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e=this,n=document.getElementById("input-canvas").getContext("2d"),t=n.getImageData(0,0,n.canvas.width,n.canvas.height),a=t.data,o=t.width,r=t.height,s=(0,i.default)(new Float32Array(a),[o,r,4]),c=(0,i.default)(new Float32Array(o*r*3),[o,r,3]);l.default.divseq(s,255),l.default.subseq(s,.5),l.default.mulseq(s,2),l.default.assign(c.pick(null,null,0),s.pick(null,null,0)),l.default.assign(c.pick(null,null,1),s.pick(null,null,1)),l.default.assign(c.pick(null,null,2),s.pick(null,null,2));var u={input_1:c.data};this.model.predict(u).then(function(n){e.output=n.predictions,e.modelRunning=!1})},clearAll:function(){this.modelRunning=!1,this.imageURLInput=null,this.imageURLSelect=null,this.imageLoading=!1,this.imageLoadingError=!1,this.output=null,this.model.layersWithResults=[];var e=document.getElementById("input-canvas").getContext("2d");e.clearRect(0,0,e.canvas.width,e.canvas.height)}}})},function(e,n,t){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.Menu=void 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a=document.getElementById("input-canvas-scaled").getContext("2d");a.clearRect(0,0,a.canvas.width,a.canvas.height),this.output=new Float32Array(10),this.drawing=!1,this.strokes=[]},activateDraw:function(e){this.drawing=!0,this.strokes.push([]);var n=this.strokes[this.strokes.length-1];n.push(m.getCoordinates(e))},draw:function(e){if(this.drawing){var n=document.getElementById("input-canvas").getContext("2d");n.lineWidth=20,n.lineJoin=n.lineCap="round",n.strokeStyle="#393E46",n.clearRect(0,0,n.canvas.width,n.canvas.height);var t=this.strokes[this.strokes.length-1];t.push(m.getCoordinates(e));for(var a=0,o=this.strokes.length;a2&&void 0!==arguments[2]?arguments[2]:{};o(this,e),this._type=a.type||Float32Array,n&&n.length&&(n instanceof this._type||n instanceof Array)?(d(n,t),this.tensor=(0,s.default)(n,t),this.tensor=(0,s.default)(new this._type(n),t)):!n.length&&t.length?this.tensor=(0,s.default)(new this._type(t.reduce(function(e,n){return e*n},1)),t):this.tensor=(0,s.default)(new this._type([]),[])}return r(e,[{key:"createWeblasTensor",value:function(){if(this.weblasTensor&&this.weblasTensor.delete(),this.weblasTensorsSplit&&this.weblasTensorsSplit.forEach(function(e){return e.delete()}),1===this.tensor.shape.length){var e=this.tensor.shape[0];if(e>u.MAX_TEXTURE_SIZE){this.weblasTensorsSplit=[];for(var n=Math.ceil(u.MAX_TEXTURE_SIZE/e),t=0;tu.MAX_TEXTURE_SIZE}))throw new Error("[Tensor] cannot create Tensor with both dimensions exceeding MAX_TEXTURE_SIZE");var s=this.tensor.shape[0],l=this.tensor.shape[1];if(s>u.MAX_TEXTURE_SIZE){this.weblasTensorsSplit=[];for(var c=Math.ceil(s/u.MAX_TEXTURE_SIZE),d=0;du.MAX_TEXTURE_SIZE){this.weblasTensorsSplit=[];for(var h=Math.ceil(l/u.MAX_TEXTURE_SIZE),b=0;b .mdl-switch {\n margin-bottom: 5px;\n}\n.demo.inception-v3 .top-container .controls > .mdl-switch .mdl-switch__label {\n font-size: 12px !important;\n}\n.demo.inception-v3 .columns.input-output {\n max-width: 800px;\n margin: 0 auto;\n}\n.demo.inception-v3 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.loading-indicator .error {\n color: #D24D57;\n font-size: 14px;\n font-family: 'Fira Sans',sans-serif;\n margin: 20px;\n}\n.demo.inception-v3 .columns.input-output .column.input-column .canvas-container {\n display: -webkit-inline-box;\n display: -ms-inline-flexbox;\n display: inline-flex;\n -webkit-box-pack: end;\n -ms-flex-pack: end;\n justify-content: flex-end;\n}\n.demo.inception-v3 .columns.input-output .column.input-column .canvas-container canvas {\n background: white;\n}\n.demo.inception-v3 .columns.input-output .column.output-column .output {\n width: 370px;\n height: 160px;\n display: -webkit-box;\n display: -ms-flexbox;\n display: flex;\n -webkit-box-orient: vertical;\n -webkit-box-direction: normal;\n -ms-flex-direction: column;\n flex-direction: column;\n -webkit-box-align: start;\n -ms-flex-align: start;\n align-items: flex-start;\n -webkit-box-pack: center;\n -ms-flex-pack: center;\n justify-content: center;\n}\n.demo.inception-v3 .columns.input-output .column.output-column .output .output-class {\n display: -webkit-box;\n display: -ms-flexbox;\n display: flex;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n -ms-flex-direction: row;\n flex-direction: row;\n -webkit-box-align: center;\n -ms-flex-align: center;\n align-items: center;\n -webkit-box-pack: center;\n -ms-flex-pack: center;\n justify-content: center;\n padding: 6px 0;\n}\n.demo.inception-v3 .columns.input-output .column.output-column .output .output-class .output-label {\n text-align: right;\n width: 200px;\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n font-family: 'Share Tech Mono',sans-serif;\n font-size: 18px;\n color: #69707a;\n padding: 0 6px;\n border-right: 2px solid rgba(27, 188, 155, 0.3);\n}\n.demo.inception-v3 .columns.input-output .column.output-column .output .output-class .output-bar {\n height: 8px;\n -webkit-transition: width 0.2s ease-out;\n transition: width 0.2s ease-out;\n}\n.demo.inception-v3 .columns.input-output .column.output-column .output .output-class .output-value {\n text-align: left;\n margin-left: 5px;\n font-family: 'Share Tech Mono',sans-serif;\n font-size: 14px;\n color: #69707a;\n}\n.demo.inception-v3 .columns.input-output .column.output-column .output .output-class.predicted .output-label {\n color: #1BBC9B;\n border-left-color: #1BBC9B;\n}\n.demo.inception-v3 .columns.input-output .column.output-column .output .output-class.predicted .output-value {\n color: #1BBC9B;\n}\n.demo.inception-v3 .architecture-container {\n min-width: 800px;\n max-width: 1200px;\n margin: 0 auto;\n position: relative;\n}\n.demo.inception-v3 .architecture-container .layers-row {\n display: -webkit-box;\n display: -ms-flexbox;\n display: flex;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n -ms-flex-direction: row;\n flex-direction: row;\n -webkit-box-align: center;\n -ms-flex-align: center;\n align-items: center;\n -webkit-box-pack: center;\n -ms-flex-pack: center;\n justify-content: center;\n margin-bottom: 5px;\n position: relative;\n z-index: 1;\n}\n.demo.inception-v3 .architecture-container .layers-row .layer-column {\n -webkit-box-flex: 1;\n -ms-flex: 1;\n flex: 1;\n display: -webkit-box;\n display: -ms-flexbox;\n display: flex;\n -webkit-box-align: center;\n -ms-flex-align: center;\n align-items: center;\n -webkit-box-pack: center;\n -ms-flex-pack: center;\n justify-content: center;\n padding: 5px;\n}\n.demo.inception-v3 .architecture-container .layers-row .layer-column .layer {\n display: inline-block;\n background: white;\n border: 2px solid white;\n border-radius: 5px;\n padding: 2px 10px 0px;\n margin: 3px;\n}\n.demo.inception-v3 .architecture-container .layers-row .layer-column .layer .layer-class-name {\n color: #1BBC9B;\n font-size: 14px;\n font-weight: bold;\n}\n.demo.inception-v3 .architecture-container .layers-row .layer-column .layer .layer-details {\n color: #999999;\n font-size: 12px;\n font-weight: bold;\n}\n.demo.inception-v3 .architecture-container .layers-row .layer-column .layer.has-result {\n border-color: #1BBC9B;\n}\n.demo.inception-v3 .architecture-container .architecture-connections {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 0;\n}\n.demo.inception-v3 .architecture-container .architecture-connections path {\n stroke-width: 4px;\n stroke: #AAAAAA;\n fill: none;\n}\n",""])},function(e,n,t){n=e.exports=t(3)(),n.push([e.i,"@import url(https://fonts.googleapis.com/css?family=Fira+Sans);",""]),n.push([e.i,"@import url(https://fonts.googleapis.com/css?family=Share+Tech+Mono);",""]),n.push([e.i,"@import url(https://fonts.googleapis.com/css?family=Nothing+You+Could+Do);",""]),n.push([e.i,"body {\n background: -webkit-linear-gradient(bottom, #CCCCCC, #F0F0F0);\n background: linear-gradient(0deg, #CCCCCC, #F0F0F0);\n color: #393E46;\n min-height: 100vh;\n font-family: 'Fira Sans',sans-serif;\n}\n\n.title {\n width: 100%;\n display: -webkit-box;\n display: -ms-flexbox;\n display: flex;\n -webkit-box-orient: horizontal;\n -webkit-box-direction: normal;\n -ms-flex-direction: row;\n flex-direction: row;\n -webkit-box-align: center;\n -ms-flex-align: center;\n align-items: center;\n}\n\n.title span {\n color: #1BBC9B;\n margin-right: 20px;\n}\n\n.subtitle {\n color: #393E46;\n}\n\n.demo {\n padding: 50px 30px;\n}\n\n.demo .loading-progress {\n position: absolute;\n top: 0;\n right: 0;\n padding: 30px;\n color: #1BBC9B;\n font-size: 18px;\n font-family: 'Share Tech Mono',sans-serif;\n padding: 20px 50px;\n margin: 30px;\n background: rgba(255, 255, 255, 0.5);\n border-radius: 10px;\n}\n\n.demo .info-panel {\n width: 100%;\n max-width: 1200px;\n border-radius: 5px;\n background: white;\n color: #393E46;\n padding: 20px 20px 5px 20px;\n margin: 20px auto;\n box-shadow: 5px 5px #CCCCCC;\n}\n\n.demo .info-panel .info-panel-text {\n width: 100%;\n margin-bottom: 10px;\n font-size: 12px;\n}\n\n.demo .info-panel .info-panel-text a {\n color: #1BBC9B;\n -webkit-transition: color 0.2s ease-in;\n transition: color 0.2s ease-in\n}\n\n.demo .info-panel .info-panel-text a:hover{\n color: rgba(27, 188, 155, 0.6);\n}\n\n.demo .info-panel .info-panel-close {\n display: -webkit-box;\n display: -ms-flexbox;\n display: flex;\n -webkit-box-align: center;\n -ms-flex-align: center;\n align-items: center;\n -webkit-box-pack: end;\n -ms-flex-pack: end;\n justify-content: flex-end;\n}\n\n.demo .info-panel .info-panel-close .info-panel-close-btn {\n display: -webkit-box;\n display: -ms-flexbox;\n display: flex;\n -webkit-box-align: center;\n -ms-flex-align: center;\n align-items: center;\n color: #69707a;\n -webkit-transition: color 0.2s ease-in;\n transition: color 0.2s ease-in\n}\n\n.demo .info-panel .info-panel-close .info-panel-close-btn:hover{\n color: rgba(27, 188, 155, 0.3);\n cursor: pointer;\n}\n\n/* MDL overrides */\n\n.demo .mdl-switch__label {\n font-size: 14px !important;\n color: 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background: none;\n}\n.menu .menu-list.github a:hover .github-logo, .menu .menu-list.contact a:hover .github-logo {\n fill: rgba(27, 188, 155, 0.6);\n}\n",""])},function(e,n,t){n=e.exports=t(3)(),n.push([e.i,".demo.mnist-cnn .column {\n display: -webkit-box;\n display: -ms-flexbox;\n display: flex;\n -webkit-box-align: center;\n -ms-flex-align: center;\n align-items: center;\n -webkit-box-pack: center;\n -ms-flex-pack: center;\n justify-content: center;\n}\n.demo.mnist-cnn .column.input-column {\n -webkit-box-pack: end;\n -ms-flex-pack: end;\n justify-content: flex-end;\n}\n.demo.mnist-cnn .column.input-column .input-container {\n text-align: right;\n margin: 20px;\n position: relative;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n.demo.mnist-cnn .column.input-column .input-container .input-label {\n font-family: 'Nothing You Could Do',cursive;\n font-size: 18px;\n color: #69707a;\n text-align: right;\n}\n.demo.mnist-cnn 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{{ outputClasses[i].name }}
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{{ Math.round(100 * outputClasses[i].probability) }}%
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\n
\n Basic Convnet for MNIST\n \n
\n
\n Loading...{{ loadingProgress }}%\n
\n
\n
\n Interactive demo of a simple convnet trained on MNIST (see Jupyter notebook). All computation performed entirely in your browser. Toggling GPU on/off shouldn\'t reveal any significant speed differences, as this is a fairly small network. In the architecture diagram below, intermediate outputs at each layer are also visualized.\n
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closeCLOSE
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Draw any digit (0-9) here ⤸
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clearCLEAR
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\n use GPU\n
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{{ i }}
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\n
\n Convolutional Variational Autoencoder, trained on MNIST\n \n
\n
\n Loading...{{ loadingProgress }}%\n
\n
\n
\n Modified from the MNIST convolution/deconvolution variational autoencoder example here. The network demonstrated here is the generative decoder portion (see Jupyter notebook). The network generates an image through a series of Deconvolution2D layers from coordinates in the 2D latent space. All computation performed entirely in your browser. Toggling GPU on/off shouldn\'t reveal any significant speed differences, as this is a fairly small network. In the architecture diagram below, intermediate outputs at each layer are also visualized.\n
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\n
closeCLOSE
\n
\n
\n
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\n
\n
Click around the latent space ⤸
\n
\n \n
\n -1.5\n x\n 1.5\n
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\n -1.5\n y\n 1.5\n
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\n use GPU\n
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x: {{ inputCoordinates[0] < 0 ? inputCoordinates[0].toFixed(2) : inputCoordinates[0].toFixed(3) }}
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y: {{ inputCoordinates[1] < 0 ? inputCoordinates[1].toFixed(2) : inputCoordinates[1].toFixed(3) }}
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\n 50-layer Residual Network, trained on ImageNet\n \n
\n
\n Loading...{{ loadingProgress }}%\n
\n
\n
\n Note that ~100 MB of weights must be loaded. We use the Keras architecture from here and pretrained weights from here. Enter any valid image URL as input to the network. You can also select from a list of prepopulated image URLs. The endpoint must have CORS enabled, to enable us to extract the numeric data from the canvas element, so not all URLs will work. Imgur and Flickr creative commons all work, and are good places to start. After running the network, the top-5 classes are displayed. Keep in mind also we are limited to the 1,000 classes of ImageNet. Keep in mind that this is image classification and not object detection, so the network is forced to output a single class through softmax. Best results are on images where the classification target spans a large portion of the image. All computation performed entirely in your browser. Toggling GPU on should offer significant speedups compared to CPU. Running the network may still take several seconds (optimizations to come). With "show computational flow" toggled, computation through the network will be shown in the architecture diagram (scroll down as computation is performed layer by layer). Turning this feature off will also speed up computation.\n
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closeCLOSE
\n
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Enter a valid image URL or select an image from the dropdown:
\n
\n \n or\n \n
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\n Use GPU\n Show computation flow\n
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Error loading URL
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{{ outputClasses[i].name }}
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{{ Math.round(100 * outputClasses[i].probability) }}%
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{{ layer.className }}
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{{ layer.details }}
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