jbochi

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.

normalize_image.ipynb

mrdrozdov

from tf_logger import TFLogger


""" Example of using TFLogger to save train & dev statistics. To visualize
    in tensorboard simply do:
    
        tensorboard --logdir /path/to/summaries
        
    This code does depend on Tensorflow, but does not require that your model
    is built using Tensorflow. For instance, could build a model in Chainer, then

nlm

from functools import wraps
from types import FunctionType, GeneratorType
import logging
import time

def coroutine(f):
    @wraps(f)
    def wrapper(*args, **kwargs):
        logging.debug('coroutine starting: {}'.format(f.__name__))
        return f(*args, **kwargs)

stared

Sequential model in Keras -> ASCII

by Piotr Migdał from deepsense.io

EDIT: Now at: https://github.com/stared/keras-sequential-ascii, pip-installable.

Usage

from keras_ascii_sequential import sequential_model_to_ascii_printout

npyoung

robertsdionne

#!/usr/local/bin/python3

import glob
import h5py
import matplotlib.animation as animation
import matplotlib.pyplot as plot
import numpy as np
import tensorflow as tf

fsodogandji

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

taku-y