notnil

'use strict';
const ImageSearchAPIClient = require('azure-cognitiveservices-imagesearch');
const CognitiveServicesCredentials = require('ms-rest-azure').CognitiveServicesCredentials;

//replace this value with your valid subscription key.
let serviceKey = "ENTER YOUR KEY HERE";

//the search term for the request
let searchTerm = "sixgill shark";

notnil

class DataGenerator(tf.keras.utils.Sequence):
    'Generates data for Keras'
    def __init__(self, labels, batch_size=BATCH_SIZE, shuffle=True, arch=MODEL_MOBILENET_V2):
        'Initialization'
        self.batch_size = batch_size
        self.labels = labels
        self.shuffle = shuffle
        self.arch = arch        
        self.on_epoch_end()

notnil

IMAGE_SHAPE = (224, 224, 3)
feature_extractor_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
image_input = tf.keras.Input(shape=IMAGE_SHAPE, name='img')
nn = hub.KerasLayer(feature_extractor_url,input_shape=IMAGE_SHAPE,trainable=True)(image_input)
outputs = []
for i in range(64):
  out = layers.Dense(13, activation='softmax')(nn)
  outputs.append(out)
model = tf.keras.models.Model(inputs=image_input, outputs=outputs)
model.compile(optimizer='adam', loss=["categorical_crossentropy"] * 64, loss_weights=[1.0] * 64)

notnil

IMAGE_SHAPE = (224, 224, 3)
feature_extractor_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
model = tf.keras.Sequential([
  hub.KerasLayer(feature_extractor_url, input_shape=IMAGE_SHAPE, trainable=True),
  tf.keras.layers.Dense(13, activation='softmax')  
])
model.compile(optimizer='adam', loss="categorical_crossentropy")

notnil

private const int resWidth = 1600;
private const int resHeight = 1000;
public static byte[] TakeScreenshot(Camera camera)
{
    RenderTexture rt = new RenderTexture(resWidth, resHeight, 24);
    camera.targetTexture = rt;
    Texture2D screenShot = new Texture2D(resWidth, resHeight, TextureFormat.RGB24, false);
    camera.Render();
    RenderTexture.active = rt;
    screenShot.ReadPixels(new Rect(0, 0, resWidth, resHeight), 0, 0);

notnil

private static readonly Vector3 PositionA1 = new Vector3(-9.0f, 1.1f, 9.0f);
private static readonly Vector3 PositionH8 = new Vector3(9.0f, 1.1f, -9.0f);
    
private Vector3 positionForSquare(Square sq)
{
    int row = (int)sq / 8;
    float dx = Math.Abs(PositionA1.x - PositionH8.x);
    float x = PositionA1.x + (((float)row * dx) / 7f);

    int file = (int)sq % 8;

notnil

public class Board
    {
        public Dictionary<Square, Piece> pieceMap;

        public Board(Dictionary<Square, Piece> pieceMap)
        {
            this.pieceMap = new Dictionary<Square, Piece>(pieceMap);
        }
        public static Board Random()
        {

notnil

package main

import (
	"bytes"
	"encoding/json"
	"io"
	"log"
	"net/http"
	"net/url"
)

notnil

require 'active_record'

class Note < ActiveRecord::Base
  attr_accessible :body, :title
end

notnil

require 'sinatra'
require 'activerecord'
require './model'

set :content_type, :json
  
configure do
  db = URI.parse(ENV['HEROKU_POSTGRESQL_COBALT_URL'] || 'postgres://localhost/mydb')
  ActiveRecord::Base.establish_connection(
  :adapter  => db.scheme == 'postgres' ? 'postgresql' : db.scheme,