Emmanuel Fuentes eifuentes

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

	def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
	""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
	Args:
	logits: logits distribution shape (vocabulary size)
	top_k >0: keep only top k tokens with highest probability (top-k filtering).
	top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
	Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
	"""
	assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear
	top_k = min(top_k, logits.size(-1)) # Safety check

	---
	AWSTemplateFormatVersion: '2010-09-09'
	Description: Creates a Systems Manager Association with inline PowerShell.
	Resources:
	Association:
	Type: AWS::SSM::Association
	Properties:
	AssociationName: AssociationName
	Name: AWS-RunPowerShellScript
	Parameters:

	conda uninstall --force pillow -y

	# install libjpeg-turbo to $HOME/turbojpeg
	git clone https://github.com/libjpeg-turbo/libjpeg-turbo
	pushd libjpeg-turbo
	mkdir build
	cd build
	cmake .. -DCMAKE_INSTALL_PREFIX:PATH=$HOME/turbojpeg
	make
	make install

	from __future__ import print_function

	from keras.datasets import cifar10
	from keras.layers import merge, Input
	from keras.layers.convolutional import Convolution2D, ZeroPadding2D, AveragePooling2D
	from keras.layers.core import Dense, Activation, Flatten
	from keras.layers.normalization import BatchNormalization
	from keras.models import Model
	from keras.preprocessing.image import ImageDataGenerator
	from keras.utils import np_utils

	g.set_titles("") #set title to blank
	g.set(yticks=[]) #set y ticks to blank
	g.despine(bottom=True, left=True) #remove 'spines'