vmarkovtsev

simonw

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb

kylemcdonald

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

rspeer

minhlab

import numpy as np
from scipy.stats import spearmanr, pearsonr
from matplotlib import pyplot as pl
import sys

if __name__ == '__main__':
    repeats = int(sys.argv[1]) if len(sys.argv) >= 2 else 5 # change this and see what happens
    dim = 50
    #sizes = {'simlex': 999, 'men': 3000, 'mc': 30, 'rg': 65, 'ws353': 353}
    sizes = {'men': (3000, '#0066ff', '#4d94ff'), 'mc': (30, '#ff3300', '#ffd6cc80'), 'rg': (65, '#00ff00', '#ccffcc80')}

nasimrahaman

import torch
import torch.nn as nn


def log_sum_exp(x):
    # See implementation detail in
    # http://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick/
    # b is a shift factor. see link.
    # x.size() = [N, C]:
    b, _ = torch.max(x, 1)

eamartin

fchollet

import keras
import numpy as np

timesteps = 60
input_dim = 64
samples = 10000
batch_size = 128
output_dim = 64

# Test data.

ledell

# https://ropensci.org/tutorials/arxiv_tutorial.html
install.packages("aRxiv")

library(aRxiv)
library(stringr)
library(ggplot2)

# Query arxiv: 6892 results including "Deep"; 49 DeepThings (Sept 21, 2017)
df <- arxiv_search('ti:"Deep"', batchsize = 1000, limit = 100000)
titles <- grep(pattern = "Deep[[:upper:]][[:lower:]]+",