neuralnetworktests/cnn_quickdraw.html at master · NeedToUpdate/neuralnetworktests · GitHub

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<!DOCTYPE html>
<html>

<head>
    <meta content="text/html;charset=utf-8"
        http-equiv="Content-Type" />
    <meta content="utf-8"
        http-equiv="encoding" />
    <title>cnn attempts</title>
    <script src="lib/matrix.js"></script>
    <script src="lib/nn.js"></script>
    <script src="lib/drawbase.js"></script>

    <style>
        #canvas {
            border: black solid 2px;
            height: 112px;
            width: 112px;
        }

        button {
            z-index: 100;
        }

        #training_data {

            z-index: 0;
        }

        #draw_area {

            z-index: 10;
        }

        #draw_canvas {
            height: 112px;
            width: 112px;
            border: solid black 2px;
            display: block;
            align-self: center;
            left: 42px;
            position: relative;
        }

        #draw_canvas:hover {
            cursor: pointer;
        }

        .container {
            height: 600px;
            width: 200px;
            text-align: center;
            padding: 10px;
            z-index: -10;
            display: inline-table;
            position: relative;

        }

    </style>
</head>

<body>
    <input type="file"
        id="choose" />
    <button id="loaddata"
        type="button">upload</button>
    <button id="download"
        type="button">download</button>
    <button id="start"
        type="button">start</button>
    <button id="restart"
        type="button">restart</button>
    <button id="train"
        type="button">training: off</button>
    <button id="load_model"
        style="background-color: lightyellow;"
        type="button">load trained model</button>
    <br />
    <br />
    <div class="container"
        id="training_data">
        <h4>Train:</h4>
        <canvas height="112"
            width="112"
            id="canvas"></canvas>
        <h5>Target Data:</h5>
        <p id="info1">Label:</p>
        <p id="info2">Array:</p>
        <h5>Guess Data:</h5>
        <p id="info3">Prarr:</p>
        <p id="info4">Prlab:</p>
        <p id="info5">Error:</p>
        <p id="info6">Error:</p>
        <p id="info7">Error:</p>
    </div>
    <div class="container"
        id="draw_area">
        <h4>Draw:</h4>
        <canvas height="112"
            width="112"
            id="draw_canvas"></canvas>
        <label for="brush_stroke">brush size:</label><input type="range"
            min="1"
            max="12"
            value="6"
            id="brush_stroke" />
        <br />
        <button id="input_prediction"
            type="button"
            style="margin: auto">
            guess
        </button>
        <button id="clear_prediction"
            type="button"
            style="margin: auto">
            clear
        </button>
        <p>guess:</p>
        <h1 id="prediction_text"
            style="font-size: 3em; color: blue">?</h1>
        <h6 id="peek_info">
            peek into the feature maps
        </h6>
        <button id="peek_button"
            type="button"
            style="margin: auto">
            peek
        </button>
    </div>
    <script>
        let pictures = 3;
        let dict = {
            0: 'fish',
            1: 'star',
            2: 'car',
            fish: 0,
            star: 1,
            car: 2,
        };
        let brain;
        let training = false;
        let info1 = id('info1');
        let info2 = id('info2');
        let info3 = id('info3');
        let info4 = id('info4');
        let info5 = id('info5');
        let info6 = id('info6');
        let info7 = id('info7');
        let ctx = id('canvas').getContext('2d');
        let somedata = [];

        //useful functions specific to this case
        function drawImage(ctx, data, w, h, pixelsize, startx, starty) {
            let upscale = pixelsize || 4;
            let ww = (w ? w : 28) * upscale;
            let hh = (h ? h : 28) * upscale;
            let pixels = ctx.createImageData(ww, hh);
            let count = 0 - ww * 4 * (upscale - 1);
            data.forEach((pixel, i) => {
                if (i % (ww / upscale) === 0) count += ww * 4 * (upscale - 1);
                let pixel_index_array = [];
                for (let k = 0; k < upscale; k++) {
                    pixel_index_array.push(ww * 4 * k + i * 4 * upscale + count);
                }
                for (let j = 0; j < 4 * upscale; j++) {
                    pixel_index_array.forEach(index => {
                        pixels.data[index + j] = 255 - pixel;
                        if ((j + 1) % 4 === 0) {
                            pixels.data[index + j] = 255;
                        }
                    });
                }
            });
            ctx.putImageData(pixels, startx || 0, starty || 0);
        }

        function loadCSV(csv) {
            let lines = csv.split('\n');
            let arrays = lines.map(x => {
                return x.split(',');
            });
            let nums = [];
            arrays.forEach(arr => {
                let n = {};
                n.label = arr.splice(0, 1)[0];
                n.pixels = arr;
                nums.push(n);
            });
            return nums;
        }
        id('load_model').addEventListener('click', async () => {
            console.log('fetching file..')
            let file = await fetch('./sorta trained model/quickdraw_cnn.json');
            let json = await file.text();
            console.log('loading json...');
            brain = NeuralNetwork.fromJSON(json);
            id('peek_info').innerText =
                'Peek into the hidden layer, darker values means larger weights. (0-' + (brain.layers[0].num_of_outputs - 1) + ')';
            console.log('nn ready');
            id('load_model').style.backgroundColor = 'lightgreen'
        })

        function start() {
            MAINLOOP = setInterval(loop, 1000 / FPS);
            id('start').innerText = 'pause';
            LOOPING = true;
        }

        function stop() {
            clearInterval(MAINLOOP);
            id('start').innerText = 'start';
            LOOPING = false;
        }
        //button functionality
        id('train').addEventListener('click', () => {
            training = !training;
            if (training) {
                id('train').innerText = 'Training: on';
            } else {
                id('train').innerText = 'Training: off';
            }
        });
        let input = id('choose');
        id('loaddata').addEventListener('click', () => {
            if (input.files[0]) {
                if (/[.csv]$/.test(input.files[0].name)) {
                    console.log('loading...');
                    let reader = new FileReader();
                    reader.readAsText(input.files[0]);
                    reader.onload = () => {
                        somedata = loadCSV(reader.result);
                        console.log('csv loaded');
                    };
                } else if (/[.json]$/.test(input.files[0].name)) {
                    console.log('loading json...');
                    let reader = new FileReader();
                    reader.readAsText(input.files[0]);
                    reader.onload = () => {
                        brain = NeuralNetwork.fromJSON(reader.result);
                        console.log('nn ready');
                    };
                } else {
                    console.error('unknown file');
                }
            }
        });
        id('download').addEventListener('click', () => {
            download(brain, 'quickdraw_cnn.json');
        });
        id('restart').addEventListener('click', () => {
            initAll();
            trainings = 0;
            lim = 95;
        });
        id('start').addEventListener('click', () => {
            if (!LOOPING) {
                start();
            } else {
                stop();
            }
        });

        //draw on the canvas
        let brush_size = 6;
        let draw_canvas_width = 112; //canvas is square
        let draw_canvas = id('draw_canvas');
        let draw_ctx = draw_canvas.getContext('2d');
        let draw_img_data = draw_ctx.getImageData(0, 0, draw_canvas_width, draw_canvas_width);
        let clear_data = draw_ctx.getImageData(0, 0, draw_canvas_width, draw_canvas_width);
        let painting = false;

        draw_canvas.addEventListener('mousedown', () => {
            painting = true;
            draw_ctx = draw_canvas.getContext('2d');
            draw_img_data = draw_ctx.getImageData(0, 0, draw_canvas_width, draw_canvas_width);
        });
        draw_canvas.addEventListener('mouseup', () => {
            painting = false;
            draw_ctx.putImageData(draw_img_data, 0, 0);
        });
        id('input_prediction').addEventListener('click', () => {
            let pixels = [];
            draw_img_data = draw_ctx.getImageData(0, 0, draw_canvas_width, draw_canvas_width);
            let scaling = draw_canvas_width / 28;
            for (let i = 0; i < 28; i++) {
                for (let j = 0; j < 28; j++) {
                    if (draw_img_data.data[j * 4 * scaling + i * draw_canvas_width * scaling * 4 + 3] === 255) {
                        pixels.push(255);
                    } else {
                        pixels.push(0);
                    }
                }
            }
            drawImage(ctx, pixels, 28, 28);
            let guess = brain.predict([
                [Matrix.fromArray(pixels, 28, 28)]
            ]);
            id('prediction_text').innerText = dict[denumerate(guess, pictures)];
            let index = argMax(guess);
            let normarray = guess.map(x => ((x * 100) | 0) / 100);
            let lastpart = normarray.splice(index);
            let chosen = lastpart.splice(0, 1);
            info3.innerHTML =
                'Array: \n' +
                `[${normarray.length ? normarray + ',' : ''}<span style="color:blue">${chosen}</span>${lastpart.length ? ',' + lastpart : ''}]`;

            info5.innerText = 'Loss: ' + brain.calc_loss();
        });
        id('clear_prediction').addEventListener('click', () => {
            draw_ctx.putImageData(clear_data, 0, 0);
            id('prediction_text').innerText = '?';
        });
        id('brush_stroke').addEventListener('mouseup', () => {
            brush_size = id('brush_stroke').value;
        });
        draw_canvas.addEventListener('touchend', e => {
            draw_ctx.putImageData(draw_img_data, 0, 0);
        });
        draw_canvas.addEventListener('touchmove', e => {
            e.preventDefault(); // we don't want to scroll
            draw_ctx = draw_canvas.getContext('2d');
            draw_img_data = draw_ctx.getImageData(0, 0, draw_canvas_width, draw_canvas_width);
            let touch = e.touches[0];
            let left = touch.clientX - (draw_canvas.offsetLeft + draw_canvas.parentElement.offsetLeft) - 2;
            if (left < 0) left = 0;
            if (left > draw_canvas_width) left = draw_canvas_width;
            let top = touch.clientY - (draw_canvas.offsetTop + draw_canvas.parentElement.offsetTop) - 2;
            if (top < 0) top = 0;
            if (top > draw_canvas_width) top = draw_canvas_width;
            top = top | 0;
            left = left | 0;

            let pixel_index_array = [];
            for (let k = 0; k < brush_size; k++) {
                pixel_index_array.push(draw_canvas_width * 4 * k + left * 4 + top * draw_canvas_width * 4);
            }
            for (let j = 0; j < 4 * brush_size; j++) {
                pixel_index_array.forEach(index => {
                    draw_img_data.data[index + j] = 0;
                    if ((j + 1) % 4 === 0) {
                        draw_img_data.data[index + j] = 255;
                    }
                });
            }

            draw_ctx.putImageData(draw_img_data, 0, 0);
        });
        draw_canvas.addEventListener('mousemove', e => {
            if (painting) {
                let left = e.clientX - (draw_canvas.offsetLeft + draw_canvas.parentElement.offsetLeft) - 2;
                if (left < 0) left = 0;
                if (left > draw_canvas_width) left = draw_canvas_width;
                let top = e.clientY - (draw_canvas.offsetTop + draw_canvas.parentElement.offsetTop) - 2;
                if (top < 0) top = 0;
                if (top > draw_canvas_width) top = draw_canvas_width;
                top = top | 0;
                left = left | 0;

                let pixel_index_array = [];
                for (let k = 0; k < brush_size; k++) {
                    pixel_index_array.push(draw_canvas_width * 4 * k + left * 4 + top * draw_canvas_width * 4);
                }
                for (let j = 0; j < 4 * brush_size; j++) {
                    pixel_index_array.forEach(index => {
                        draw_img_data.data[index + j] = 0;
                        if ((j + 1) % 4 === 0) {
                            draw_img_data.data[index + j] = 255;
                        }
                    });
                }
                draw_ctx.putImageData(draw_img_data, 0, 0);
            }
        });
        draw_canvas.addEventListener('mouseleave', () => {
            if (painting) {
                painting = false;
                draw_ctx.putImageData(draw_img_data, 0, 0);
            }
        });
        //peeking function
        id('peek_button').addEventListener('click', () => {
            brain.layers[0].weight_array.forEach((arr, i) => {
                let k = brain.layers[0].config.kernel_size;
                drawImage(
                    ctx,
                    arr[0]
                    .copy()
                    .norm()
                    .flatten()
                    .map(val => val * 255),
                    k,
                    k,
                    3,
                    (i * (k + 1) * 3) % (draw_canvas_width - 4),
                    (((i * (k + 1) * 3) / (draw_canvas_width - 4)) | 0) * (k + 1) * 3);
            });
        });

        function initAll() {
            //functin called when restart is hit
            brain = new NeuralNetwork(1);
            brain.addLayer(20, 'conv', {
                color_depth: 'w',
                kernel_size: 5,
                weights_start: 'calculated',
                biases_start: 0,
                norm_weight_val: 4,
                norm_gradient_val: 2,
                output_dropout_rate: 0.2
            });
            brain.addLayer(20, 'empty', {
                activation_fn: 'relu'
            });
            brain.addLayer(20, 'maxpool', {
                color_depth: 'w',
                kernel_size: 2,
            });
            brain.addLayer(60, 'conv', {
                color_depth: 'w',
                kernel_size: 5,
                weights_start: 'calculated',
                biases_start: 0,
                norm_weight_val: 4,
                norm_gradient_val: 2,
                output_dropout_rate: 0.2
            });
            brain.addLayer(60, 'empty', {
                activation_fn: 'sigmoid'
            });
            brain.addLayer(60, 'maxpool', {
                color_depth: 'w',
                kernel_size: 2,
            });
            brain.addLayer(240, 'maxpool', {
                color_depth: 'w',
                kernel_size: 2,
                flatten_layer: true,
                activation_fn: 'linear',
                output_dropout_rate: 0.2
            });
            //brain.addLayer(300, 'dense', {activation_fn: 'sigmoid', weights_start: 'calculated', biases_start: 0});
            brain.addLayer(pictures, 'dense', {
                activation_fn: 'softmax',
                weights_start: 'calculated',
                biases_start: 0
            });
            brain.config.loss_fn = 'cross_entropy';
            brain.config.learning_fn = 'SGD';
            brain.skil_alpha_steps = 100;
            brain.setLearningRate(0.01);
            brain.init();
        }

        initAll();

        function predict(data) {
            return brain.predict([
                [Matrix.fromArray(data, 28, 28)]
            ]);
        }

        function train(num) {
            if (!num) {
                num = 1;
            }
            for (let i = 0; i < num; i++) {
                let data = getData();
                brain.train([
                    [Matrix.fromArray(data.pixels.map(x => parseInt(x)), 28, 28)]
                ], enumerate(data.label, pictures));
                training_times++;
                if (i === num - 1) return data;
            }
        }

        function getData() {
            return somedata[(Math.random() * (somedata.length - 1)) | 0];
        }

        let training_times = 0;
        let correct_times = 0;
        let last_50 = [];
        let accNum = 500;

        function loop() {
            let currData;
            if (training) {
                currData = train(1);
                brain.layers[0].weight_array.forEach((arr, i) => {
                    let k = brain.layers[0].config.kernel_size;
                    drawImage(
                        draw_ctx,
                        arr[0]
                        .copy()
                        .norm()
                        .flatten()
                        .map(val => val * 255),
                        k,
                        k,
                        3,
                        (i * (k + 1) * 3) % (draw_canvas_width - 4),
                        (((i * (k + 1) * 3) / (draw_canvas_width - 4)) | 0) * (k + 1) * 3);
                });
            } else {
                currData = getData();
            }

            let lbl = currData.label;
            let prediction = predict(currData.pixels);
            let pred_num = denumerate(prediction);
            let correct = parseInt(lbl) === pred_num;
            drawImage(ctx, currData.pixels);
            info1.innerText = 'Label: ' + dict[currData.label];
            info2.innerText = 'Array:\n ' + JSON.stringify(enumerate(lbl, pictures));
            let index = argMax(prediction);
            let normarray = prediction.map(x => ((x * 100) | 0) / 100);
            let lastpart = normarray.splice(index);
            let chosen = lastpart.splice(0, 1);
            info3.innerHTML =
                'Array: \n' +
                `[${normarray.length ? normarray + ',' : ''}<span style="color:blue">${chosen}</span>${lastpart.length ? ',' + lastpart : ''}]`;
            info4.innerText = 'Guess: ' + dict[pred_num];
            info5.innerText = 'Loss: ' + brain.calc_loss();
            info6.innerText = 'Loops: ' + training_times;
            info7.innerText =
                'Correct: ' +
                (((last_50.reduce((tot, b) => {
                                if (b) {
                                    return (tot += 1);
                                } else {
                                    return tot;
                                }
                            }, 0) /
                            last_50.length) *
                        100) |
                    0) +
                '% (' +
                last_50.length +
                ')';
            if (correct) {
                info4.style.color = 'green';
                correct_times++;
            } else {
                info4.style.color = 'red';
            }
            last_50.push(correct);
            if (last_50.length > accNum) {
                last_50.splice(0, 1);
                if (last_50.length - accNum > 0) {
                    last_50.splice(0, last_50.length - accNum);
                }
            }
        }

    </script>
</body>

</html>