gurucharanmk

import random
import os

import torch
import numpy as np

def set_seed(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    

gurucharanmk

learn = cnn_learner(dls, resnet34, pretrained=True, metrics=accuracy).to_fp16()

learn.fit_one_cycle(4)
learn.save('stage_1')

#Understand which classes are misclassified
interp = ClassificationInterpretation.from_learner(learn)
losses,idxs = interp.top_losses()
interp.plot_top_losses(9, figsize=(15,10))
interp.most_confused(min_val=3)

gurucharanmk

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)

gurucharanmk

#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()

gurucharanmk

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,

gurucharanmk

from fastai2.data.all import *
from fastai2.vision.all import *
from fastai2.callback.all import *

gurucharanmk

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

gurucharanmk

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

gurucharanmk

let colors = [ "red", "green", "blue" ];

let iter = colors.entries();
let entry;
while (!(entry = iter.next()).done) {
    console.log(entry.value);
}

gurucharanmk

let iterable = {
    0: 'a',
    1: 'b',
    2: 'c',
    length: 3,
    [Symbol.iterator]() {
	  var keys = Object.keys(this).sort();
	  var index = 0;
  
	  return {