| 1 | |
| 00:00:00,760 --> 00:00:01,300 | |
| Right. | |
| 2 | |
| 00:00:01,320 --> 00:00:08,130 | |
| So we've just imported the data into carious However that is not in the ideal or the correct format | |
| 3 | |
| 00:00:08,130 --> 00:00:10,410 | |
| that cares needs to train on. | |
| 4 | |
| 00:00:10,440 --> 00:00:15,370 | |
| So this chapter is going to be a tiny chapter in how to get us data into the correct shape. | |
| 5 | |
| 00:00:15,450 --> 00:00:17,020 | |
| So let's look at the next slide. | |
| 6 | |
| 00:00:17,330 --> 00:00:21,780 | |
| So as I said before we've brought this in here and help with it it's here. | |
| 7 | |
| 00:00:21,780 --> 00:00:22,650 | |
| All right. | |
| 8 | |
| 00:00:22,650 --> 00:00:29,340 | |
| How ever terrorists requires it to be in a special shape and that she basically as number of rows columns | |
| 9 | |
| 00:00:29,910 --> 00:00:33,040 | |
| a number of samples rows of columns and Dept. | |
| 10 | |
| 00:00:33,120 --> 00:00:40,880 | |
| So this is how extreme it looks here but healthcare is actually once it is 16000 to 28 and 1. | |
| 11 | |
| 00:00:40,950 --> 00:00:46,090 | |
| So we sort of have to add a fourth dimension antoh data and that's how we do it here. | |
| 12 | |
| 00:00:46,090 --> 00:00:47,580 | |
| So now let's do this in Iraq. | |
| 13 | |
| 00:00:47,910 --> 00:00:52,980 | |
| And the reason Chris is looking for a fourth dimension here is because when you load a greyscale image | |
| 14 | |
| 00:00:52,980 --> 00:00:57,930 | |
| dataset it doesn't add the dimension of the depth to up. | |
| 15 | |
| 00:00:58,230 --> 00:01:00,550 | |
| Mainly because it's a greyscale and is Nakul adept. | |
| 16 | |
| 00:01:00,630 --> 00:01:04,850 | |
| If it was a color image dataset you would have the tree popping up at the end here. | |
| 17 | |
| 00:01:05,160 --> 00:01:10,390 | |
| However we each need to add this one just to indicate to Cara's that it's a greyscale image data set. | |
| 18 | |
| 00:01:10,590 --> 00:01:17,070 | |
| Otherwise it's going to return an error is an incorrect format so to change this you would just use | |
| 19 | |
| 00:01:17,070 --> 00:01:20,210 | |
| non-place reship function which is quite simple to use. | |
| 20 | |
| 00:01:20,220 --> 00:01:23,170 | |
| You just use extreme thought reshape. | |
| 21 | |
| 00:01:23,170 --> 00:01:24,760 | |
| Take the first 60000 digits. | |
| 22 | |
| 00:01:24,810 --> 00:01:27,620 | |
| That's when you just when you use shape. | |
| 23 | |
| 00:01:27,780 --> 00:01:30,640 | |
| This is a first I mentioned at 0 addresses. | |
| 24 | |
| 00:01:30,750 --> 00:01:37,310 | |
| Then you enter 28 28 or image or image columns and add 1 and it's quite simple to use. | |
| 25 | |
| 00:01:37,320 --> 00:01:38,880 | |
| So let's get into it. | |
| 26 | |
| 00:01:39,190 --> 00:01:42,350 | |
| I buy that book now. | |
| 27 | |
| 00:01:42,740 --> 00:01:43,120 | |
| OK. | |
| 28 | |
| 00:01:43,160 --> 00:01:49,220 | |
| So like we just saw this is a section of code where we reshape and add the fourth dimension onto these | |
| 29 | |
| 00:01:49,790 --> 00:01:50,610 | |
| treating datasets. | |
| 30 | |
| 00:01:50,620 --> 00:01:52,580 | |
| Untested sets. | |
| 31 | |
| 00:01:52,620 --> 00:01:57,650 | |
| I didn't point this out to you before but we just get image rows and image columns by simply addressing | |
| 32 | |
| 00:01:57,650 --> 00:02:00,450 | |
| the first mention of the shape here extreme. | |
| 33 | |
| 00:02:00,540 --> 00:02:08,030 | |
| Now we use extreme zero and extreme in one we don't need to use Xorn we can use X test as well because | |
| 34 | |
| 00:02:08,030 --> 00:02:09,850 | |
| they're the same dimensionality. | |
| 35 | |
| 00:02:09,890 --> 00:02:15,530 | |
| However this just gives us the rows and columns so let's actually print this out so you can actually | |
| 36 | |
| 00:02:15,530 --> 00:02:16,990 | |
| see what's going on here. | |
| 37 | |
| 00:02:17,480 --> 00:02:21,800 | |
| So this run print that you'll see it turns 28. | |
| 38 | |
| 00:02:21,800 --> 00:02:23,590 | |
| So this is getting to 28 here. | |
| 39 | |
| 00:02:23,960 --> 00:02:32,210 | |
| And if we were to just use this for columns two you'll see will also get 28 right. | |
| 40 | |
| 00:02:32,210 --> 00:02:38,130 | |
| So moving on this is fairly simple if you guys make it nice and neat. | |
| 41 | |
| 00:02:38,150 --> 00:02:41,010 | |
| Again this is how we make the input shape. | |
| 42 | |
| 00:02:41,010 --> 00:02:48,340 | |
| Now you've seen before we needed input shape dimension in the first layer of the convolutional on that. | |
| 43 | |
| 00:02:48,450 --> 00:02:50,870 | |
| And this is how we just create our image shape here. | |
| 44 | |
| 00:02:51,060 --> 00:02:52,540 | |
| It's a tuple That's combined. | |
| 45 | |
| 00:02:52,550 --> 00:02:57,560 | |
| What rows columns and the dimensionality of the depth of the image. | |
| 46 | |
| 00:02:57,710 --> 00:02:58,540 | |
| This would be tree. | |
| 47 | |
| 00:02:58,590 --> 00:03:04,400 | |
| Again if it was a color image dataset and because Kurus expected it to be in a floated to. | |
| 48 | |
| 00:03:04,730 --> 00:03:08,090 | |
| Right now I believe it's going to be in some sort of integer format here. | |
| 49 | |
| 00:03:08,490 --> 00:03:10,360 | |
| So we just need to change this. | |
| 50 | |
| 00:03:10,830 --> 00:03:15,410 | |
| So we just change extra in as type flow to the two and do it for X test as well. | |
| 51 | |
| 00:03:15,570 --> 00:03:18,280 | |
| We don't need to do this for the labels just the turning of data. | |
| 52 | |
| 00:03:18,780 --> 00:03:25,290 | |
| And this is how we normalized data we normalize all data by dividing it by 255 because remember images | |
| 53 | |
| 00:03:25,380 --> 00:03:27,270 | |
| range from 0 to 255. | |
| 54 | |
| 00:03:27,530 --> 00:03:34,720 | |
| So if we divide it all the image data by 255 we basically bring it into range 0 to 1. | |
| 55 | |
| 00:03:34,830 --> 00:03:36,470 | |
| So that's quite simple here now. | |
| 56 | |
| 00:03:36,720 --> 00:03:40,350 | |
| And basically we just print this out again if we need to do that because we did it before. | |
| 57 | |
| 00:03:40,630 --> 00:03:43,680 | |
| But just do a sanity check on our data. | |
| 58 | |
| 00:03:43,770 --> 00:03:45,480 | |
| So let's run this again. | |
| 59 | |
| 00:03:45,480 --> 00:03:49,770 | |
| If you want to see what extreme It actually looks like now this is it. | |
| 60 | |
| 00:03:49,860 --> 00:03:53,930 | |
| You can see basically the decimal points because it's going to hidden here. | |
| 61 | |
| 00:03:54,720 --> 00:03:59,870 | |
| But all the data ranges from 0 to 256 0 to 1 right now. | |
| 62 | |
| 00:04:00,430 --> 00:04:00,670 | |
| OK. | |
| 63 | |
| 00:04:00,690 --> 00:04:03,440 | |
| So let's move on to heart encoding labels. | |