niclasmattsson

Nothing here, just a Gist hack to display the title correctly on Gist.
(Prefix the title of this file with a dot on Gist.)

datlife

"""An example of how to use tf.Dataset in Keras Model"""
import tensorflow as tf   # only work from tensorflow==1.9.0-rc1 and after
 
_EPOCHS      = 5
_NUM_CLASSES = 10
_BATCH_SIZE  = 128

def training_pipeline():
  # #############
  # Load Dataset

jeffhuangtw

// Sample: download json as excel file (BOM + utf-16le encoding)
// reference: 
// https://gist.github.com/maciejjankowski/2db91642fb9eaa771111f2c0538e4560
// 
<script>
function JSON2CSV(objArray) {
    var array = typeof objArray != 'object' ? JSON.parse(objArray) : objArray;
    var str = '';
    var line = '';
    // header

kmundnic

function alpha=kriAlpha(data,scale)
% alpha=kriAlpha(data,scale)
%     calculates Krippendorff's Alpha as a measure of inter-rater agreement
%     data: rate matrix, each row is a rater or coder, each column is a case
%     scale: level of measurement, supported are 'nominal', 'ordinal', 'interval'
%     missing values have to be coded as NaN or inf

% For details about Krippendorff's Alpha see:
% http://en.wikipedia.org/wiki/Krippendorff%27s_Alpha
% Hayes, Andrew F. & Krippendorff, Klaus (2007). Answering the call for a

N-McA

def tf_pca(x):
    '''
        Compute PCA on the bottom two dimensions of x,
        eg assuming dims = [..., observations, features]
    '''
    # Center
    x -= tf.reduce_mean(x, -2, keepdims=True)

    # Currently, the GPU implementation of SVD is awful.
    # It is slower than moving data back to CPU to SVD there

erenon

# This example shows how to use keras TensorBoard callback
# with model.train_on_batch

import tensorflow.keras as keras

# Setup the model
model = keras.models.Sequential()
model.add(...) # Add your layers
model.compile(...) # Compile as usual

ServerlessBot

{
    "Statement": [
        {
            "Action": [
                "apigateway:*",
                "cloudformation:CancelUpdateStack",
                "cloudformation:ContinueUpdateRollback",
                "cloudformation:CreateChangeSet",
                "cloudformation:CreateStack",
                "cloudformation:CreateUploadBucket",

cshjin

yalmip('clear')

T = [0 1 0 1; 1 0 1 0; 0 1 0 1; 1 0 1 0];

x = sdpvar(4, 1);
y = sdpvar(4, 1);
assign(x, randn(4, 1));
assign(y, randn(4, 1));
const = [x'*x <= 1; y'*y <=1];
% const = [];

bradtraversy

MySQL Cheat Sheet

Help with SQL commands to interact with a MySQL database

MySQL Locations

• Mac /usr/local/mysql/bin
• Windows /Program Files/MySQL/MySQL version/bin
• Xampp /xampp/mysql/bin

Add mysql to your PATH

vadimkantorov

# ported from https://github.com/pvigier/perlin-numpy/blob/master/perlin2d.py

import torch
import math

def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
    delta = (res[0] / shape[0], res[1] / shape[1])
    d = (shape[0] // res[0], shape[1] // res[1])
    
    grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1