Old models wont work as Fastai introduced a breaking change pin lib in diffrent env

fastai == 2.0.3
fastcore == 1.0.4
pytorch == 1.4.0
  1. Downloading data from Google Images: use the script go get all URLS run it in browser console
  urls=Array.from(document.querySelectorAll('.rg_i')).map(el=> el.hasAttribute('data-src')?el.getAttribute('data-src'):el.getAttribute('data-iurl'));
  window.open('data:text/csv;charset=utf-8,' + escape(urls.join('\n')));
  1. Creating validation set automatically from data
    new API is DataBlock = template for creating a `DataLoaders````python
    bears = DataBlock(
    blocks=(ImageBlock, CategoryBlock),
    splitter=RandomSplitter(valid_pct=0.2, seed=42),
    dls = bears.dataloaders(path)
data = ImageDataBunch.from_folder(path, train=".", valid_pct=0.2,ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats)


np.random.seed(42) is always set to same number here so that validation set remains same and improve our model independant of data changes.

We also use RandomResizedCrop to use a n random crop in every epoch

  1. Tips for choosing learning rate: Look for the range where the accuracy is imroving steadily (not very steep).
    ex here the error rate decrese is steady between &10^-5& and &10^-4& so we use
    learn.fit_one_cycle(2, max_lr=slice(3e-5,3e-4))


  1. All data sets have x and y
    x = file name
    y = labels
  2. You can create application with UI inside Jupyter notebooks.
  3. open_image(path) fn to open any image
  4. learn.export() exports the trained model to export.pkl its ~99MB
  5. Starlette is web app toolkit similar to flas which has async/await style
  6. Trainining loss > validation loss means data is not properly fitted
  7. Too many epochs leads to overfitting. It happens when error rate seems to be going down tthen in subsequents epochs start going up.

Lesson 2: SGD

Stochastic Gradient Descent (SGD)

Tensor in deeplearning it is nothing but a array ( 1 or more dimensions) which is of regular shape (all rows are of equal size)
Rank: number of dimensions

x = matrix/ vector = tensor
a = matrix/ vector = tensor
y = x@a = matrix product

Mean squared Error MSE is the common error fn for regression
def mse(y_hat, y): return ((y_hat-y)**2).mean()
y_hat or $\hat{y}$ is predicted value and y is the actual

tensor(-1.,1) is eqivalent to tensor(-1.0,1.0) we do this because we want everything to be float

In this we are trying to fit a line of the form
y = mx +c which we convert to
$y_i$ = $a_1x_{1i} + a_2x_{2i}$ where $x_2$ = 1
We first try to gues $a_1,a_2$ and calculate MSE. we then try minimise MSE
We can do that either try to keep guessing by changing $a_1,a_2$ up and down and calculate MSE and see where it reduces or we can use Maths in which a derivative calculates the same thing
Gradient/Derivative~ tells us whether moving it up or down $a_2$ or tilting it up or down $a_1$ make it better i.e change the MSE.

Gradient descent is an algorithm that minimizes functions. Given a function defined by a set of parameters, gradient descent starts with an initial set of parameter values and iteratively moves toward a set of parameter values that minimize the function. This iterative minimization is achieved by taking steps in the negative direction of the function gradient.


In this ex we calculated loss for all the points in practice it willnot possible as tehre will be too mnay points when the rank/complexity increases.
So we use Mini batches for every epoch/iteration we calculate loss from a random subset of points

Model or Architecture is nothing but the mathematical fn here it is y = mx + c or $y = a_1x_1 + a_2x_2$

Parameter are the coefficients or weights are the number which we are trying to find here they are $a_1 and\ a_2$

How (and why) to create a good validation set? link