lucidrains

import torch
import torch.nn as nn
import torch.nn.functional as F

# helpers

def make_unit_length(x, epsilon=1e-6):
    norm = x.norm(p=2, dim=-1, keepdim=True)
    return x.div(norm + epsilon)

saliksyed

""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

compor

# produces a patch between the files in the 2 dirs

# -c : special context for output
# -r : directory recursively
# -B : ignore blanks

diff -crB before_dir after_dir > foo.patch

# if you want to patch before_dir now