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3 lines
85 KiB
JavaScript
3 lines
85 KiB
JavaScript
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a=t(11),i=t(10),o=t(12);Vue.component("menu",a.Menu),Vue.component("home",i.Home),Vue.component("mnist-cnn",o.MnistCnn),Vue.use(VueMdl.default);var s=new Vue({el:"#app",data:{currentView:"home"}});window.addEventListener("load",r),window.addEventListener("hashchange",r)},function(e,n,t){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.Home=void 0,t(50);n.Home=Vue.extend({template:'\n <div class="demo home">\n <div class="title">Home</div>\n </div>\n '})},function(e,n,t){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.Menu=void 0,t(52);n.Menu=Vue.extend({template:'\n <aside class="menu">\n <h1>Keras.js Demos</h1>\n <p class="menu-label">\n Demos\n </p>\n <ul class="menu-list">\n <li><a href="#/">Home</a></li>\n <li><a href="#/mnist-cnn">Basic Convnet - MNIST</a></li>\n </ul>\n <p class="menu-label">\n Links\n </p>\n <ul class="menu-list">\n <li>\n <a href="https://github.com/transcranial/keras-js" target="_blank">\n <svg height="24" width="24" class="github-logo" viewBox="0 0 16 16" version="1.1" aria-hidden="true"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path></svg>\n GitHub repo\n </a>\n </li>\n </ul>\n <p class="menu-label">\n Contact\n </p>\n <ul class="menu-list contact">\n <li><a href="https://github.com/transcranial" target="_blank">Leon Chen <@transcranial></a></li>\n <li></li>\n </ul>\n </aside>\n '})},function(e,n,t){"use strict";function r(e){if(e&&e.__esModule)return e;var 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s=t(35),c=a(s),l=t(46),u=a(l),p=t(13),d=r(p),f={filepaths:{model:"/demos/data/mnist_cnn/mnist_cnn.json",weights:"/demos/data/mnist_cnn/mnist_cnn_weights.buf",metadata:"/demos/data/mnist_cnn/mnist_cnn_metadata.json"},gpu:!1};Object.assign(f,{filepaths:{model:"demos/data/mnist_cnn/mnist_cnn.json",weights:"https://transcranial.github.io/keras-js-demos-data/mnist_cnn/mnist_cnn_weights.buf",metadata:"demos/data/mnist_cnn/mnist_cnn_metadata.json"}});var m={convolution2d_1:{heading:"32 3x3 filters, border mode valid, 1x1 strides",scalingFactor:2},activation_1:{heading:"ReLU",scalingFactor:2},convolution2d_2:{heading:"32 3x3 filters, border mode valid, 1x1 strides",scalingFactor:2},activation_2:{heading:"ReLU",scalingFactor:2},maxpooling2d_1:{heading:"2x2 pools, 1x1 strides",scalingFactor:2},dropout_1:{heading:"p=0.25 (only active during training phase)",scalingFactor:2},flatten_1:{heading:"",scalingFactor:2},dense_1:{heading:"output dimensionality 128",scalingFactor:4},activation_3:{heading:"ReLU",scalingFactor:4},dropout_2:{heading:"p=0.5 (only active during training phase)",scalingFactor:4},dense_2:{heading:"output dimensionality 10",scalingFactor:8},activation_4:{heading:"Softmax",scalingFactor:8}};n.MnistCnn=Vue.extend({template:'\n <div class="demo mnist-cnn">\n <div class="title">Basic Convnet - MNIST</div>\n <div class="loading-progress" v-if="modelLoading && loadingProgress < 100">\n Loading...{{ loadingProgress }}%\n </div>\n <div class="columns input-output">\n <div class="column input-column">\n <div class="input-container">\n <div class="input-label">Draw any digit (0-9) here <span class="arrow">⤸</span></div>\n <div class="canvas-container">\n <canvas\n id="input-canvas" width="240" height="240"\n @mousedown="activateDraw"\n @mouseup="deactivateDrawAndPredict"\n @mouseleave="deactivateDrawAndPredict"\n @mousemove="draw"\n @touchstart="activateDraw"\n @touchend="deactivateDrawAndPredict"\n @touchmove="draw"\n ></canvas>\n <canvas id="input-canvas-scaled" width="28" height="28" style="display:none;"></canvas>\n <canvas id="input-canvas-centercrop" style="display:none;"></canvas>\n </div>\n <div class="input-clear" v-on:click="clear">\n <i class="material-icons">clear</i>CLEAR\n </div>\n </div>\n </div>\n <div class="column is-2 controls-column">\n <mdl-switch :checked.sync="useGpu" @click="toggleGpu">Use GPU</mdl-switch>\n </div>\n <div class="column output-column">\n <div class="output">\n <div class="output-class"\n v-bind:class="{ \'predicted\': i === predictedClass }"\n v-for="i in outputClasses"\n >\n <div class="output-label">{{ i }}</div>\n <div class="output-bar"\n style="height: {{ Math.round(100 * output[i]) }}px; background: rgba(27, 188, 155, {{ output[i].toFixed(2) }});"\n ></div>\n </div>\n </div>\n </div>\n </div>\n <div class="layer-results-container">\n <div class="bg-line"></div>\n <div\n v-for="layerResult in layerResultImages"\n class="layer-result"\n >\n <div 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e=document.getElementById("input-canvas").getContext("2d"),n=d.centerCrop(e.getImageData(0,0,e.canvas.width,e.canvas.height)),t=document.getElementById("input-canvas-centercrop").getContext("2d");t.canvas.width=n.width,t.canvas.height=n.height,t.putImageData(n,0,0);var r=document.getElementById("input-canvas-scaled").getContext("2d");r.save(),r.scale(28/t.canvas.width,28/t.canvas.height),r.clearRect(0,0,t.canvas.width,t.canvas.height),r.drawImage(document.getElementById("input-canvas-centercrop"),0,0);var a=r.getImageData(0,0,r.canvas.width,r.canvas.height);r.restore();var i=a.data;this.input=new Float32Array(784);for(var o=0,s=i.length;o<s;o+=4)this.input[o/4]=i[o+3]/255;this.output=this.model.predict({input:this.input}).output,this.getIntermediateResults()}},200,{leading:!0,trailing:!0}),getIntermediateResults:function(){var e=this,n=[],t=!0,r=!1,a=void 0;try{for(var i,s=this.model.modelLayersMap.entries()[Symbol.iterator]();!(t=(i=s.next()).done);t=!0){var c=o(i.value,2),l=c[0],u=c[1];if("input"!==l){var p=u.layerClass||"",f=[];u.result&&3===u.result.tensor.shape.length?f=d.unroll3Dtensor(u.result.tensor):u.result&&2===u.result.tensor.shape.length?f=[d.image2Dtensor(u.result.tensor)]:u.result&&1===u.result.tensor.shape.length&&(f=[d.image1Dtensor(u.result.tensor)]),n.push({name:l,layerClass:p,images:f})}}}catch(e){r=!0,a=e}finally{try{!t&&s.return&&s.return()}finally{if(r)throw a}}this.layerResultImages=n,setTimeout(function(){e.showIntermediateResults()},0)},showIntermediateResults:function(){var e=this;this.layerResultImages.forEach(function(n,t){var r=e.layerDisplayConfig[n.name].scalingFactor;n.images.forEach(function(e,n){var a=document.getElementById("intermediate-result-"+t+"-"+n).getContext("2d");a.putImageData(e,0,0);var 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