ml5/dist/ml5.min.js

A friendly machine learning library for the web.

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/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
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e.shape}):r.shape}),r},e.prototype.registerTensor=function(e){var t=this.refCounter.has(e.dataId)?this.refCounter.get(e.dataId):0;this.numTensors++,0===t&&(this.numDataBuffers++,"complex64"!==e.dtype&&(this.numBytes+=b(e.shape)*M(e.dtype)),this.backend.register(e.dataId,e.shape,e.dtype)),this.refCounter.set(e.dataId,t+1),e instanceof ee||this.track(e)},e.prototype.registerVariable=function(e){if(null!=this.registeredVariables[e.name])throw new Error("Variable with name "+e.name+" was already registered");this.registeredVariables[e.name]=e},e.prototype.disposeTensor=function(e){if(this.refCounter.has(e.dataId)){this.keepTensors.has(e.id)&&this.keepTensors.delete(e.id),this.numTensors--;var t=this.refCounter.get(e.dataId);t<=1?(this.refCounter.delete(e.dataId),this.backend.disposeData(e.dataId),this.numDataBuffers--,"complex64"!==e.dtype&&(this.numBytes-=b(e.shape)*M(e.dtype))):this.refCounter.set(e.dataId,t-1)}},e.prototype.disposeVariables=function(){for(var e in 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a={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:r,outputs:[t],gradient:function(e){var t={};return n(e).forEach(function(e,n){t[n]=function(){return e}}),t}};this.activeTape.push(a)},e.prototype.keep=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.");return this.keepTensors.add(e.id),e},e.prototype.startScope=function(e,t){void 0===t&&(t=!1),t&&0===this.gradientScopeCount&&(this.activeTape=[]),t&&this.gradientScopeCount++;var n={track:[],name:"unnamed scope"};e&&(n.name=e),this.scopeStack.push(n),this.activeScope=n},e.prototype.endScope=function(e,t){var n=this;void 0===t&&(t=!1),t&&(this.gradientScopeCount--,0===this.gradientScopeCount&&(this.activeTape=null));var r=new Set(this.keepTensors),a=re(e);a.forEach(function(e){return r.add(e.id)});for(var i=0;i<this.activeScope.track.length;i++){var o=this.activeScope.track[i];r.has(o.id)||(null!=this.activeTape?a.push(o):o.dispose())}var s=this.scopeStack.pop();this.activeScope=0===this.scopeStack.length?{track:[],name:"default scope"}:this.scopeStack[this.scopeStack.length-1],a.forEach(function(e){!n.keepTensors.has(e.id)&&function(e,t){for(var n=0;n<t.length;n++)if(t[n].id===e.id)return!0;return!1}(e,s.track)&&n.track(e)})},e.prototype.gradients=function(e,t,n,r){var a=this;return void 0===r&&(r=!1),d(t.length>0,"gradients() received an empty list of xs."),this.tidy("gradients",function(){var i=e();d(i instanceof Q,"The result y returned by f() must be a tensor.");var o=function(e,t,n){for(var r={},a={},i=0;i<t.length;i++)r[t[i].id]=!0;for(i=0;i<e.length;i++){var o=(d=e[i]).inputs;for(var s in o){for(var u=o[s],l=!1,c=0;c<t.length;c++)if(r[u.id]){d.outputs.forEach(function(e){return r[e.id]=!0}),l=!0,a[d.id]=!0;break}if(l)break}}var f={};f[n.id]=!0;var p={};for(i=e.length-1;i>=0;i--)for(o=(d=e[i]).inputs,c=0;c<d.outputs.length;c++)if(f[d.outputs[c].id]){for(var s in o)f[o[s].id]=!0,p[d.id]=!0;break}var h=[];for(i=0;i<e.length;i++){var d;if(a[(d=e[i]).id]&&p[d.id]){var m={};for(var s in d.inputs){var g=d.inputs[s];r[g.id]&&(m[s]=g)}var y=Object.assign({},d);y.inputs=m,y.outputs=d.outputs,h.push(y)}}return h}(a.activeTape,t,i);if(!r&&0===o.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");var s={};return s[i.id]=null==n?function(e){var t=z(b(e),"float32");return Q.make(e,{values:t})}(i.shape):n,function(e,t){for(var n=function(n){var r=t[n],a=[];if(r.outputs.forEach(function(t){var n=e[t.id];if(null!=n)a.push(n);else{var r=Q.make(t.shape,{values:B(t.size,t.dtype)},t.dtype);a.push(r)}}),null==r.gradient)throw new Error("Cannot compute gradient: gradient function not found for "+r.name+".");var i=r.gradient(1===r.outputs.length?a[0]:a);for(var o in r.inputs){if(!(o in i))throw new Error("Cannot backprop through input "+o+". Available gradients found: "+Object.keys(i)+".");var s=i[o](),u=r.inputs[o];if(!w(s.shape,u.shape))throw new Error("Error in gradient for op "+r.name+". The gradient of input '"+o+"' has shape '"+s.shape+"', which does not match the shape of the input '"+u.shape+"'");if(null==e[u.id])e[u.id]=s;else{var l=e[u.id];e[u.id]=l.add(s),l.dispose()}}},r=t.length-1;r>=0;r--)n(r)}(s,o),{value:i,grads:t.map(function(e){return s[e.id]})}},!0)},e.prototype.customGrad=function(e){var t=this;return d(R(e),"The f passed in customGrad(f) must be a function."),function(){for(var n,r,a=[],i=0;i<arguments.length;i++)a[i]=arguments[i];return d(a.every(function(e){return e instanceof Q}),"The args passed in customGrad(f)(x1, x2,...) must all be tensors"),t.scopedRun(function(){return t.customGradientDepth++},function(){return t.customGradientDepth--},function(){r=t.tidy(e.name,function(){var t=e.apply(void 0,a),r=t.value,i=t.gradFunc;return d(r instanceof Q,"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),d(R(i),"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n=i,r},!0)}),t.shouldRecord()&&t.addTapeNode(a,r,function(e){var t=n(e),r=Array.isArray(t)?t:[t];return d(r.length===a.length,"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),d(r.every(function(e){return e instanceof Q}),"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."),r}),r}},e.prototype.write=function(e,t){this.backend.write(e,t)},e.prototype.readSync=function(e){return this.backend.readSync(e)},e.prototype.read=function(e){return this.backend.read(e)},e.prototype.fromPixels=function(e,t){return this.backend.fromPixels(e,t)},e.prototype.time=function(e){return l(this,void 0,void 0,function(){var t,n;return c(this,function(r){switch(r.label){case 0:return t=F(),[4,this.backend.time(e)];case 1:return(n=r.sent()).wallMs=F()-t,[2,n]}})})},e.prototype.track=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. 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