#概要
tensorflow.jsで手書き分類、やってみた。
#以下から、モデルお借りした。
#サンプルコード
var canvas = $("#canvas").get(0);
var touchableDevice = ('ontouchstart' in window);
if (canvas.getContext)
{
	var context = canvas.getContext('2d');
	var drawing = false;
	var prev = {};
	canvas.width = 2 * $("#canvas").width();
	canvas.height = 2 * $("#canvas").height();
	context.scale(2.0, 2.0);
	context.lineJoin = "round";
	context.lineCap = "round";
	context.lineWidth = 20;
	context.strokeStyle = 'rgb(0, 0, 0)';
	$("#canvas").bind('touchstart mousedown', function(e) {
		e.preventDefault();
		prev = getPointOnCanvas(this, event, e);
		drawing = true;
	});
	$("#canvas").bind('touchmove mousemove', function(e) {
		if (drawing == false) return;
		e.preventDefault();
		curr = getPointOnCanvas(this, event, e);
		context.beginPath();
		context.moveTo(prev.x, prev.y);
		context.lineTo(curr.x, curr.y);
		context.stroke();
		prev = curr;
	});
	$("#canvas").bind('touchend mouseup mouseleave', function(e) {
		drawing = false;
	});
	var getPointOnCanvas = function(elem, windowEvent, touchEvent) {
		return {
			x : (touchableDevice ? windowEvent.changedTouches[0].clientX : touchEvent.clientX) - $(elem).offset().left,
			y : (touchableDevice ? windowEvent.changedTouches[0].clientY : touchEvent.clientY) - $(elem).offset().top
		};
	};
	$("#run_button").click(function() {
		test_predict();
	});
	$("#delete_button").click(function() {
		context.clearRect(0, 0, 280, 280);
	});
	var getImageBuffer = function(context, width, height) {
		var tmpCanvas = $('<canvas>').get(0);
		tmpCanvas.width = width;
		tmpCanvas.height = height;
		var tmpContext = tmpCanvas.getContext('2d');
		tmpContext.drawImage(context.canvas, 0, 0, width, height);
		var image = tmpContext.getImageData(0, 0, width, height);
		var buffer = []
		for (var i = 0; i < image.data.length; i += 4)
		{
			var sum = image.data[i + 0] + image.data[i + 1] + image.data[i + 2] + image.data[i + 3];
			buffer.push(Math.min(sum, 255));
		}
		return buffer;
	};
}
var test_predict;
tf.loadModel('https://rawgit.com/CreativeGP/tensorflowjs-mnist/master/model/model.json').then((model) => {
    test_predict = function() {
	    var p = getImageBuffer(context, 28, 28);
        const buffer = tf.buffer([1, 28, 28, 1]);
        for (var i = 0; i < 28; i++) 
        {
            for (var j = 0; j < 28; j++)
            {
                var s = i * 28 + j;
                var v = p[s] / 255.0;
                buffer.set(v, 0, i, j, 0);
            }
        }
        const input = buffer.toTensor();
        const predict = model.predict(input).argMax().dataSync();
        alert(predict);
    }
});
#成果物
#モデルを変更した。
#成果物
以上。