| function [J grad] = nnCostFunction(nn_params, ... | |
| input_layer_size, ... | |
| hidden_layer_size, ... | |
| num_labels, ... | |
| X, y, lambda) | |
| %NNCOSTFUNCTION Implements the neural network cost function for a two layer | |
| %neural network which performs classification | |
| % [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ... | |
| % X, y, lambda) computes the cost and gradient of the neural network. The | |
| % parameters for the neural network are "unrolled" into the vector |
This totally happened, y'all can stop +1ing this now. GitHub Blog post. Direct link to settings where you can set this.
#Per-organization / per-repo email overrides - A feature suggestion
Here the concepts "organization" and "user" are interchangeable, I'm talking about an entity that owns a repo, whether it is jQuery or John Resig. I'll stick to using organization as it best represents my original use-case.
##TL;DR
| function [J, grad] = costFunctionReg(theta, X, y, lambda) | |
| %COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization | |
| % J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using | |
| % theta as the parameter for regularized logistic regression and the | |
| % gradient of the cost w.r.t. to the parameters. | |
| % Initialize some useful values | |
| m = length(y); % number of training examples | |
| % You need to return the following variables correctly |
| function [all_theta] = oneVsAll(X, y, num_labels, lambda) | |
| %ONEVSALL trains multiple logistic regression classifiers and returns all | |
| %the classifiers in a matrix all_theta, where the i-th row of all_theta | |
| %corresponds to the classifier for label i | |
| % [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels | |
| % logisitc regression classifiers and returns each of these classifiers | |
| % in a matrix all_theta, where the i-th row of all_theta corresponds | |
| % to the classifier for label i | |
| % Some useful variables |
| function p = predictOneVsAll(all_theta, X) | |
| %PREDICT Predict the label for a trained one-vs-all classifier. The labels | |
| %are in the range 1..K, where K = size(all_theta, 1). | |
| % p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions | |
| % for each example in the matrix X. Note that X contains the examples in | |
| % rows. all_theta is a matrix where the i-th row is a trained logistic | |
| % regression theta vector for the i-th class. You should set p to a vector | |
| % of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2 | |
| % for 4 examples) |
Git for Windows comes bundled with the "Git Bash" terminal which is incredibly handy for unix-like commands on a windows machine. It is missing a few standard linux utilities, but it is easy to add ones that have a windows binary available.
The basic idea is that C:\Program Files\Git\mingw64\ is your / directory according to Git Bash (note: depending on how you installed it, the directory might be different. from the start menu, right click on the Git Bash icon and open file location. It might be something like C:\Users\name\AppData\Local\Programs\Git, the mingw64 in this directory is your root. Find it by using pwd -W).
If you go to that directory, you will find the typical linux root folder structure (bin, etc, lib and so on).
If you are missing a utility, such as wget, track down a binary for windows and copy the files to the corresponding directories. Sometimes the windows binary have funny prefixes, so
Reset
You can reset the commit for a local branches using git reset
To change the commit of a local branch:
git fetch
git reset origin/master --hard
The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.
I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).
So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht