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keras-js/dist/keras.js
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Leon Chen 9b352c1935 update build
2016-10-25 10:52:17 -04:00

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e=t.tensor.shape[0],r=t.tensor.shape[1],n=s(this.kernelShape,3),o=n[0],i=n[1],a=n[2],u=i+(i-1)*(this.atrousRate[0]-1),c=a+(a-1)*(this.atrousRate[1]-1),l="same"===this.borderMode?Math.floor((e+this.subsample[0]-1)/this.subsample[0]):Math.floor((e-u+this.subsample[0])/this.subsample[0]),f="same"===this.borderMode?Math.floor((r+this.subsample[1]-1)/this.subsample[1]):Math.floor((r-c+this.subsample[1])/this.subsample[1]),p=o,h="same"===this.borderMode?Math.max(0,Math.floor((l-1)*this.subsample[0]+u-e)):0,d="same"===this.borderMode?Math.max(0,Math.floor((f-1)*this.subsample[1]+c-r)):0,v=Math.floor(h/2),y=h-v,g=Math.floor(d/2),_=d-g;this.outputShape=[l,f,p],this.inputPadding=[v,y,g,_]}},{key:"_im2col",value:function(t){var e=s(t.tensor.shape,3),r=e[0],n=e[1],o=e[2],i=this.kernelShape[1],a=this.kernelShape[2],u=this.outputShape[0],c=this.outputShape[1],f=u*c,p=i*a*o,h=i+(i-1)*(this.atrousRate[0]-1),v=a+(a-1)*(this.atrousRate[1]-1);this._imColsMat||(this._imColsMat=new 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e=t.tensor.shape[0],r=t.tensor.shape[1],n=t.tensor.shape[2],o=u(this.kernelShape,4),i=o[0],a=o[1],s=o[2],c=o[3],l="same"===this.borderMode?Math.floor((e+this.subsample[0]-1)/this.subsample[0]):Math.floor((e-a+this.subsample[0])/this.subsample[0]),f="same"===this.borderMode?Math.floor((r+this.subsample[1]-1)/this.subsample[1]):Math.floor((r-s+this.subsample[1])/this.subsample[1]),p="same"===this.borderMode?Math.floor((n+this.subsample[2]-1)/this.subsample[2]):Math.floor((n-c+this.subsample[2])/this.subsample[2]),h=i,d="same"===this.borderMode?Math.max(0,Math.floor((l-1)*this.subsample[0]+a-e)):0,v="same"===this.borderMode?Math.max(0,Math.floor((f-1)*this.subsample[1]+s-r)):0,y="same"===this.borderMode?Math.max(0,Math.floor((p-1)*this.subsample[2]+c-n)):0,g=Math.floor(d/2),_=d-g,b=Math.floor(v/2),m=v-b,w=Math.floor(y/2),x=y-w;this.outputShape=[l,f,p,h],this.inputPadding=[g,_,b,m,w,x]}},{key:"_padInput",value:function(t){if("same"===this.borderMode){var e=u(t.tensor.shape,4),r=e[0],n=e[1],o=e[2],i=e[3],a=u(this.inputPadding,6),s=a[0],c=a[1],l=a[2],f=a[3],p=a[4],h=a[5],v=r+s+c,y=n+l+f,g=o+p+h,b=new d.default([],[v,y,g,i]);_.default.assign(b.tensor.hi(r+s,n+l,o+p,i).lo(s,l,p,0),t.tensor),t.tensor=b.tensor}return t}},{key:"_vol2col",value:function(t){var e=u(t.tensor.shape,4),r=e[0],n=e[1],o=e[2],i=e[3],a=this.kernelShape[1],s=this.kernelShape[2],c=this.kernelShape[3],l=this.outputShape[0],f=this.outputShape[1],p=this.outputShape[2],h=l*f*p,v=a*s*c*i;if(this._volColsMat||(this._volColsMat=new d.default([],[h,v])),1===a&&1===s&&1===c&&1===this.subsample[0]&&1===this.subsample[1]&&1===this.subsample[2])return this._volColsMat.replaceTensorData(t.tensor.data),this._useWeblas&&this._volColsMat.createWeblasTensor(),this._volColsMat;for(var y=new d.default([],[a,s,c,i]),g=0,b=0,m=r-a;b<=m;b+=this.subsample[0])for(var w=0,x=n-s;w<=x;w+=this.subsample[1])for(var O=0,j=o-c;O<=j;O+=this.subsample[2])_.default.assign(y.tensor,t.tensor.hi(b+a,w+s,O+c,i).lo(b,w,O,0)),this._volColsMat.tensor.data.set(y.tensor.data,g),g+=v;return this._useWeblas&&this._volColsMat.createWeblasTensor(),this._volColsMat}},{key:"_w2row",value:function(){for(var t=this.weights.W.tensor.shape[3],e=u(this.kernelShape,4),r=e[0],n=e[1],o=e[2],i=e[3],a=n*o*i*t,s=new d.default([],[a,r]),c=new d.default([],[n,o,i,t]),l=new d.default([],[a]),f=0;f<r;f++)_.default.assign(c.tensor,this.weights.W.tensor.pick(null,null,null,null,f)),l.replaceTensorData(c.tensor.data),_.default.assign(s.tensor.pick(null,f),l.tensor);return s}},{key:"call",value:function(t){"th"===this.dimOrdering&&(t.tensor=t.tensor.transpose(1,2,3,0)),this._calcOutputShape(t),this._padInput(t),this._vol2col(t);var e=this.kernelShape[0],r=this.outputShape[0],n=this.outputShape[1],o=this.outputShape[2],i=r*n*o,a=new d.default([],[i,e]);if(!this._useWeblas||this._volColsMat._gpuMaxSizeExceeded||this._wRowsMat._gpuMaxSizeExceeded){if(this.bias)for(var s=0;s<e;s++)_.default.assigns(a.tensor.pick(null,s),this.weights.b.tensor.get(s));(0,m.default)(a.tensor,this._volColsMat.tensor,this._wRowsMat.tensor,1,1)}else{var u=this.bias?this.weights.b.weblasTensor:this._zerosVec.weblasTensor;a.tensor.data=weblas.pipeline.sgemm(1,this._volColsMat.weblasTensor,this._wRowsMat.weblasTensor,1,u).transfer()}for(var c=new d.default([],this.outputShape),l=new d.default([],[r*n*o]),f=new d.default([],[r,n,o]),p=0;p<e;p++)_.default.assign(l.tensor,a.tensor.pick(null,p)),f.replaceTensorData(l.tensor.data),_.default.assign(c.tensor.pick(null,null,null,p),f.tensor);return t.tensor=c.tensor,this.activation(t),"th"===this.dimOrdering&&(t.tensor=t.tensor.transpose(3,0,1,2)),t}}]),e}(y.default);e.default=w},function(t,e,r){"use strict";function n(t){return t&&t.__esModule?t:{default:t}}function o(t){if(t&&t.__esModule)return 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function(e,r){if(Array.isArray(e))return e;if(Symbol.iterator in Object(e))return t(e,r);throw new TypeError("Invalid attempt to destructure non-iterable instance")}}(),c=function(){function t(t,e){for(var r=0;r<e.length;r++){var n=e[r];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(e,r,n){return r&&t(e.prototype,r),n&&t(e,n),e}}(),l=function t(e,r,n){null===e&&(e=Function.prototype);var o=Object.getOwnPropertyDescriptor(e,r);if(void 0===o){var i=Object.getPrototypeOf(e);return null===i?void 0:t(i,r,n)}if("value"in o)return o.value;var a=o.get;if(void 0!==a)return a.call(n)},f=r(19),p=o(f),h=r(3),d=n(h),v=r(1),y=n(v),g=r(4),_=n(g),b=r(63),m=n(b),w=function(t){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};i(this,e);var r=a(this,(e.__proto__||Object.getPrototypeOf(e)).call(this,t));r.layerClass="Deconvolution2D";var n=t.nbFilter,o=void 0===n?1:n,s=t.nbRow,u=void 0===s?1:s,c=t.nbCol,l=void 0===c?1:c,f=t.outputShape,h=void 0===f?[]:f,d=t.activation,v=void 0===d?"linear":d,y=t.borderMode,g=void 0===y?"valid":y,_=t.subsample,b=void 0===_?[1,1]:_,m=t.dimOrdering,w=void 0===m?"tf":m,x=t.bias,O=void 0===x||x;if(r.kernelShape=[o,u,l],null==h[0]?r.outputShape=h.slice(1):r.outputShape=h,r.activation=p[v],"valid"!==g&&"same"!==g)throw new Error(r.name+" [Deconvolution2D layer] Invalid borderMode.");if(r.borderMode=g,r.subsample=b,"tf"!==w&&"th"!==w)throw new Error(r.name+" [Deconvolution2D layer] Only tf and th dim ordering are allowed.");return r.dimOrdering=w,r.bias=O,r.params=r.bias?["W","b"]:["W"],r}return s(e,t),c(e,[{key:"setWeights",value:function(t){"th"===this.dimOrdering&&(t[0].tensor=t[0].tensor.transpose(2,3,1,0)),l(e.prototype.__proto__||Object.getPrototypeOf(e.prototype),"setWeights",this).call(this,t),this._wRowsMat=this._w2row(),this._useWeblas&&(this._wRowsMat.createWeblasTensor(),this._wRowsMat.weblasTensor=this._wRowsMat.weblasTensor.transpose())}},{key:"_calcOutputPadding",value:function(t){var e=t.tensor.shape[0],r=t.tensor.shape[1],n=this.kernelShape[1],o=this.kernelShape[2],i=this.outputShape[0],a=this.outputShape[1],s="same"===this.borderMode?Math.max(0,Math.floor((e-1)*this.subsample[0]+n-i)):0,u="same"===this.borderMode?Math.max(0,Math.floor((r-1)*this.subsample[1]+o-a)):0,c=Math.floor(s/2),l=s-c,f=Math.floor(u/2),p=u-f;this.outputPadding=[c,l,f,p]}},{key:"_im2col",value:function(t){for(var e=u(t.tensor.shape,3),r=e[0],n=e[1],o=e[2],i=new d.default([],[r*n,o]),a=new d.default([],[r*n]),s=new d.default([],[r,n]),c=0;c<o;c++)_.default.assign(s.tensor,t.tensor.pick(null,null,c)),a.replaceTensorData(s.tensor.data),_.default.assign(i.tensor.pick(null,c),a.tensor);return i}},{key:"_w2row",value:function(){for(var t=u(this.weights.W.tensor.shape,4),e=t[0],r=t[1],n=t[2],o=t[3],i=new d.default([],[n,e*r*o]),a=new d.default([],[e*r*o]),s=new d.default([],[e,r,o]),c=0;c<n;c++)_.default.assign(s.tensor,this.weights.W.tensor.pick(null,null,c,null)),a.replaceTensorData(s.tensor.data),_.default.assign(i.tensor.pick(c,null),a.tensor);return i}},{key:"call",value:function(t){"th"===this.dimOrdering&&(t.tensor=t.tensor.transpose(1,2,0));var e=this._im2col(t);this._useWeblas&&e.createWeblasTensor();var r=t.tensor.shape[0],n=t.tensor.shape[1],o=u(this.kernelShape,3),i=o[0],a=o[1],s=o[2],c=new d.default([],[r*n,a*s*i]);if(!this._useWeblas||e._gpuMaxSizeExceeded||this._wRowsMat._gpuMaxSizeExceeded)(0,m.default)(c.tensor,e.tensor,this._wRowsMat.tensor,1,1);else{var l=new 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* The MIT License (MIT)
*
* Copyright (c) 2013-2015 Petka Antonov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*
*/
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