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var funcArray_1 = []; | |
var funcArray_2 = []; | |
//IIFE of type 1 | |
for (var i = 0; i < 10; i++) { | |
funcArray_1.push(function(val){ | |
return function() { console.log(val); } | |
}(i)); | |
} | |
console.log(i); // 10 |
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var funcArray = []; | |
for (let i = 0; i < 10; i++) { | |
funcArray.push(function() { console.log(i); }); | |
} | |
//console.log(i); // Not accessible as i is block scope variable !!! | |
funcArray.forEach(function(func) { | |
func(); | |
}); |
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var Arr = [3, 6, 9, 12, 15, 18, 21]; | |
Arr.author="Gurucharan"; | |
//Using legacy for loop | |
console.log('==============[Legacy for loop]=============='); | |
for (var i = 0; i < Arr.length; ++i) { | |
console.log("index is of type", typeof i, Arr[i]); | |
} | |
//Using forEach |
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console.log("======================[Array]======================"); | |
for (let x of ['a', 'b', 'c']) { | |
console.log(x); | |
} | |
//String | |
console.log("======================[String]======================"); | |
for (let x of 'Hello World!') { | |
console.log(x); | |
} |
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let iterable = { | |
0: 'a', | |
1: 'b', | |
2: 'c', | |
length: 3, | |
[Symbol.iterator]() { | |
var keys = Object.keys(this).sort(); | |
var index = 0; | |
return { |
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let colors = [ "red", "green", "blue" ]; | |
let iter = colors.entries(); | |
let entry; | |
while (!(entry = iter.next()).done) { | |
console.log(entry.value); | |
} |
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from fastai2.data.all import * | |
from fastai2.vision.all import * | |
from fastai2.callback.all import * |
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get_x = lambda x:CELEBA_PATH/f'{x[0]}' | |
get_y = lambda x:[headers[index+1] for index,val in enumerate(x[1:]) if val == 1] | |
batch_tfms = [*aug_transforms(size=224, max_warp=0), Normalize.from_stats(*imagenet_stats)] | |
item_tfms = RandomResizedCrop(460, min_scale=0.75, ratio=(1.,1.)) | |
bs=64 | |
celleba_data = DataBlock(blocks=(ImageBlock, MultiCategoryBlock), | |
get_x=get_x, | |
splitter=RandomSplitter(), | |
get_y=get_y, |
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#Mixed precision model to train | |
learn = cnn_learner(dls, resnet18, pretrained=True, metrics=[accuracy_multi]).to_fp16() | |
#Train only the classifier using one cycle policy, with default optimizer(ADAM) and learning rate. | |
learn.fit_one_cycle(4) | |
#Save the trained model | |
learn.save('stage_1') | |
#Unfreeze the model, now entire model is available for training | |
learn.unfreeze() |
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batch_tfms = [*aug_transforms(size=224, max_warp=0), Normalize.from_stats(*imagenet_stats)] | |
item_tfms = RandomResizedCrop(460, min_scale=0.75, ratio=(1.,1.)) | |
bs=128 | |
dls = ImageDataLoaders.from_folder(data_path, train='train', valid='val', batch_tfms=batch_tfms, | |
item_tfms=item_tfms, bs=bs) |