Install working tensorflow or pytorch via standard conda environment workflow.
The recommended conda-based install process works smoothly:
$ # Create a fresh environment
def soft_dice_loss(y_true, y_pred, epsilon=1e-6): | |
''' | |
Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions. | |
Assumes the `channels_last` format. | |
# Arguments | |
y_true: b x X x Y( x Z...) x c One hot encoding of ground truth | |
y_pred: b x X x Y( x Z...) x c Network output, must sum to 1 over c channel (such as after softmax) | |
epsilon: Used for numerical stability to avoid divide by zero errors | |
#!/bin/bash | |
# | |
# script to extract ImageNet dataset | |
# ILSVRC2012_img_train.tar (about 138 GB) | |
# ILSVRC2012_img_val.tar (about 6.3 GB) | |
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory | |
# | |
# https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md | |
# | |
# train/ |
wget https://cmake.org/files/v3.12/cmake-3.12.3.tar.gz
tar zxvf cmake-3.*
These methods in this gist worked for me on my U.S.-based keyboard layouts. I am unsure about other layouts. If you have problems, revert your changes; delete the registry key you created (and reboot).
Update: you should probably scroll down to approach 4 where I suggest using Microsoft PowerToys Keyboard Manager.
Navigate to and create a new binary value in HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Keyboard Layout
named Scancode Map
.
#!/bin/bash | |
# Script for installing tmux on systems where you don't have root access. | |
# tmux will be installed in $HOME/local/bin. | |
# It's assumed that wget and a C/C++ compiler are installed. | |
# exit on error | |
set -e | |
TMUX_VERSION=2.3 |
""" | |
A weighted version of categorical_crossentropy for keras (2.0.6). This lets you apply a weight to unbalanced classes. | |
@url: https://gist.github.com/wassname/ce364fddfc8a025bfab4348cf5de852d | |
@author: wassname | |
""" | |
from keras import backend as K | |
def weighted_categorical_crossentropy(weights): | |
""" | |
A weighted version of keras.objectives.categorical_crossentropy | |
Let's start by getting it out on the table: Sublime Text is great, and version 3 should no longer be considered "abandonware" as of build 3065. Sublime Text served me very, very well over the years. That said, trying out new things is a major part of leveling-up, and in that vein Vim deserves a go. (And have you seen some of those thoughtbot guys flying around in Vim? It's awesome!)
Getting Vim up-and-running on your Windows machine doesn't have to be an all-day project. In this post, we'll walk through: