVnww5Kw2Qar1asSkpz/uzd/YvaE3/G6vQXCv2vD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\n9nxILB7gntxUbphckuDZCZKrgXXZN5MGnPDX0OB+piBxx1M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125 | "text/plain": [
126 | ""
127 | ]
128 | },
129 | "metadata": {
130 | "tags": []
131 | }
132 | }
133 | ]
134 | },
135 | {
136 | "cell_type": "markdown",
137 | "metadata": {
138 | "id": "xk4RRtBxP5ZK",
139 | "colab_type": "text"
140 | },
141 | "source": [
142 | "We now have data where we 1000 data points having a label of 1 and 1000 data points having a label of 0. So, in total we have 2000 data points labeled in two unique classes (0 and 1). The shape of our data is **(2000, 2)**. \n",
143 | "\n",
144 | "In the [deck](http://bit.ly/gdg-goa-20), we discussed that we need to apply some transformations to uncrumple the above data. The transformations were of the following form:\n",
145 | "\n",
146 | "\n",
147 | "\n",
148 | "Let's generate $W$ and $b$."
149 | ]
150 | },
151 | {
152 | "cell_type": "code",
153 | "metadata": {
154 | "id": "voKkevpCP17C",
155 | "colab_type": "code",
156 | "colab": {}
157 | },
158 | "source": [
159 | "# This is our weight matrix\n",
160 | "w = tf.Variable(tf.random.uniform(shape=(2, 1)))\n",
161 | "# This is our bias vector\n",
162 | "b = tf.Variable(tf.zeros(shape=(1,)))"
163 | ],
164 | "execution_count": 0,
165 | "outputs": []
166 | },
167 | {
168 | "cell_type": "code",
169 | "metadata": {
170 | "id": "p20DvamgRfkG",
171 | "colab_type": "code",
172 | "outputId": "6fe7b542-5f55-4670-b9b8-94169474b2d3",
173 | "colab": {
174 | "base_uri": "https://localhost:8080/",
175 | "height": 51
176 | }
177 | },
178 | "source": [
179 | "# Preview of the weights\n",
180 | "print(w.numpy())"
181 | ],
182 | "execution_count": 6,
183 | "outputs": [
184 | {
185 | "output_type": "stream",
186 | "text": [
187 | "[[0.22532213]\n",
188 | " [0.7545028 ]]\n"
189 | ],
190 | "name": "stdout"
191 | }
192 | ]
193 | },
194 | {
195 | "cell_type": "markdown",
196 | "metadata": {
197 | "id": "j6Lpq_aJRUrU",
198 | "colab_type": "text"
199 | },
200 | "source": [
201 | "Why did we generated the weights to be of shape **(2, 1)** (2 rows and 1 column)? \n",
202 | "\n",
203 | "**To make matrix multiplication work!**\n",
204 | "\n",
205 | "Remember that, our input data is of shape **(2000, 2)**. For matrix multiplication to work the [shape compatibility rule](https://www.varsitytutors.com/hotmath/hotmath_help/topics/compatible-matrices) must follow. In this case, if we multiply our input data with the weights we would get an array of shape **(2000, 1)**. "
206 | ]
207 | },
208 | {
209 | "cell_type": "code",
210 | "metadata": {
211 | "id": "zB6v6_rTRQEp",
212 | "colab_type": "code",
213 | "outputId": "0d9e81fc-3d1a-4ce8-a44f-a60433d51e07",
214 | "colab": {
215 | "base_uri": "https://localhost:8080/",
216 | "height": 153
217 | }
218 | },
219 | "source": [
220 | "# We first need to convert NumPy array to TensorFlow tensors\n",
221 | "features = tf.convert_to_tensor(features, dtype=tf.float32) \n",
222 | "# Affine trasnform\n",
223 | "tf.matmul(features, w) + b"
224 | ],
225 | "execution_count": 7,
226 | "outputs": [
227 | {
228 | "output_type": "execute_result",
229 | "data": {
230 | "text/plain": [
231 | ""
239 | ]
240 | },
241 | "metadata": {
242 | "tags": []
243 | },
244 | "execution_count": 7
245 | }
246 | ]
247 | },
248 | {
249 | "cell_type": "markdown",
250 | "metadata": {
251 | "id": "HG88S1OFTWpO",
252 | "colab_type": "text"
253 | },
254 | "source": [
255 | "The trasnformation we just applied is also called **Affine Transformation**. Let's see how well did we do. "
256 | ]
257 | },
258 | {
259 | "cell_type": "code",
260 | "metadata": {
261 | "id": "njp2mKpRTVT-",
262 | "colab_type": "code",
263 | "outputId": "c1b7de79-c6b2-48b5-a23a-c94355024f10",
264 | "colab": {
265 | "base_uri": "https://localhost:8080/",
266 | "height": 282
267 | }
268 | },
269 | "source": [
270 | "predictions = tf.matmul(features, w) + b\n",
271 | "plt.scatter(features[:, 0], features[:, 1], c=predictions[:, 0] > 0.5)"
272 | ],
273 | "execution_count": 9,
274 | "outputs": [
275 | {
276 | "output_type": "execute_result",
277 | "data": {
278 | "text/plain": [
279 | ""
280 | ]
281 | },
282 | "metadata": {
283 | "tags": []
284 | },
285 | "execution_count": 9
286 | },
287 | {
288 | "output_type": "display_data",
289 | "data": {
290 | "image/png": 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OhB/FmcMKO3/aCNG3wb9nnxLx9jBC3hN0pAZG7F3n8c5gaA+7VUOI2DvkjurI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\nlBCnErmiKEqI+z+AUPHWaGP5zgAAAABJRU5ErkJggg==\n",
291 | "text/plain": [
292 | ""
293 | ]
294 | },
295 | "metadata": {
296 | "tags": []
297 | }
298 | }
299 | ]
300 | },
301 | {
302 | "cell_type": "code",
303 | "metadata": {
304 | "id": "edlpJfpXsH4f",
305 | "colab_type": "code",
306 | "colab": {
307 | "base_uri": "https://localhost:8080/",
308 | "height": 136
309 | },
310 | "outputId": "cb831f61-33d8-463b-bf65-72f2a9dfe593"
311 | },
312 | "source": [
313 | "predictions.numpy()"
314 | ],
315 | "execution_count": 10,
316 | "outputs": [
317 | {
318 | "output_type": "execute_result",
319 | "data": {
320 | "text/plain": [
321 | "array([[1.4310477],\n",
322 | " [1.2830482],\n",
323 | " [1.8315872],\n",
324 | " ...,\n",
325 | " [0.4527592],\n",
326 | " [0.0724498],\n",
327 | " [1.741094 ]], dtype=float32)"
328 | ]
329 | },
330 | "metadata": {
331 | "tags": []
332 | },
333 | "execution_count": 10
334 | }
335 | ]
336 | },
337 | {
338 | "cell_type": "markdown",
339 | "metadata": {
340 | "id": "qw1BY-8YT3TY",
341 | "colab_type": "text"
342 | },
343 | "source": [
344 | "**Terrible!** Isn't it? So, where can we go from here? \n",
345 | "\n",
346 | "> A very naive approach would be to adjust the parameters manually and see how well are we doing. \n",
347 | "\n",
348 | "Can't we be better than that? If yes, how? \n",
349 | "\n",
350 | "Think how you learn. Let's say you are learning about prepositions in English Grammar. To better aid your learning, you make **use of the errors** that you make in the beginning and try to focus on the areas that need improvements. \n",
351 | "\n",
352 | "So, if we were to do something similar here, we first need to be able to make use of the errors we are making. \n",
353 | "\n",
354 | "> How do we calculate that error? \n",
355 | "\n",
356 | "From the above cell, we got our `predictions`. Our labels are stored in the `labels` array. So, we can compute the difference between `labels` and `predictions` to see how far off we are from the desired values. We do this in the following way (the reasons are not important at this point):\n",
357 | "- Take the absolute difference between `labels` and `predictions`: `abs_diff = labels - predictions`.\n",
358 | "- Square the difference: `sqr_diff = tf.square(abs_diff)`.\n",
359 | "- Compute the mean: `tf.reduce_mean(sqr_diff)`. \n",
360 | "\n",
361 | "If we tie the above together, we get the following - "
362 | ]
363 | },
364 | {
365 | "cell_type": "code",
366 | "metadata": {
367 | "id": "cxvmjOcOVpff",
368 | "colab_type": "code",
369 | "outputId": "f707b5d6-90ea-46b3-f98e-e61325df4b37",
370 | "colab": {
371 | "base_uri": "https://localhost:8080/",
372 | "height": 34
373 | }
374 | },
375 | "source": [
376 | "# Calculate the error (Mean Squared Error)\n",
377 | "tf.reduce_mean(tf.square(labels - predictions)).numpy()"
378 | ],
379 | "execution_count": 0,
380 | "outputs": [
381 | {
382 | "output_type": "execute_result",
383 | "data": {
384 | "text/plain": [
385 | "0.6362906"
386 | ]
387 | },
388 | "metadata": {
389 | "tags": []
390 | },
391 | "execution_count": 9
392 | }
393 | ]
394 | },
395 | {
396 | "cell_type": "markdown",
397 | "metadata": {
398 | "id": "L7zRMF2WXzVU",
399 | "colab_type": "text"
400 | },
401 | "source": [
402 | "Our objective is to ***minimize the above quantity***. \n",
403 | "\n",
404 | "> After calculating the error in the above way, how do we use this error to inform our system how good/bad it did? \n",
405 | "\n",
406 | "We cannot change the values of the input data, we have only the parameters to change to minimize the error. Now, we expressed the error in terms of a function - **Mean Squared Error**. Error is referred to as Loss, sometimes interchangeably, sometimes they are different. From now on, we will use Loss. \n",
407 | "\n",
408 | "*Our predictions are dependent on the parameters* (recall the euqations stated above). To find the **minimum value of this function that is dependent on the labels and predictions**, we need to **find the derivative of this function with respect to the parameters** to know the direction in which we would need to proceed.\n",
409 | "\n",
410 | "\n",
411 | "The border line is ***we would need to nudge the parameters to minimize the function***. \n",
412 | "\n",
413 | "Let's write different modules to handle the above things. "
414 | ]
415 | },
416 | {
417 | "cell_type": "code",
418 | "metadata": {
419 | "id": "1ZjwVL7cYM20",
420 | "colab_type": "code",
421 | "colab": {}
422 | },
423 | "source": [
424 | "LEARNING_RATE = 0.01\n",
425 | "\n",
426 | "# Affine transform\n",
427 | "def compute_predictions(features):\n",
428 | " return tf.matmul(features, w) + b\n",
429 | "\n",
430 | "# Loss\n",
431 | "def compute_loss(labels, predictions):\n",
432 | " return tf.reduce_mean(tf.square(labels - predictions))\n",
433 | "\n",
434 | "def minimize_loss(x, y):\n",
435 | " with tf.GradientTape() as tape:\n",
436 | " # Transform data and compute the loss\n",
437 | " predictions = compute_predictions(x)\n",
438 | " loss = compute_loss(y, predictions)\n",
439 | " \n",
440 | " # Compute the derivative/gradients\n",
441 | " dloss_dw, dloss_db = tape.gradient(loss, [w, b])\n",
442 | "\n",
443 | " # Update the parameters and return loss\n",
444 | " w.assign_sub(LEARNING_RATE * dloss_dw)\n",
445 | " b.assign_sub(LEARNING_RATE * dloss_db)\n",
446 | " return loss"
447 | ],
448 | "execution_count": 0,
449 | "outputs": []
450 | },
451 | {
452 | "cell_type": "markdown",
453 | "metadata": {
454 | "id": "sFI_MxpDbeuO",
455 | "colab_type": "text"
456 | },
457 | "source": [
458 | "`LEARNING_RATE` helps to accelerate the minimization process. \n",
459 | "\n",
460 | "Let's now apply it to our data. "
461 | ]
462 | },
463 | {
464 | "cell_type": "code",
465 | "metadata": {
466 | "id": "sIRUh5bIcWe3",
467 | "colab_type": "code",
468 | "colab": {}
469 | },
470 | "source": [
471 | "# Prepare a dataset.\n",
472 | "num_samples = 10000\n",
473 | "negative_samples = np.random.multivariate_normal(\n",
474 | " mean=[0, 3], cov=[[1, 0.5],[0.5, 1]], size=num_samples)\n",
475 | "positive_samples = np.random.multivariate_normal(\n",
476 | " mean=[3, 0], cov=[[1, 0.5],[0.5, 1]], size=num_samples)\n",
477 | "features = np.vstack((negative_samples, positive_samples)).astype(np.float32)\n",
478 | "labels = np.vstack((np.zeros((num_samples, 1), dtype='float32'),\n",
479 | " np.ones((num_samples, 1), dtype='float32')))"
480 | ],
481 | "execution_count": 0,
482 | "outputs": []
483 | },
484 | {
485 | "cell_type": "markdown",
486 | "metadata": {
487 | "id": "zDQNCLEYBHHs",
488 | "colab_type": "text"
489 | },
490 | "source": [
491 | "We shuffle the data points to make sure our system does not learn any ordering bias that might be there in the data points."
492 | ]
493 | },
494 | {
495 | "cell_type": "code",
496 | "metadata": {
497 | "id": "ZG1R9WGxbcue",
498 | "colab_type": "code",
499 | "colab": {}
500 | },
501 | "source": [
502 | "# Shuffle the data.\n",
503 | "indices = np.random.permutation(len(features))\n",
504 | "features = features[indices]\n",
505 | "labels = labels[indices]"
506 | ],
507 | "execution_count": 0,
508 | "outputs": []
509 | },
510 | {
511 | "cell_type": "markdown",
512 | "metadata": {
513 | "id": "ppcO7vrQdsMA",
514 | "colab_type": "text"
515 | },
516 | "source": [
517 | "Instead of computing the loss on each data points and then update the parameters accordingly, we will do it in a ***batch-wise manner***. We can easily do so by - "
518 | ]
519 | },
520 | {
521 | "cell_type": "code",
522 | "metadata": {
523 | "id": "LDIDiZzIeGV2",
524 | "colab_type": "code",
525 | "colab": {}
526 | },
527 | "source": [
528 | "# Create a tf.data.Dataset object for easy batched iteration\n",
529 | "dataset = tf.data.Dataset.from_tensor_slices((features, labels))\n",
530 | "dataset = dataset.shuffle(buffer_size=1024).batch(256)"
531 | ],
532 | "execution_count": 0,
533 | "outputs": []
534 | },
535 | {
536 | "cell_type": "code",
537 | "metadata": {
538 | "id": "wxZP-vGbcOmD",
539 | "colab_type": "code",
540 | "outputId": "cd0ba5c4-7138-40b3-f0a3-2875e28419e3",
541 | "colab": {
542 | "base_uri": "https://localhost:8080/",
543 | "height": 187
544 | }
545 | },
546 | "source": [
547 | "# Minimize the loss function\n",
548 | "for epoch in range(10):\n",
549 | " for step, (x, y) in enumerate(dataset):\n",
550 | " loss = minimize_loss(x, y)\n",
551 | " print('Epoch %d: last batch loss = %.4f' % (epoch, float(loss)))"
552 | ],
553 | "execution_count": 16,
554 | "outputs": [
555 | {
556 | "output_type": "stream",
557 | "text": [
558 | "Epoch 0: last batch loss = 0.0784\n",
559 | "Epoch 1: last batch loss = 0.0798\n",
560 | "Epoch 2: last batch loss = 0.0242\n",
561 | "Epoch 3: last batch loss = 0.0278\n",
562 | "Epoch 4: last batch loss = 0.0369\n",
563 | "Epoch 5: last batch loss = 0.0372\n",
564 | "Epoch 6: last batch loss = 0.0173\n",
565 | "Epoch 7: last batch loss = 0.0255\n",
566 | "Epoch 8: last batch loss = 0.0324\n",
567 | "Epoch 9: last batch loss = 0.0230\n"
568 | ],
569 | "name": "stdout"
570 | }
571 | ]
572 | },
573 | {
574 | "cell_type": "markdown",
575 | "metadata": {
576 | "id": "pVEv9IiRew7B",
577 | "colab_type": "text"
578 | },
579 | "source": [
580 | "Notice that the loss value is also decreasing - we are minimizing the loss function!\n",
581 | "\n",
582 | "How did we do? "
583 | ]
584 | },
585 | {
586 | "cell_type": "code",
587 | "metadata": {
588 | "id": "goFfrWVYeZYT",
589 | "colab_type": "code",
590 | "outputId": "5163938f-e3e1-4b68-d1b3-d1cafb0afa0c",
591 | "colab": {
592 | "base_uri": "https://localhost:8080/",
593 | "height": 282
594 | }
595 | },
596 | "source": [
597 | "predictions = compute_predictions(features)\n",
598 | "plt.scatter(features[:, 0], features[:, 1], c=predictions[:, 0] > 0.5)"
599 | ],
600 | "execution_count": 17,
601 | "outputs": [
602 | {
603 | "output_type": "execute_result",
604 | "data": {
605 | "text/plain": [
606 | ""
607 | ]
608 | },
609 | "metadata": {
610 | "tags": []
611 | },
612 | "execution_count": 17
613 | },
614 | {
615 | "output_type": "display_data",
616 | "data": {
617 | "image/png": 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gEED9+vXPd9gyR1S1SPZs2M+kJz9FCMFDb/Rn7YINHN5xjIat6nH/y/0Ijwpj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\nw20mEgj7idSH8Qd9HHTmvsw4pLwyTa888QZ//e3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3Aoy8H2291lSA8oyBwCwk+DXEO67s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\nyxDn/wHj8yCzIzq4ygAAAABJRU5ErkJggg==\n",
618 | "text/plain": [
619 | ""
620 | ]
621 | },
622 | "metadata": {
623 | "tags": []
624 | }
625 | }
626 | ]
627 | },
628 | {
629 | "cell_type": "markdown",
630 | "metadata": {
631 | "id": "5mnru58Ke7_a",
632 | "colab_type": "text"
633 | },
634 | "source": [
635 | "Look at that! 😍 \n",
636 | "\n",
637 | "But, we can do the above with following lines of code - "
638 | ]
639 | },
640 | {
641 | "cell_type": "code",
642 | "metadata": {
643 | "id": "NvLQB_4EgCAy",
644 | "colab_type": "code",
645 | "colab": {}
646 | },
647 | "source": [
648 | "# Stuff to minimize the loss function\n",
649 | "model = tf.keras.Sequential()\n",
650 | "model.add(tf.keras.layers.Dense(units=1, input_shape=(2,)))\n",
651 | "model.compile(optimizer='sgd', loss='mean_squared_error')"
652 | ],
653 | "execution_count": 0,
654 | "outputs": []
655 | },
656 | {
657 | "cell_type": "code",
658 | "metadata": {
659 | "id": "Sl4jVutBgZRO",
660 | "colab_type": "code",
661 | "outputId": "8c2d2a43-c76a-4abf-86a2-9288574546a8",
662 | "colab": {
663 | "base_uri": "https://localhost:8080/",
664 | "height": 374
665 | }
666 | },
667 | "source": [
668 | "# Minimize the loss function\n",
669 | "model.fit(dataset, epochs=10)"
670 | ],
671 | "execution_count": 19,
672 | "outputs": [
673 | {
674 | "output_type": "stream",
675 | "text": [
676 | "Epoch 1/10\n",
677 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0968\n",
678 | "Epoch 2/10\n",
679 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0412\n",
680 | "Epoch 3/10\n",
681 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0332\n",
682 | "Epoch 4/10\n",
683 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0290\n",
684 | "Epoch 5/10\n",
685 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0271\n",
686 | "Epoch 6/10\n",
687 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0262\n",
688 | "Epoch 7/10\n",
689 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0258\n",
690 | "Epoch 8/10\n",
691 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0254\n",
692 | "Epoch 9/10\n",
693 | "79/79 [==============================] - 0s 1ms/step - loss: 0.0253\n",
694 | "Epoch 10/10\n",
695 | "79/79 [==============================] - 0s 2ms/step - loss: 0.0251\n"
696 | ],
697 | "name": "stdout"
698 | },
699 | {
700 | "output_type": "execute_result",
701 | "data": {
702 | "text/plain": [
703 | ""
704 | ]
705 | },
706 | "metadata": {
707 | "tags": []
708 | },
709 | "execution_count": 19
710 | }
711 | ]
712 | },
713 | {
714 | "cell_type": "markdown",
715 | "metadata": {
716 | "id": "aq_332sygsjv",
717 | "colab_type": "text"
718 | },
719 | "source": [
720 | "How did we do here? "
721 | ]
722 | },
723 | {
724 | "cell_type": "code",
725 | "metadata": {
726 | "id": "An_vR0-Ygmj9",
727 | "colab_type": "code",
728 | "outputId": "6818d21e-06ba-4817-ea8f-a629a7cc3d27",
729 | "colab": {
730 | "base_uri": "https://localhost:8080/",
731 | "height": 282
732 | }
733 | },
734 | "source": [
735 | "predictions = model.predict(features)\n",
736 | "plt.scatter(features[:, 0], features[:, 1], c=predictions[:, 0] > 0.5)"
737 | ],
738 | "execution_count": 20,
739 | "outputs": [
740 | {
741 | "output_type": "execute_result",
742 | "data": {
743 | "text/plain": [
744 | ""
745 | ]
746 | },
747 | "metadata": {
748 | "tags": []
749 | },
750 | "execution_count": 20
751 | },
752 | {
753 | "output_type": "display_data",
754 | "data": {
755 | "image/png": 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gIECjRo1Od9gKR2zNaLav3sW4xz5GCMF9r/VnxZzV7Nt8kCatG3L3i/2IjI1g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\n88EbH/HYLc90KHEwSvqdF/7LgoeN58XC+e/nBb8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756 | "text/plain": [
757 | ""
758 | ]
759 | },
760 | "metadata": {
761 | "tags": []
762 | }
763 | }
764 | ]
765 | },
766 | {
767 | "cell_type": "code",
768 | "metadata": {
769 | "id": "cTyKN3mRhTU3",
770 | "colab_type": "code",
771 | "outputId": "c675911e-dd5c-49b7-de71-026bc5bb1531",
772 | "colab": {
773 | "base_uri": "https://localhost:8080/",
774 | "height": 201
775 | }
776 | },
777 | "source": [
778 | "tf.keras.utils.plot_model(model, \"model.png\", show_shapes=True)"
779 | ],
780 | "execution_count": 0,
781 | "outputs": [
782 | {
783 | "output_type": "execute_result",
784 | "data": {
785 | "image/png": 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786 | "text/plain": [
787 | ""
788 | ]
789 | },
790 | "metadata": {
791 | "tags": []
792 | },
793 | "execution_count": 29
794 | }
795 | ]
796 | }
797 | ]
798 | }
--------------------------------------------------------------------------------