Thursday, January 23, 2020

Transfer learning




TRANSFER LEARNING Using a pre trained network on images not in training set is known as transfer learning

MNIST GAN


Here we have 2 neural networks GENERATOR and  DISCRIMINATOR

DISCRIMINATOR:

  • Is a simple classifier that tries to classify the images as real from the training set or fake generated images.

Glob module in python


Usually, the programmers require to traverse through a list of files at some location, mostly having a specific pattern. Python’s glob module has several functions that can help in listing files under a specified folder. We may filter them based on extensions, or with a particular string as a portion of the filename.
All the methods of Glob module follow the Unix-style pattern matching mechanism and rules. However, it doesn’t allow expanding the tilde (~) and environment variables.

Wednesday, January 22, 2020

Flattening in NLP




  • Instead of representing this 4*4 matrix, we can construct a vector with 16 entries.
  • First 4 entries correspond to the first row.similarly 2nd 3rd and 4th

Note:After converting our image to an vector they can be fed to an input layer on NLP.




MoviePy




MoviePy is a Python module for video editing, which can be used for basic operations (like cuts, concatenations, title insertions), video compositing (a.k.a. non-linear editing), video processing, or to create advanced effects. It can read and write the most common video formats, including GIF.
Here it is in action (run in an IPython Notebook):

Gaussian Blur operation




In Gaussian Blur operation, the image is convolved with a Gaussian filter instead of the box filter. The Gaussian filter is a low-pass filter that removes the high-frequency components are reduced.
You can perform this operation on an image using the Gaussianblur()method of the imgproc class. Following is the syntax of this method −
GaussianBlur(src, dst, ksize, sigmaX)
This method accepts the following parameters −
  • src − A Mat object representing the source (input image) for this operation.
  • dst − A Mat object representing the destination (output image) for this operation.
  • ksize − A Size object representing the size of the kernel.
  • sigmaX − A variable of the type double representing the Gaussian kernel standard deviation in X direction.

Geometric Image Transformation




Geometric Image Transformations

The functions in this section perform various geometrical transformations of 2D images. They do not change the image content but deform the pixel grid and map this deformed grid to the destination image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from destination to the source. That is, for each pixel (x, y) of the destination image, the functions compute coordinates of the corresponding “donor” pixel in the source image and copy the pixel value:
\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))
In case when you specify the forward mapping \left<g_x, g_y\right>: \texttt{src} \rightarrow \texttt{dst} , the OpenCV functions first compute the corresponding inverse mapping \left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src} and then use the above formula.