@tensorflow/tfjs-layers/dist/tf-layers.es2017.min.js

/**
 * @license
 * Copyright 2022 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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got ${t.length}`);e=t[0]}else e=t;return e}function Ut(t){if(Array.isArray(t)&&Array.isArray(t[0])){if(1===t.length)return t[0];throw new a(`Expected exactly 1 Shape; got ${t.length}`)}return t}function Pt(t){let e=0;for(const s of t)0===s.shape.length?e+=1:e+=s.shape.reduce(((t,e)=>t*e));return e}const jt="Variable";class Vt{constructor(t,e="float32",s="Variable",n=!0,r=null){this.dtype=null==e?"float32":e,this.shape=t.shape,this.id=T(),s=null==s?jt:s,this.originalName=V(s),this.name=q(this.originalName),this.trainable_=n,this.constraint=r,this.val=i.variable(t,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(t){return this.assertNotDisposed(),function(t,e){if(t.shape.toString()!==e.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(t.shape)+" vs. "+JSON.stringify(e.shape))}(this.val,t),this.val.id!==t.id&&(this.val.assign(t),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(t){this.trainable_=t,this.val.trainable=t}}function qt(t){return t.map((t=>t.read()))}function Jt(t){t.forEach((t=>{t[0].write(t[1])}))}class Ht{constructor(t){this.dtype=t.dtype,this.shape=t.shape,null!=t.shape?this.ndim=t.shape.length:this.ndim=t.ndim,this.maxNDim=t.maxNDim,this.minNDim=t.minNDim,this.axes=t.axes||{}}}class Zt{constructor(t,e,s,i,n,r,a){this.dtype=t,this.shape=e,this.sourceLayer=s,this.inputs=i,this.callArgs=n,this.outputTensorIndex=a,this.id=T(),null!=r&&(this.originalName=V(r),this.name=q(this.originalName)),this.rank=e.length}}let Kt=0;class Gt{constructor(t,e){this.callArgs=e,this.id=Kt++,this.outboundLayer=t.outboundLayer,this.inboundLayers=t.inboundLayers,this.nodeIndices=t.nodeIndices,this.tensorIndices=t.tensorIndices,this.inputTensors=t.inputTensors,this.outputTensors=t.outputTensors,this.inputMasks=t.inputMasks,this.outputMasks=t.outputMasks,this.inputShapes=t.inputShapes,this.outputShapes=t.outputShapes;for(const e of t.inboundLayers)null!=e&&e.outboundNodes.push(this);t.outboundLayer.inboundNodes.push(this)}getConfig(){const t=[];for(const e of this.inboundLayers)null!=e?t.push(e.name):t.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:t,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let Yt=0;class Xt extends e.serialization.Serializable{constructor(t={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Yt++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let e=t.name;if(!e){const t=this.getClassName();e=f(t)+"_"+D(t)}if(this.name=e,this.trainable_=null==t.trainable||t.trainable,null!=t.inputShape||null!=t.batchInputShape){let e;if(null!=t.batchInputShape)e=t.batchInputShape;else if(null!=t.inputShape){let s=null;null!=t.batchSize&&(s=t.batchSize),e=[s].concat(t.inputShape)}this.batchInputShape=e;let s=t.dtype;null==s&&(s=t.inputDType),null==s&&(s="float32"),this.dtype=s}null!=t.weights?this.initialWeights=t.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(t,e){return t.name+"_ib-"+e.toString()}getNodeAtIndex(t,e){if(0===this.inboundNodes.length)throw new r(`The layer has never been called and thus has no defined ${e}.`);if(this.inboundNodes.length<=t)throw new a(`Asked to get ${e} at node ${t}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[t]}getInputAt(t){return d(this.getNodeAtIndex(t,"input").inputTensors)}getOutputAt(t){return d(this.getNodeAtIndex(t,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new n(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(0===this.inboundNodes.length)throw new n(`Layer ${this.name} is not connected, no input to return.`);return d(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new n(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new n(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return d(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map((t=>t()))}get updates(){return this._updates}get built(){return this._built}set built(t){this._built=t}get trainable(){return this.trainable_}set trainable(t){this._trainableWeights.forEach((e=>e.trainable=t)),this.trainable_=t}get trainableWeights(){return this.trainable_?this._trainableWeights.filter((t=>t.trainable)):[]}set trainableWeights(t){this._trainableWeights=t}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter((t=>!t.trainable)).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(t){this._nonTrainableWeights=t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(t){if(t=g(t),null==this.inputSpec||0===this.inputSpec.length)return;const e=g(this.inputSpec);if(t.length!==e.length)throw new a(`Layer ${this.name} expects ${e.length} inputs, but it received ${t.length} input tensors. Input received: ${t}`);for(let s=0;s<t.length;s++){const i=t[s],n=e[s];if(null==n)continue;const r=i.rank;if(null!=n.ndim&&r!==n.ndim)throw new a(`Input ${s} is incompatible with layer ${this.name}: expected ndim=${n.ndim}, found ndim=${r}`);if(null!=n.maxNDim&&r>n.maxNDim)throw new a(`Input ${s} is incompatible with layer ${this.name}: expected max_ndim=${n.maxNDim}, found ndim=${r}`);if(null!=n.minNDim&&r<n.minNDim)throw new a(`Input ${s} is incompatible with layer ${this.name}: expected min_ndim=${n.minNDim}, found ndim=${r}.`);if(null!=n.dtype&&i.dtype!==n.dtype)throw new a(`Input ${s} is incompatible with layer ${this.name} : expected dtype=${n.dtype}, found dtype=${i.dtype}.`);if(n.axes){const t=i.shape;for(const e in n.axes){const i=Number(e),r=n.axes[e],o=i>=0?t[i]:t[t.length+i];if(null!=r&&-1===[r,null].indexOf(o))throw new a(`Input ${s} is incompatible with layer ${this.name}: expected axis ${i} of input shape to have value ${r} but got shape ${t}.`)}}if(null!=n.shape)for(let t=0;t<n.shape.length;++t){const e=n.shape[t],r=i.shape[t];if(null!=e&&null!=r&&e!==r)throw new a(`Input ${s} is incompatible with layer ${this.name}: expected shape=${n.shape}, found shape=${i.shape}.`)}}}call(t,e){return t}invokeCallHook(t,e){null!=this._callHook&&this._callHook(t,e)}setCallHook(t){this._callHook=t}clearCallHook(){this._callHook=null}apply(t,e){e=e||{},this.assertNotDisposed();const s=g(t);let i=!0;for(const t of s)if(!(t instanceof Zt)){i=!1;break}let n=!0;for(const t of s)if(t instanceof Zt){n=!1;break}if(i===n)throw new a("Arguments to apply() must be all SymbolicTensors or all Tensors");return j(this.name,(()=>{if(!this.built){this.assertInputCompatibility(t);const e=[];for(const s of g(t))e.push(s.shape);this.build(d(e)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&n&&(this._refCount=1)}if(this.assertInputCompatibility(t),n){let i=this.call(t,e);const n=g(i),r=[];for(let t of n)-1!==s.indexOf(t)&&(t=t.clone()),r.push(t);if(i=d(r),null!=this.activityRegularizer)throw new o("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}{const s=function(t){t=g(t);const e=[];for(const s of t)e.push(s.shape);return d(e)}(t),i=this.computeOutputShape(s);let n;const r="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(t)?s[0]:s),n=null!=i&&i.length>0&&Array.isArray(i[0])?i.map(((s,i)=>new Zt(r,s,this,g(t),e,this.name,i))):new Zt(r,i,this,g(t),e,this.name),this.addInboundNode(t,n,null,null,s,i,e),this._refCount++,null!=this.activityRegularizer)throw new o("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return n}}))}warnOnIncompatibleInputShape(t){if(null!=this.batchInputShape)if(t.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(t)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let e=!1;this.batchInputShape.forEach(((s,i)=>{null!=s&&null!=t[i]&&t[i]!==s&&(e=!0)})),e&&console.warn(`The shape of the input tensor (${JSON.stringify(t)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new n(`The layer ${this.name} has never been called and thus has no defined output shape.`);const t=[];for(const e of this.inboundNodes){const s=JSON.stringify(e.outputShapes);-1===t.indexOf(s)&&t.push(s)}if(1===t.length){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&1===t.length?t[0]:t}throw new n(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new r(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Pt(this.weights)}build(t){this.built=!0}getWeights(t=!1){return qt(t?this.trainableWeights:this.weights)}setWeights(t){e.tidy((()=>{const s=this.weights;if(s.length!==t.length)throw new a(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${t.length}, but the layer was expecting ${s.length} weights. Provided weights: ${t}...`);if(0===s.length)return;const i=[],n=qt(s);for(let r=0;r<n.length;++r){const o=n[r],l=s[r],u=t[r];if(!e.util.arraysEqual(o.shape,u.shape))throw new a(`Layer weight shape ${o.shape} not compatible with provided weight shape ${u.shape}`);i.push([l,u])}Jt(i)}))}addWeight(t,e,s,i,n,r,o,l){if(-1!==this._addedWeightNames.indexOf(t))throw new a(`Duplicate weight name ${t} for layer ${this.name}`);this._addedWeightNames.push(t),null==s&&(s="float32"),this.fastWeightInitDuringBuild&&(i=null!=l?l():_t("zeros"));const u=i.apply(e,s),h=new Vt(u,s,t,r,o);return u.dispose(),null!=n&&this.addLoss((()=>n.apply(h.read()))),null==r&&(r=!0),r?this._trainableWeights.push(h):this._nonTrainableWeights.push(h),h}setFastWeightInitDuringBuild(t){this.fastWeightInitDuringBuild=t}addLoss(t){null==t||Array.isArray(t)&&0===t.length||(t=g(t),void 0!==this._losses&&null!==this._losses&&this.losses.push(...t))}computeOutputShape(t){return t}computeMask(t,e){if(!this.supportsMasking){if(null!=e){if(!Array.isArray(e))throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);e.forEach((t=>{if(null!=t)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)}))}return null}return e}addInboundNode(t,e,s,i,n,r,a=null){const o=g(t);e=g(e),s=g(s),i=g(i),n=Bt(n),r=Bt(r);const l=[],u=[],h=[];for(const t of o)l.push(t.sourceLayer),u.push(t.nodeIndex),h.push(t.tensorIndex);new Gt({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:h,inputTensors:o,outputTensors:e,inputMasks:s,outputMasks:i,inputShapes:n,outputShapes:r},a);for(let t=0;t<e.length;t++)e[t].sourceLayer=this,e[t].nodeIndex=this.inboundNodes.length-1,e[t].tensorIndex=t}getConfig(){const t={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(t.batchInputShape=this.batchInputShape),null!=this.dtype&&(t.dtype=this.dtype),t}disposeWeights(){return this.weights.forEach((t=>t.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(null===this._refCount)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let t=0;return 0==--this._refCount&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}}}function Qt(t,e,s){if((null==e||null!=s&&s>0)&&(e=t.sourceLayer,s=t.nodeIndex),0===e.inboundNodes.length)return[t];{const t=e.inboundNodes[s];if(0===t.inboundLayers.length)return t.inputTensors;{const e=[];for(let s=0;s<t.inboundLayers.length;s++){const i=Qt(t.inputTensors[s],t.inboundLayers[s],t.nodeIndices[s]);for(const t of i)-1===e.indexOf(t)&&e.push(t)}return e}}}class te extends Xt{constructor(t){if(super({dtype:t.dtype,name:null!=t.name?t.name:D("input").toString()}),null==t.batchSize&&(t.batchSize=null),null==t.sparse&&(t.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=t.sparse,null!=t.inputShape&&null!=t.batchInputShape)throw new a("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let e=t.batchInputShape;if(null==e){if(null==t.inputShape)throw new a("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");e=[t.batchSize].concat(t.inputShape)}else if(null!=t.batchSize)throw new a("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const s=t.dtype||"float32";this.batchInputShape=e,this.dtype=s,this.inputSpec=[{shape:e}];const i=new Zt(this.dtype,this.batchInputShape,this,[],{},this.name);i.nodeIndex=0,i.tensorIndex=0,new Gt({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[i],outputTensors:[i],inputMasks:[null],outputMasks:[null],inputShapes:[e],outputShapes:[e]})}apply(t,e){throw new a(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}function ee(t){if(null==t.batchShape&&null==t.shape)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(null!=t.batchShape&&null!=t.shape)throw new a("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let e=t.batchShape;null!=t.shape&&null==e&&(e=[null].concat(t.shape));let s=t.dtype;null==s&&(s="float32");return new te({batchInputShape:e,name:t.name,dtype:s,sparse:t.sparse}).inboundNodes[0].outputTensors[0]}te.className="InputLayer",e.serialization.registerClass(te);class se{constructor(t){if(this.id2Value={},this.id2Mask={},this.name2Id={},t instanceof se)for(const e in t.id2Value)this.id2Value[e]=t.id2Value[e],e in t.id2Mask&&(this.id2Mask[e]=t.id2Mask[e]);else{if(null==t)return;for(const e of t)this.add(e.key,e.value)}}add(t,s,i){if(null!=this.id2Value[t.id])throw new a(`Duplicate key: name=${t.name}, id=${t.id}`);return this.id2Value[t.id]=function(t,s){if(null==t.dtype||t.dtype===s.dtype)return s;try{return e.cast(s,t.dtype)}catch(e){throw new a(`The dtype of the feed (${s.dtype}) can not 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l)-1!==h.indexOf(t)?u.push(s.getValue(t)):u.push(null);null!=n&&(n.maxNumTensors=-1/0,n.minNumTensors=1/0);const c=l.join(",")+"|"+s.names().sort().join(",");let p,d=ie.get(c);if(null==d){const t=function(t,s){e.util.assert(null!=t&&t.length>0,(()=>"Expected at least one fetch, got none"));let i=[],n={};if(1===t.length){const e=oe(t[0],s);i=e.sorted,n=e.recipientMap}else{const e=new Set;for(const r of t){const{sorted:t,recipientMap:a}=oe(r,s);for(const s of t)e.has(s.name)||(i.push(s),e.add(s.name));for(const t in a)null==n[t]&&(n[t]=new Set),a[t].forEach((e=>n[t].add(e)))}}return{sorted:i,recipientCounts:ae(n)}}(o,s);d=t.sorted,p=t.recipientCounts,ie.put(c,d),ne.put(c,p)}p={},r||Object.assign(p,ne.get(c));const f=new se(s);for(let t=0;t<d.length;++t){if(null!=n){const t=e.memory().numTensors;t>n.maxNumTensors&&(n.maxNumTensors=t),t<n.minNumTensors&&(n.minNumTensors=t)}const a=d[t],o=a.sourceLayer;if(o instanceof te)continue;const h=[],c=[],m=[];let y=!1;for(const t of a.inputs){const e=f.getValue(t),i=f.getMask(t);h.push(e),c.push(i),null!=i&&(y=!0),r||(p[t.name]--,0!==p[t.name]||s.hasKey(t)||-1!==l.indexOf(t.name)||e.isDisposed||!0===t.sourceLayer.stateful||m.push(e))}y&&((i=i||{}).mask=c[0]);const b=g(o.apply(h,i));let w=null;o.supportsMasking&&(w=o.computeMask(h,c));const z=le(a),k=Array.isArray(z)?z:[z];for(let t=0;t<k.length;++t){f.hasKey(k[t])||f.add(k[t],b[t],Array.isArray(w)?w[0]:w);const e=l.indexOf(k[t].name);-1!==e&&(u[e]=b[t])}r||e.dispose(m)}return f.disposeMasks(),a?u:u[0]}function ae(t){const e={};for(const s in t)e[s]=t[s].size;return e}function oe(t,e){const s=new Set,i=[],n={};for(const t of e.names())s.add(t);const r=[],a=[];for(r.push(t);r.length>0;){const t=r[r.length-1];if(s.has(t.name)){r.pop();continue}const e=a[a.length-1]===r.length-1;if(0===t.inputs.length||e)r.pop(),i.push(t),s.add(t.name),e&&a.pop();else{a.push(r.length-1);for(const e of t.inputs)null==n[e.name]&&(n[e.name]=new 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i.mul(t,i.div(s,i.add(Q(),e)))}))}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}}ce.className="MaxNorm",e.serialization.registerClass(ce);class pe extends he{constructor(t){super(),this.defaultAxis=0,this.axis=null!=t.axis?t.axis:this.defaultAxis}apply(t){return e.tidy((()=>i.div(t,i.add(Q(),ue(t,this.axis)))))}getConfig(){return{axis:this.axis}}}pe.className="UnitNorm",e.serialization.registerClass(pe);class de extends he{apply(t){return i.relu(t)}}de.className="NonNeg",e.serialization.registerClass(de);class ge extends he{constructor(t){super(),this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=null!=t.minValue?t.minValue:this.defaultMinValue,this.maxValue=null!=t.maxValue?t.maxValue:this.defaultMaxValue,this.rate=null!=t.rate?t.rate:this.defaultRate,this.axis=null!=t.axis?t.axis:this.defaultAxis}apply(t){return e.tidy((()=>{const 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Please make sure its serialized length is <= 1048576.`)}}function is(t){if(null===t)return!0;if("object"==typeof t){if(Object.getPrototypeOf(t)===Object.prototype){const e=Object.keys(t);for(const s of e){if("string"!=typeof s)return!1;if(!is(t[s]))return!1}return!0}if(Array.isArray(t)){for(const e of t)if(!is(e))return!1;return!0}return!1}{const e=typeof t;return"string"===e||"number"===e||"boolean"===e}}function ns(t,e,s,i=console.log){const n=function(t){let e=!0;const s=[],i=[];for(const e in t.nodesByDepth)s.push(t.nodesByDepth[e]);for(const t of s){if(t.length>1||1===t.length&&t[0].inboundLayers.length>1){e=!1;break}i.push(...t)}if(e)for(const s of t.layers){let t=!1;for(const n of s.inboundNodes)if(-1!==i.indexOf(n)){if(t){e=!1;break}t=!0}if(!e)break}return e}(t),r=["Layer (type)","Input Shape","Output shape","Param #"];let a;if(n?(e=e||90,s=s||[.32,.61,.89,1]):(e=e||115,s=s||[.24,.48,.7,.8,1]),s[s.length-1]<=1&&(s=s.map((t=>Math.floor(e*t)))),!n){r.push("Receives inputs"),a=[];for(const e in t.nodesByDepth)a.push(...t.nodesByDepth[e])}i("_".repeat(e)),rs(r,s,i),i("=".repeat(e));const o=t.layers;for(let t=0;t<o.length;++t)n?as(o[t],s,i):os(o[t],s,a,i),i((t===o.length-1?"=":"_").repeat(e));t.checkTrainableWeightsConsistency();const l=function(t){let e;e=null!=t.collectedTrainableWeights?Pt(t.collectedTrainableWeights):Pt(t.trainableWeights);return e}(t),u=Pt(t.nonTrainableWeights);i(`Total params: ${l+u}`),i(`Trainable params: ${l}`),i(`Non-trainable params: ${u}`),i("_".repeat(e))}function rs(t,e,s=console.log){let i="";for(let s=0;s<t.length;++s)s>0&&(i=i.slice(0,i.length-1)+" "),i+=t[s],i=i.slice(0,e[s]),i+=" ".repeat(e[s]-i.length);s(i)}function as(t,e,s){let i,n;try{n=t.inboundNodes.map((t=>JSON.stringify(t.inputShapes))).join(",")}catch(t){n="multiple"}try{i=JSON.stringify(t.outputShape)}catch(t){i="multiple"}rs([`${t.name} (${t.getClassName()})`,n,i,t.countParams().toString()],e,s)}function os(t,e,s,i){let n,r;try{r=t.inboundNodes.map((t=>JSON.stringify(t.inputShapes))).join(",")}catch(t){r="multiple"}try{n=JSON.stringify(t.outputShape)}catch(t){n="multiple"}const a=[];for(const e of t.inboundNodes)if(!(null!=s&&s.length>0&&-1===s.indexOf(e)))for(let t=0;t<e.inboundLayers.length;++t){const s=e.inboundLayers[t].name,i=e.nodeIndices[t],n=e.tensorIndices[t];a.push(`${s}[${i}][${n}]`)}const o=t.name,l=t.getClassName(),u=0===a.length?"":a[0];rs([`${o} (${l})`,r,n,t.countParams().toString(),u],e,i);for(let t=1;t<a.length;++t)rs(["","","","",a[t]],e,i)}function ls(t,e,s){return("inboundNodes"===t||"outputLayers"===t||"inputLayers"===t)&&0===e&&"string"==typeof s}function us(t,e){if(null===t)return null;if("string"==typeof t)return m(t);if("number"==typeof t||"boolean"==typeof t)return t;if(t instanceof Array){const s=[],i=t.length;for(let n=0;n<i;++n){const i=t[n];ls(e,n,i)?s.push(i):s.push(us(i,e))}return s}{const e={};for(const s of Object.keys(t)){const i=t[s];if("name"===s&&"string"==typeof i)e[s]=i;else{const t=m(s);e[t]=us(i,t)}}return e}}function hs(t,e){if(null==t)return null;if("string"==typeof t)return f(t);if("number"==typeof t||"boolean"==typeof t)return t;if(t instanceof Array){const s=[],i=t.length;for(let n=0;n<i;++n){const i=t[n];ls(e,n,i)?s.push(i):s.push(hs(i,e))}return s}{const e={};for(const s of Object.keys(t)){const i=t[s],n=f(s);e[n]="name"!==s&&"className"!==s||"string"!=typeof i?hs(i,s):i}return e}}const cs="3.20.0";class ps extends Xt{constructor(t){if(super({}),this.containerNodes=new Set,this.name=t.name,null==this.name){const t=this.getClassName().toLowerCase();this.name=D(t)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(t.inputs)?this.inputs=t.inputs.slice():this.inputs=[t.inputs],Array.isArray(t.outputs)?this.outputs=t.outputs.slice():this.outputs=[t.outputs],S(this.inputs).length!==this.inputs.length)throw new a(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((t=>t.name))}`);S(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((t=>t.name))}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const t of this.outputs){const e=t.sourceLayer,s=t.nodeIndex,i=t.tensorIndex;this.outputLayers.push(e),this.outputLayersNodeIndices.push(s),this.outputLayersTensorIndices.push(i)}for(const t of this.inputs){const e=t.sourceLayer,s=t.nodeIndex,i=t.tensorIndex;c(0===s,"input layer has >1 nodes"),c(0===i,"input layer has >1 tensors"),this.inputLayers.push(e),this.inputLayersNodeIndices.push(s),this.inputLayersTensorIndices.push(i)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let e=0;e<this.inputLayers.length;e++){const s=this.inputLayers[e];if(!(s instanceof te))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${t.inputs}. Input ${e} (0-based) originates from layer type ${s.getClassName()}.`);this.inputNames.push(s.name),this.feedInputShapes.push(s.batchInputShape),this.feedInputNames.push(s.name)}for(const t of this.outputLayers)this.outputNames.push(t.name);this.internalInputShapes=this.inputs.map((t=>t.shape)),this.internalOutputShapes=this.outputs.map((t=>t.shape));const e={},s={},i={},n={},o={},l=[],u=(t,e,s,i,n,a)=>{null!=i&&null!=n&&null!=a||(i=t.sourceLayer,n=t.nodeIndex,a=t.tensorIndex);const h=i.inboundNodes[n];if(-1!==s.indexOf(h))throw new r(`The tensor ${t.name} at layer "${i.name}" is part of a cycle.`);if(-1!==e.indexOf(h))return;this.containerNodes.add(ps.nodeKey(i,n)),i.id in o||(o[i.id]=Object.keys(o).length),-1===s.indexOf(h)&&s.push(h);const c=h.inboundLayers.length;for(let t=0;t<c;t++){const i=h.inputTensors[t],n=h.inboundLayers[t],r=h.nodeIndices[t],a=h.tensorIndices[t];u(i,e,s,n,r,a)}for(e.push(h);s.indexOf(h)>=0;)s.splice(s.indexOf(h),1);l.push(h)},h=[],p=[];for(const t of this.outputs)u(t,h,p);const d=l.slice().reverse();for(const t of d){s[t.id]=t,t.id in e||(e[t.id]=0);let r=e[t.id];const a=null==i[t.outboundLayer.id]?0:i[t.outboundLayer.id];r=Math.max(r,a),i[t.outboundLayer.id]=r,n[t.outboundLayer.id]=t.outboundLayer,e[t.id]=r;for(let i=0;i<t.inboundLayers.length;i++){const n=t.inboundLayers[i],a=t.nodeIndices[i],o=n.inboundNodes[a],l=null==e[o.id]?0:e[o.id];e[o.id]=Math.max(r+1,l),s[o.id]=o}}const g={};for(const t in e){const i=e[t];i in g||(g[i]=[]),g[i].push(s[t])}const f={};for(const t in i){const e=i[t];e in f||(f[e]=[]),f[e].push(n[t])}let m=Object.keys(f).map((t=>parseInt(t,10))).sort(k);this.layers=[];for(const t of m){const e=f[t];e.sort(((t,e)=>{const s=o[t.id],i=o[e.id];return s<i?-1:s>i?1:0}));for(const t of e)t instanceof ps&&this.internalContainerRefs.push(t),this.layers.push(t)}this.layersByDepth=f,m=Object.keys(g).map((t=>parseInt(t,10))).sort(k);const y=this.inputs.slice(),b=[];for(const t of m)for(const e of g[t]){const t=e.outboundLayer;if(null!=t){for(const s of e.inputTensors)if(-1===y.indexOf(s))throw new r(`Graph disconnected: cannot obtain value for tensor ${s} at layer "${t.name}". The following previous layers were accessed without issue: ${b}`);for(const t of e.outputTensors)y.push(t);b.push(t.name)}}this.nodesByDepth=g;const w=this.layers.map((t=>t.name));for(const t of w){const e=w.filter((e=>e===t)).length;if(1!==e)throw new r(`The name "${t}" is used ${e} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(w))}this.outboundNodes=[],this.inboundNodes=[],new Gt({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map((t=>null)),outputMasks:this.outputs.map((t=>null)),inputShapes:this.inputs.map((t=>t.shape)),outputShapes:this.outputs.map((t=>t.shape))}),this.built=!0,this._refCount=1}assertNotDisposed(){if(0===this._refCount)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const t={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount){for(const e of this.layers)t.numDisposedVariables+=e.dispose().numDisposedVariables;for(const e of this.internalContainerRefs)t.numDisposedVariables+=e.dispose().numDisposedVariables}return t.refCountAfterDispose=this._refCount,t}get trainable(){return this.trainable_}set trainable(t){this.layers.forEach((e=>{e._trainableWeights.forEach((e=>e.trainable=t))})),this.trainable_=t}get trainableWeights(){if(this._trainableWeights.length>0)throw new a("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let t=[];for(const e of this.layers)t=t.concat(e.trainableWeights);return t}get nonTrainableWeights(){const t=[];for(const e of this.layers)t.push(...e.nonTrainableWeights);if(!this.trainable){const e=[];for(const t of this.layers)e.push(...t.trainableWeights);return e.concat(t)}return t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(t,e=!0){const s={};let i=0;for(const t of this.layers)for(const e of t.weights){if(null!=s[e.originalName])throw new a(`Duplicate weight name: ${e.originalName}`);s[e.originalName]=e,i++}const n=[];for(const i in t){let r=i;if(null==s[i]){const t=i.split("/");r=t.slice(0,-2).concat([t[t.length-1]]).join("/")}if(null!=s[r])n.push([s[r],t[i]]);else if(e)throw new a(`Provided weight data has no target variable: ${i}`);delete s[r]}if(e){const t=[];for(const e in s)t.push(e);if(t.length>0)throw new a(`${t.length} of ${i} weights are not set: ${t}`)}Jt(n)}updatedConfig(){const t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion="tfjs-layers 3.20.0",e.backend="TensorFlow.js",e}toJSON(t,e=!0){const s=hs(this.updatedConfig());return e?JSON.stringify(s):s}call(t,s){return e.tidy((()=>{t=g(t);const e=new se;for(let s=0;s<this.inputs.length;++s)e.add(this.inputs[s],t[s]);return re(this.outputs,e,s)}))}computeMask(t,s){return e.tidy((()=>{let e;return t=g(t),e=null==s?h(null,t.length):g(s),this.runInternalGraph(t,e)[1]}))}computeOutputShape(t){const e=Bt(t);if(e.length!==this.inputLayers.length)throw new a(`Invalid inputShape argument ${t}: model has ${this.inputLayers.length} tensor inputs.`);const s={};for(let t=0;t<e.length;t++){const i=this.inputLayers[t],n=e[t];s[i.name+"_0_0"]=n}const i=Object.keys(this.nodesByDepth).map((t=>parseInt(t,10))).sort(k);if(i.length>1)for(const t of i){const e=this.nodesByDepth[t];for(const t of e){const e=t.outboundLayer;if(-1!==this.inputLayers.map((t=>t.id)).indexOf(e.id))continue;const i=[];for(let e=0;e<t.inboundLayers.length;e++){const n=t.inboundLayers[e],r=t.nodeIndices[e],a=t.tensorIndices[e],o=s[`${n.name}_${r}_${a}`];i.push(o)}const n=Bt(e.computeOutputShape(d(i))),r=e.inboundNodes.indexOf(t);for(let t=0;t<n.length;t++){s[`${e.name}_${r}_${t}`]=n[t]}}}const n=[],r=[];for(let t=0;t<this.outputLayers.length;t++){const e=this.outputLayers[t],s=this.outputLayersNodeIndices[t],i=this.outputLayersTensorIndices[t],n=`${e.name}_${s}_${i}`;r.push(n)}for(let t=0;t<r.length;t++){const e=r[t];c(e in s),n.push(s[e])}return d(n)}runInternalGraph(t,e){null==e&&(e=h(null,t.length));const s={};for(let i=0;i<this.inputs.length;++i){const n=this.inputs[i],r=t[i],a=e[i];s[n.id]=[r,a]}const i=Object.keys(this.nodesByDepth).map((t=>parseInt(t,10))).sort(k);for(const t of i){const e=this.nodesByDepth[t];for(const t of e){const e=t.outboundLayer,i=t.inputTensors,n=t.outputTensors,r=new Array;for(const t of i)t.id in s&&r.push(s[t.id]);if(r.length===i.length){let i,a,l,u,h={};if(null!=t.callArgs&&(h=t.callArgs),1===r.length){const[t,s]=r[0];null==h.mask&&(h.mask=s),l=g(e.call(t,h)),u=g(e.computeMask(t,s)),i=[t],a=[s]}else i=r.map((t=>t[0])),a=r.map((t=>t[1])),null==h.mask&&(h.mask=a),l=g(e.call(i,h)),u=g(e.computeMask(i,a));if(e.activityRegularizer)throw new o("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let t=0;t<n.length;++t){const e=n[t],i=l[t],r=u[t];s[e.id]=[i,r]}}}}const n=[],r=[],a=[];for(const t of this.outputs){c(t.id in s,`Could not compute output ${t.name} : ${t.id}`);const[e,i]=s[t.id];a.push(e.shape),n.push(e),r.push(i)}return[n,r,a]}buildNodeConversionMap(t){const e={};let s;for(const t of this.layers){s=t instanceof ps?1:0;for(let i=0;i<t.inboundNodes.length;i++){const n=ps.nodeKey(t,i);this.containerNodes.has(n)&&(e[n]=s,s+=1)}}return e}getLayer(t,e){if(null!=e){if(this.layers.length<=e)throw new a(`Was asked to retrieve layer at index ${e}, but model only has ${this.layers.length} layer(s).`);return this.layers[e]}if(null==t)throw new a("Provide either a layer name or layer index");for(const e of this.layers)if(e.name===t)return e;throw new a(`No such layer: ${t}`)}calculateLosses(){return e.tidy((()=>{const t=[];for(const e of this.layers)for(let s=0;s<e.inboundNodes.length;++s){const i=ps.nodeKey(e,s);this.containerNodes.has(i)&&t.push(...e.calculateLosses())}return t}))}getConfig(){const t={name:this.name},e=this.buildNodeConversionMap(this.layers),s=[];for(const t of this.layers){const i=t.getClassName(),n=t.getConfig(),r=[];for(let s=0;s<t.inboundNodes.length;s++){const i=t.inboundNodes[s],n=ps.nodeKey(t,s);let a={};if(this.containerNodes.has(n)){if(i.callArgs)try{JSON.stringify(i.callArgs),a=i.callArgs}catch(e){console.warn(`Layer ${t.name} was passed non-serializable keyword arguments: ${i.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),a={}}if(i.inboundLayers.length>0){const t=[];for(let s=0;s<i.inboundLayers.length;s++){const n=i.inboundLayers[s],r=i.nodeIndices[s],o=i.tensorIndices[s];let l=e[ps.nodeKey(n,r)];null==l&&(l=0),t.push([n.name,l,o,a])}r.push(t)}}}const a={};a.name=t.name,a.className=i,a.config=n,a.inboundNodes=r,s.push(a)}t.layers=s;const i=[];for(let t=0;t<this.inputLayers.length;t++){const s=this.inputLayers[t],n=this.inputLayersNodeIndices[t],r=ps.nodeKey(s,n);if(!this.containerNodes.has(r))continue;let a=e[r];null==a&&(a=0);const o=this.inputLayersTensorIndices[t];i.push([s.name,a,o])}t.inputLayers=i;const n=[];for(let t=0;t<this.outputLayers.length;t++){const s=this.outputLayers[t],i=this.outputLayersNodeIndices[t],r=ps.nodeKey(s,i);if(!this.containerNodes.has(r))continue;let a=e[r];null==a&&(a=0);const o=this.outputLayersTensorIndices[t];n.push([s.name,a,o])}return t.outputLayers=n,t}static fromConfig(t,e,s={},i=!1){const n={},r={};function o(t,e){t.name in r?r[t.name].push(e):r[t.name]=[e]}function l(t,e){const s=[];let i;for(const r of e){const a=r[0],l=r[1],u=r[2];if(i=null==r[3]?{}:r[3],!(a in n))return void o(t,e);const h=n[a];if(h.inboundNodes.length<=l)return void o(t,e);const c=h.inboundNodes[l];s.push(c.outputTensors[u])}s.length>0&&t.apply(d(s),i)}function u(t){const s=t.name,r=De(t,null!=e.customObjects?e.customObjects:{});r.setFastWeightInitDuringBuild(i),n[s]=r;t.inboundNodes.forEach((t=>{if(!(t instanceof Array))throw new a(`Corrupted configuration, expected array for nodeData: ${t}`);o(r,t)}))}const h=e.name,p=e.layers;for(const t of p)u(t);for(;!v(r);)for(const t of p){const e=n[t.name];if(e.name in r){const t=r[e.name];delete r[e.name];for(const s of t)l(e,s)}}const g=[],f=[],m=e.inputLayers;for(const t of m){const e=t[0],s=t[1],i=t[2];c(e in n);const r=n[e].inboundNodes[s].outputTensors;g.push(r[i])}const y=e.outputLayers;for(const t of y){const e=t[0],s=t[1],i=t[2];c(e in n);const r=n[e].inboundNodes[s].outputTensors;f.push(r[i])}return new t({inputs:g,outputs:f,name:h})}get stateful(){if(this._stateful)throw new a("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const t of this.layers)if(t.stateful)return!0;return!1}resetStates(){e.tidy((()=>{this.layers.forEach((t=>{t.stateful&&t.resetStates()}))}))}}function ds(t,e){return function(t,e,s){const i=e.length;if(null==t||Array.isArray(t)&&0===t.length)return e.map((t=>null));if(1===i)return Array.isArray(t)&&1===t.length?t:"object"==typeof t&&e[0]in t?[t[e[0]]]:[t];if(Array.isArray(t)){if(t.length!==i)throw new Error(`Provided ${s} is an array of ${t.length} element(s), but the model has ${i} outputs. Make sure a set of weights is provided for each model output.`);return t}if("object"==typeof t&&Object.keys(t).length>0&&"object"==typeof t[Object.keys(t)[0]]){const s=[];return e.forEach((e=>{e in t?s.push(t[e]):s.push(null)})),s}throw new Error(`The model has multiple (${i}) outputs, so ${s} must be either an array with ${i} elements or an object with ${e} keys. Provided ${s} not understood: ${JSON.stringify(t)}`)}(t,e,"classWeight")}async function gs(t,s,i,n){if(null!=s||null!=n)throw new Error("Support sampleWeight is not implemented yet");if(null!=i){const s=e.tidy((()=>{if(1===t.shape.length)return e.clone(t);if(2===t.shape.length){if(t.shape[1]>1){const s=1;return e.argMax(t,s)}if(1===t.shape[1])return e.reshape(t,[t.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${t.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}throw new Error(`Unexpected rank of target (y) tensor (${t.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)})),n=Array.from(await s.data());e.dispose(s);const r=[];return n.forEach((t=>{if(null==i[t])throw new Error(`classWeight must contain all classes in the training data. The class ${t} exists in the data but not in classWeight`);r.push(i[t])})),e.tensor1d(r,"float32")}return null}function fs(t,s){return e.mul(t,s)}function ms(t,e){let s,n;const r=e;s=r.xs,n=r.ys,i.util.assert(null!=s&&null!=n,(()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${e}`));const a=ys("input",t.inputNames,s),o=ys("output",t.outputNames,n),l=a[0].shape[0];i.util.assert(a.length===t.inputs.length,(()=>`LayersModel has ${t.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(t.inputNames)})`)),i.util.assert(o.length===t.outputs.length,(()=>`LayersModel has ${t.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(t.outputNames)})`));for(let e=0;e<a.length;e++)i.util.assert(a[e].shape[0]===l,(()=>`Batch size mismatch: input ${t.inputNames[e]} has ${a[e].shape[0]}; expected  ${l} based on input ${t.inputNames[0]}.`));for(let e=0;e<o.length;e++)i.util.assert(o[e].shape[0]===l,(()=>`Batch size mismatch: output ${t.outputNames[e]} has ${o[e].shape[0]}; expected  ${l} based on input ${t.inputNames[0]}.`));return{xs:a,ys:o}}function ys(t,e,s){if(s instanceof i.Tensor)return[s];if(Array.isArray(s))return i.util.assert(s.length===e.length,(()=>`Received an array of ${s.length} Tensors, but expected ${e.length} to match the ${t} keys ${e}.`)),s;{const i=[];for(const n of e){if(null==s[n])throw new a(`The feature data generated by the dataset lacks the required ${t} key '${n}'.`);i.push(s[n])}return i}}async function bs(t,e,s){const n=null!=s.batchesPerEpoch;if(i.util.assert(null!=t.optimizer,(()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).")),i.util.assert(null!=s,(()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.")),i.util.assert(null!=s.epochs&&s.epochs>0&&Number.isInteger(s.epochs),(()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${s.epochs}`)),i.util.assert(!n||s.batchesPerEpoch>0&&Number.isInteger(s.batchesPerEpoch),(()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${s.batchesPerEpoch}`)),i.util.assert(null==s.validationSplit,(()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.")),t.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");t.isTraining=!0;try{const r=null!=s.validationData;let a,l;if(r)if(ws(s.validationData))i.util.assert(null==s.validationBatches||s.validationBatches>0&&Number.isInteger(s.validationBatches),(()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${s.validationBatches}`));else{const t=function(t){if(3===t.length)throw new o("Validation with sample weights is not implemented yet.");return{xs:t[0],ys:t[1]}}(s.validationData);a=t.xs,l=t.ys}const u=t.makeTrainFunction(),h=t.getDedupedMetricsNames();let c;c=r?h.slice().concat(h.map((t=>"val_"+t))):h.slice();const p=Le(s.callbacks,s.yieldEvery),d=null==s.verbose?1:s.verbose,{callbackList:f,history:m}=Ee(p,d,s.epochs,null,null,function(t,e){let s=null;null!=e.batchesPerEpoch?s=e.batchesPerEpoch:Number.isFinite(t.size)&&(s=t.size);return s}(e,s),null,r,c);f.setModel(t),t.history=m,await f.onTrainBegin(),t.stopTraining_=!1;let y=null==s.initialEpoch?0:s.initialEpoch,b=await e.iterator();for(;y<s.epochs;){const o={};await f.onEpochBegin(y);let c=0,p=0;for(n||(b=await e.iterator());!n||c<s.batchesPerEpoch;){const e=await b.next();if(n&&e.done){console.warn(`You provided \`batchesPerEpoch\` as ${s.batchesPerEpoch}, but your dataset iterator ran out of data after ${c} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, `+s.batchesPerEpoch*s.epochs+" batches). You may need to use the repeat() function when building your dataset.");break}if(null!=e.value){const{xs:n,ys:r}=ms(t,e.value),a={};a.batch=p,a.size=n[0].shape[0],await f.onBatchBegin(p,a);const o=[];if(null!=s.classWeight){const e=ds(s.classWeight,t.outputNames);for(let t=0;t<e.length;++t)o.push(await gs(r[t],null,e[t]))}const l=n.concat(r).concat(o),d=u(l);i.dispose(l);for(let t=0;t<h.length;++t){const e=h[t],s=d[t];a[e]=s,i.keep(s)}await f.onBatchEnd(p,a),ve(a),p++,c++}if(n?c>=s.batchesPerEpoch:e.done){if(r){let e;e=ws(s.validationData)?g(await t.evaluateDataset(s.validationData,{batches:s.validationBatches})):g(t.evaluate(a,l,{batchSize:null==s.validationBatchSize?32:s.validationBatchSize,verbose:0}));for(let s=0;s<t.metricsNames.length;++s)o[`val_${t.metricsNames[s]}`]=e[s]}break}if(t.stopTraining_)break}if(await f.onEpochEnd(y,o),y++,t.stopTraining_)break}return await f.onTrainEnd(),await t.history.syncData(),t.history}finally{t.isTraining=!1}}function ws(t){return"function"==typeof t.iterator}function zs(t){i.util.assert(t>0&&Number.isInteger(t),(()=>`batchSize is required to be a positive integer, but got ${t}`))}function ks(t,e,s){return null==t?[null]:Array.isArray(t)?t.map((t=>st(t,e,s-e))):st(t,e,s-e)}function Ss(t,e){return i.tidy((()=>null==t?null:Array.isArray(t)?t.map((t=>Ss(t,e))):ht(t,"int32"===e.dtype?e:i.cast(e,"int32"))))}function vs(t,e){const s=[];let i=0,n=null;for(;i<t;)n=i+e,n>=t&&(n=t),s.push([i,n]),i=n;return s}async function Ns(t,s,n,r={}){if(t.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");let l,u,h,c,p,d,g,f,m;t.isTraining=!0;try{const y=null==r.batchSize?32:r.batchSize;zs(y);const b=!1,w=await t.standardizeUserData(s,n,r.sampleWeight,r.classWeight,b,y);l=w[0],u=w[1],m=w[2];let z,k=!1;if(null!=r.validationData&&r.validationData.length>0){if(k=!0,2!==r.validationData.length)throw 3===r.validationData.length?new o("validationData including sample weights is not supported yet."):new a(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${r.validationData} is invalid.`);p=r.validationData[0],d=r.validationData[1];const e=!0,s=await t.standardizeUserData(p,d,null,null,e,y);g=s[0],f=s[1],z=g.concat(f)}else if(null!=r.validationSplit&&r.validationSplit>0&&r.validationSplit<1){k=!0;const t=Math.floor(l[0].shape[0]*(1-r.validationSplit)),e=l[0].shape[0];g=ks(l,t,e),h=l,l=ks(l,0,t),f=ks(u,t,e),c=u,u=ks(u,0,t),z=g.concat(f)}else null!=r.validationSteps&&(k=!0);const S=l.concat(u).concat(m);t.checkTrainableWeightsConsistency();const v=t.makeTrainFunction(),N=t.getDedupedMetricsNames();let C,A;k?(t.makeTestFunction(),C=t.testFunction,A=N.slice().concat(N.map((t=>"val_"+t)))):(C=null,z=[],A=N.slice());const x=Le(r.callbacks,r.yieldEvery),I=await async function(t,s,n,r,l,u,h,c,p,d,g,f,m,y,b){null==l&&(l=32),null==u&&(u=1),null==g&&(g=!0),null==m&&(m=0);let w=!1;if(null!=p&&null!=d&&(w=!0),null!=b&&(w=!0,null==y))throw new a("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");const z=t.checkNumSamples(n,l,y,"steps_per_epoch");let k;null!=z&&(k=Y(0,z)),null==h&&(h=1);const{callbackList:S,history:v}=Ee(c,h,u,m,z,y,l,w,f);S.setModel(t),t.history=v,await S.onTrainBegin(),t.stopTraining_=!1;for(let a=m;a<u;++a){await S.onEpochBegin(a);const u={};if(null!=y)throw new o("stepsPerEpoch mode is not implemented yet.");{if("batch"===g)throw new o("batch shuffling is not implemneted yet");g&&e.util.shuffle(k);const a=e.tensor1d(k),h=vs(z,l);for(let e=0;e<h.length;++e){const o={};if(await S.onBatchBegin(e,o),i.tidy((()=>{const c=h[e][0],g=h[e][1],f=st(a,c,g-c);o.batch=e,o.size=g-c;const m=Ss(n,f),y=s(m);for(let t=0;t<r.length;++t){const e=r[t],s=y[t];o[e]=s,i.keep(s)}if(e===h.length-1&&w){const e=t.testLoop(p,d,l);for(let t=0;t<r.length;++t){const s=r[t],n=e[t];i.keep(n),u["val_"+s]=n}}})),await S.onBatchEnd(e,o),ve(o),t.stopTraining_)break}a.dispose()}if(await S.onEpochEnd(a,u),t.stopTraining_)break}return await S.onTrainEnd(),await t.history.syncData(),t.history}(t,v,S,N,y,r.epochs,r.verbose,x,C,z,r.shuffle,A,r.initialEpoch,null,null);return I}finally{t.isTraining=!1,As(l,s),As(u,n),As(h,s),As(c,n),As(g,p),As(f,d),null!=m&&i.dispose(m)}}function Cs(t){const s=[];t instanceof e.Tensor&&(t=[t]);for(let e=0;e<t.length;++e){const i=t[e];if(1===i.rank)s.push(et(i,1));else{if(0===i.rank)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");s.push(i)}}return s}function As(t,s){if(null==t)return;const i=[];if(s instanceof e.Tensor)i.push(s.id);else if(Array.isArray(s))s.forEach((t=>i.push(t.id)));else if(null!=s)for(const t in s){const e=s[t];i.push(e.id)}const n=[];if(t instanceof e.Tensor)-1===i.indexOf(t.id)&&n.push(t);else if(Array.isArray(t))t.forEach((t=>{-1===i.indexOf(t.id)&&n.push(t)}));else if(null!=t)for(const e in t){const s=t[e];-1===i.indexOf(s.id)&&n.push(s)}n.forEach((t=>{t.isDisposed||t.dispose()}))}function xs(t){return Array.isArray(t)}function Is(t){return!function(t){return t instanceof e.Tensor}(t)&&!xs(t)}function Ls(t,e,s,i=!0,n=""){if(null==e||0===e.length){if(null!=t){let e=!1;if(xs(t)&&t.length>0)e=!0;else if(Is(t)){for(const s in t)if(t.hasOwnProperty(s)){e=!0;break}}else e=!0;if(e)throw new a(`Error when checking model ${n} expected no data, but got ${t}`)}return[]}if(null==t)return e.map((t=>null));let r;if(Is(t)){r=[];for(const s of e){if(null==t[s])throw new a(`No data provided for "${s}". Need data for each key in: ${e}`);r.push(t[s])}}else if(xs(t)){if(t.length!==e.length)throw new a(`Error when checking model ${n}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${e.length} Tensor(s), but instead got the following list of Tensor(s): ${t}`);r=t}else{if(e.length>1)throw new a(`The model ${n} expects ${e.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${t.shape}`);r=[t]}if(r=Cs(r),null!=s)for(let t=0;t<e.length;++t){if(null==s[t])continue;const o=r[t];if(o.shape.length!==s[t].length)throw new a(`Error when checking ${n}: expected ${e[t]} to have ${s[t].length} dimension(s). but got array with shape ${o.shape}`);for(let e=0;e<s[t].length;++e){if(0===e&&!i)continue;const r=o.shape[e],l=s[t][e];if(null!=l&&l>=0&&r!==l)throw new a(`${n} expected a batch of elements where each example has shape [${s[t].slice(1,s[t].length)}] (i.e.,tensor shape [*,${s[t].slice(1,s[t].length)}]) but the ${n} received an input with ${o.shape[0]} examples, each with shape [${o.shape.slice(1,o.shape.length)}] (tensor shape [${o.shape}])`)}}return r}function Ts(t,e,s,i=!0,n=""){let r;if(Array.isArray(t)){if(t.length!==e.length)throw new a(`Error when checking model ${n}: the Array of Tensors that you are passing to your model is not the size the the model expected. 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Build the model first (e.g., by calling it on some test data).");ns(this,t,e,s)}compile(t){if(null==t.loss&&(t.loss=[]),this.loss=t.loss,"string"==typeof t.optimizer)this.optimizer_=function(t){const s={Adagrad:()=>e.train.adagrad(.01),Adadelta:()=>e.train.adadelta(1,.95,Q()),Adam:()=>e.train.adam(.001,.9,.999,Q()),Adamax:()=>e.train.adamax(.002,.9,.999,Q(),0),RMSProp:()=>e.train.rmsprop(.001,.9,0,Q()),SGD:()=>e.train.sgd(.01)};if(s.adagrad=s.Adagrad,s.adadelta=s.Adadelta,s.adam=s.Adam,s.adamax=s.Adamax,s.rmsprop=s.RMSProp,s.sgd=s.SGD,t in s)return s[t]();throw new a(`Unknown Optimizer ${t}`)}(t.optimizer),this.isOptimizerOwned=!0;else{if(!(t.optimizer instanceof e.Optimizer))throw new a("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=t.optimizer,this.isOptimizerOwned=!1}let s=[];if(Array.isArray(t.loss)||"string"==typeof t.loss||"function"==typeof t.loss)if(Array.isArray(t.loss)){if(t.loss.length!==this.outputs.length)throw new a(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${t.loss}.`);const e=t.loss;s=e.map((t=>Pe(t)))}else{const e=Pe(t.loss);this.outputs.forEach((t=>{s.push(e)}))}else{t.loss=t.loss;for(const e in t.loss)if(-1===this.outputNames.indexOf(e))throw new a(`Unknown entry in loss dictionary: "${e}". Only expected the following keys: ${this.outputNames}`);for(const e of this.outputNames)null==t.loss[e]&&console.warn(`Output "${e}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${e} during training`),s.push(Pe(t.loss[e]))}this.lossFunctions=s,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let t=0;t<this.outputs.length;++t){const e=this.internalOutputShapes[t],s=this.outputNames[t];this.feedOutputNames.push(s),this.feedOutputShapes.push(e),this.feedLossFns.push(this.lossFunctions[t])}const i=[];this.metrics=t.metrics,this.metricsNames=["loss"],this.metricsTensors=[],j("loss",(()=>{for(let t=0;t<this.outputs.length;++t){if(-1!==i.indexOf(t))continue;const e=this.lossFunctions[t];this.outputs.length>1&&(this.metricsTensors.push([e,t]),this.metricsNames.push(this.outputNames[t]+"_loss"))}}));const n=function(t,e){if(null==t||Array.isArray(t)&&0===t.length)return e.map((t=>[]));let s;if("string"==typeof t||"function"==typeof t)s=[t];else{if(!Array.isArray(t)&&"object"!=typeof t)throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${t}`);s=t}if(Array.isArray(s))return e.map((t=>s));{const t=[];for(const i of e){let e=s.hasOwnProperty(i)?s[i]:[];Array.isArray(e)||(e=[e]),t.push(e)}return t}}(t.metrics,this.outputNames),r=(t,e,s)=>{this.outputNames.length>1&&(e=this.outputNames[t]+"_"+e),this.metricsNames.push(e),this.metricsTensors.push([s,t])};j("metric",(()=>{for(let t=0;t<this.outputs.length;++t){if(-1!==i.indexOf(t))continue;(e=>{let s,i,n;for(const a of e){if("string"==typeof a&&-1!==["accuracy","acc","crossentropy","ce"].indexOf(a)){const e=this.internalOutputShapes[t];let r;1===e[e.length-1]||this.lossFunctions[t]===Be?-1!==["accuracy","acc"].indexOf(a)?i=je:-1!==["crossentropy","ce"].indexOf(a)&&(i=Ze):this.lossFunctions[t]===Me?-1!==["accuracy","acc"].indexOf(a)?i=Ke:-1!==["crossentropy","ce"].indexOf(a)&&(i=Ye):-1!==["accuracy","acc"].indexOf(a)?i=Ve:-1!==["crossentropy","ce"].indexOf(a)&&(i=Ge),-1!==["accuracy","acc"].indexOf(a)?r="acc":-1!==["crossentropy","ce"].indexOf(a)&&(r="ce"),n=i,s=""+r}else{const t=Qe(a);n=t,s=""+ts(a)}let e;j(s,(()=>{e=n})),r(t,s,e)}})(n[t])}})),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){null!=this.collectedTrainableWeights&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(t,e,s={}){const i=null==s.batchSize?32:s.batchSize;zs(i);const n=this.standardizeUserDataXY(t,e,!0,i);try{const r=n[0].concat(n[1]);this.makeTestFunction();const a=this.testFunction;return d(this.testLoop(a,r,i,s.verbose,s.steps))}finally{As(n[0],t),As(n[1],e)}}async evaluateDataset(t,s){return this.makeTestFunction(),async function(t,s,n){const r=null!=(n=n||{}).batches,a=t.testFunction;let l=[];if(n.verbose>0)throw new o("Verbose mode is not implemented yet.");i.util.assert(!r||n.batches>0&&Number.isInteger(n.batches),(()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`));const u="function"==typeof s.next?s:await s.iterator();let h=0,c=0;for(;!r||c<n.batches;){const s=await u.next();if(l=i.tidy((()=>{if(s.value){const{xs:n,ys:r}=ms(t,s.value),o=n.concat(r),u=i.tidy((()=>a(o)));if(i.dispose(o),0===c)for(let t=0;t<u.length;++t)l.push(e.scalar(0));const p=o[0].shape[0];for(let t=0;t<u.length;++t){const e=u[t],s=l[t];l[t]=i.tidy((()=>i.add(l[t],i.mul(p,e)))),c>0&&i.dispose(s)}i.dispose(u),h+=p,++c}return l})),s.done){r&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let t=0;t<l.length;++t){const e=l[t];l[t]=i.div(l[t],h),i.dispose(e)}return d(l)}(this,t,s)}checkNumSamples(t,e,s,i="steps"){let n;if(null!=s){if(n=null,null!=e)throw new a(`If ${i} is set, batchSize must be null or undefined.Got batchSize = ${e}`)}else{if(null==t)throw new a(`Either the input data should have a defined shape, or ${i} shoud be specified.`);n=Array.isArray(t)?t[0].shape[0]:t.shape[0]}return n}execute(t,s){if(Array.isArray(s)&&0===s.length)throw new a("`outputs` is an empty Array, which is not allowed.");const i=Array.isArray(s),n=i?s:[s],r=this.retrieveSymbolicTensors(n),o=new se;if(t instanceof e.Tensor&&(t=[t]),Array.isArray(t)){if(t.length!==this.inputs.length)throw new a(`The number of inputs provided (${t.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let e=0;e<this.inputs.length;++e)o.add(this.inputs[e],t[e])}else for(const e of this.inputs){const s=t[e.name];if(null==s)throw new a(`No value is provided for the model's input ${e.name}`);o.add(e,s)}const l=re(r,o);return i?l:l[0]}retrieveSymbolicTensors(t){const e=h(null,t.length);let s=t.length;for(const i of this.layers){const n=Array.isArray(i.output)?i.output:[i.output],r=n.map((t=>t.name));for(let i=0;i<t.length;++i){const a=r.indexOf(t[i]);if(-1!==a&&(e[i]=n[a],s--),0===s)break}if(0===s)break}if(s>0){const s=[];throw e.forEach(((e,i)=>{null==e&&s.push(t[i])})),new a(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return e}predictLoop(t,e=32,s=!1){return i.tidy((()=>{const n=this.checkNumSamples(t);if(s)throw new o("Verbose predictLoop() is not implemented yet.");const r=vs(n,e),a=this.outputs.map((t=>[]));for(let e=0;e<r.length;++e){i.tidy((()=>{const s=r[e][0],i=r[e][1],n=ks(t,s,i),a=[];if(Array.isArray(n))for(let t=0;t<n.length;++t)a.push({key:this.inputs[t],value:n[t]});else a.push({key:this.inputs[0],value:n});const o=new se(a);return re(this.outputs,o)})).forEach(((t,e)=>a[e].push(t)))}return d(a.map((t=>i.concat(t,0))))}))}predict(t,e={}){const s=Cs(t);Ts(s,this.inputNames,this.feedInputShapes,!1);try{const i=null==e.batchSize?32:e.batchSize;return zs(i),this.predictLoop(s,i)}finally{As(s,t)}}predictOnBatch(t){Ts(t,this.inputNames,this.feedInputShapes,!0);const e=(Array.isArray(t)?t[0]:t).shape[0];return this.predictLoop(t,e)}standardizeUserDataXY(t,s,i=!0,n){if(null==this.optimizer_)throw new r("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");const o=[];for(let t=0;t<this.feedOutputShapes.length;++t){const e=this.feedOutputShapes[t];this.feedLossFns[t]===Me?o.push(e.slice(0,e.length-1).concat([1])):o.push(e)}if(function(t,s,i){const n=S(t.map((t=>t.shape[0])));n.sort();const r=S(s.map((t=>t.shape[0])));if(r.sort(),n.length>1)throw new a(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map((t=>t.shape)))}`);if(r.length>1)throw new a(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(s.map((t=>t.shape)))}`);if(n.length>0&&r.length>0&&!e.util.arraysEqual(n,r))throw new a(`Input Tensors should have the same number of samples as target Tensors. Found ${n[0]} input sample(s) and ${r[0]} target sample(s).`)}(t=Ls(t,this.feedInputNames,this.feedInputShapes,!1,"input"),s=Ls(s,this.feedOutputNames,o,!1,"target")),function(t,e,s){const i=[Fe,Be,_e];for(let n=0;n<t.length;++n){const r=t[n],o=e[n],l=s[n];if(null!=o){if(o===_e&&1===r.shape[r.shape.length-1])throw new a(`You are passing a target array of shape ${r.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(-1!==i.indexOf(o)){const t=r.shape.slice(1),e=l.slice(1);for(let s=0;s<t.length;++s){const i=t[s],n=e[s];if(null!=n&&i!==n)throw new a(`A target Tensor with shape ${r.shape} was passed for an output of shape ${l}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}(s,this.feedLossFns,this.feedOutputShapes),this.stateful&&null!=n&&n>0&&t[0].shape[0]%n!=0)throw new a(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${n}. Found: ${t[0].shape[0]} sample(s).`);return[t,s]}async standardizeUserData(t,e,s,i,n=!0,r){const[a,o]=this.standardizeUserDataXY(t,e,n,r);if(null!=s)throw new Error("sample weight is not supported yet.");let l=null;if(null!=i){const t=ds(i,this.outputNames);l=[];for(let e=0;e<t.length;++e)l.push(await gs(o[e],null,t[e]))}return[a,o,l]}testLoop(t,s,n,r=0,a){return i.tidy((()=>{const l=this.checkNumSamples(s,n,a,"steps"),u=[];if(r>0)throw new o("Verbose mode is not implemented yet.");if(null!=a)throw new o("steps mode in testLoop() is not implemented yet");{const r=vs(l,n),a=e.tensor1d(Y(0,l));for(let n=0;n<r.length;++n){const o=r[n][0],l=r[n][1],h=st(a,o,l-o),c=Ss(s,h),p=t(c);if(0===n)for(let t=0;t<p.length;++t)u.push(e.scalar(0));for(let t=0;t<p.length;++t){const e=p[t];u[t]=i.add(u[t],i.mul(l-o,e))}}for(let t=0;t<u.length;++t)u[t]=i.div(u[t],l)}return u}))}getDedupedMetricsNames(){const t=this.metricsNames,e=[];for(let s=0;s<t.length;++s){const i=t[s];let n=i;if(p(t,i)>1){n+=`_${p(t.slice(0,s),i)}`}e.push(n)}return e}makeTrainFunction(){return t=>{const e=[],s=t.slice(0,this.inputs.length),n=t.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=t.slice(this.inputs.length+this.outputs.length,this.inputs.length+2*this.outputs.length),a=[],o=this.collectedTrainableWeights.map((t=>t.read()));return[this.optimizer_.minimize((()=>{const t=[];for(let e=0;e<this.inputs.length;++e)t.push({key:this.inputs[e],value:s[e]});const o=new se(t),l=re(this.outputs,o,{training:!0});let u;for(let t=0;t<this.lossFunctions.length;++t){let s=(0,this.lossFunctions[t])(n[t],l[t]);null!=r[t]&&(s=fs(s,r[t]));const a=i.mean(s);e.push(a),u=0===t?s:i.add(u,s)}for(let t=0;t<this.metricsTensors.length;++t){let s;if(this.outputs.length>1&&t<this.outputs.length)s=e[t];else{const e=this.metricsTensors[t][0],r=this.metricsTensors[t][1];s=i.mean(e(n[r],l[r]))}i.keep(s),a.push(s)}return u=i.mean(u),this.calculateLosses().forEach((t=>{u=i.add(u,t)})),u}),!0,o)].concat(a)}}makeTestFunction(){this.testFunction=t=>i.tidy((()=>{const e=[];let s;const n=t.slice(0,this.inputs.length),r=t.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let t=0;t<this.inputs.length;++t)a.push({key:this.inputs[t],value:n[t]});const o=new se(a),l=re(this.outputs,o);for(let t=0;t<this.lossFunctions.length;++t){const n=this.lossFunctions[t],a=i.mean(n(r[t],l[t]));s=0===t?a:i.add(s,a),e.push(s)}for(let t=0;t<this.metricsTensors.length;++t){const s=this.metricsTensors[t][0],n=this.metricsTensors[t][1],a=i.mean(s(r[n],l[n]));e.push(a)}return e}))}async fit(t,e,s={}){return Ns(this,t,e,s)}async fitDataset(t,e){return bs(this,t,e)}async trainOnBatch(t,e){const s=await this.standardizeUserData(t,e),n=s[0],r=s[1],a=this.makeTrainFunction()(n.concat(r)),o=[];for(const t of a){const e=await t.data();o.push(e[0])}return i.dispose(a),As(s[0],t),As(s[1],e),d(o)}getNamedWeights(t){const e=[],s=null!=t&&t.trainableOnly,i=s?this.trainableWeights:this.weights,n=this.getWeights(s);for(let t=0;t<i.length;++t)s&&!i[t].trainable||e.push({name:i[t].originalName,tensor:n[t]});return e}set stopTraining(t){this.stopTraining_=t}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(t){this.optimizer_!==t&&(this.optimizer_=t,this.isOptimizerOwned=!1)}dispose(){const t=super.dispose();if(0===t.refCountAfterDispose&&null!=this.optimizer&&this.isOptimizerOwned){const e=i.memory().numTensors;this.optimizer_.dispose(),t.numDisposedVariables+=e-i.memory().numTensors}return t}getLossIdentifiers(){let t;if("string"==typeof this.loss)t=f(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if("string"!=typeof t)throw new Error("Serialization of non-string loss is not supported.");t=this.loss.map((t=>f(t)))}else{const e=Object.keys(this.loss);t={};const s=this.loss;for(const i of e){if("string"!=typeof s[i])throw new Error("Serialization of non-string loss is not supported.");t[i]=f(s[i])}}return t}getMetricIdentifiers(){if("string"==typeof this.metrics||"function"==typeof this.metrics)return[f(ts(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map((t=>f(ts(t))));{const t={};for(const e in this.metrics)t[e]=f(ts(this.metrics[e]));return t}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(t){if(null!=t.weighted_metrics)throw new Error("Loading weight_metrics is not supported yet.");if(null!=t.loss_weights)throw new Error("Loading loss_weights is not supported yet.");if(null!=t.sample_weight_mode)throw new Error("Loading sample_weight_mode is not supported yet.");const e=De(us(t.optimizer_config));let s,i;if("string"==typeof t.loss)s=m(t.loss);else if(Array.isArray(t.loss))s=t.loss.map((t=>m(t)));else if(null!=t.loss){s={};for(const e in t.loss)s[e]=m(t.loss[e])}if(Array.isArray(t.metrics))i=t.metrics.map((t=>m(t)));else if(null!=t.metrics){i={};for(const e in t.metrics)i[e]=m(t.metrics[e])}this.compile({loss:s,metrics:i,optimizer:e})}async save(t,s){if("string"==typeof t){const s=e.io.getSaveHandlers(t);if(0===s.length)throw new a(`Cannot find any save handlers for URL '${t}'`);if(s.length>1)throw new a(`Found more than one (${s.length}) save handlers for URL '${t}'`);t=s[0]}if(null==t.save)throw new a("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const i=await e.io.encodeWeights(this.getNamedWeights(s)),n={modelTopology:this.toJSON(null,!1),format:"layers-model",generatedBy:"TensorFlow.js tfjs-layers v3.20.0",convertedBy:null};if(null!=s&&s.includeOptimizer&&null!=this.optimizer){n.trainingConfig=this.getTrainingConfig();const t="optimizer",{data:s,specs:r}=await e.io.encodeWeights(await this.optimizer.getWeights(),t);i.specs.push(...r),i.data=e.io.concatenateArrayBuffers([i.data,s])}if(null!=this.userDefinedMetadata){const t=!0;ss(this.userDefinedMetadata,this.name,t),n.userDefinedMetadata=this.userDefinedMetadata}return n.weightData=i.data,n.weightSpecs=i.specs,t.save(n)}setUserDefinedMetadata(t){ss(t,this.name),this.userDefinedMetadata=t}getUserDefinedMetadata(){return this.userDefinedMetadata}}Es.className="Model",e.serialization.registerClass(Es);class Ds extends Es{}async function $s(t,s){if(null==s&&(s={}),"string"==typeof t){const i=e.io.getLoadHandlers(t,s);if(0===i.length)i.push(e.io.browserHTTPRequest(t,s));else if(i.length>1)throw new a(`Found more than one (${i.length}) load handlers for URL '${t}'`);t=i[0]}return async function(t,s,i){null==i&&(i={});if(null==t.load)throw new a("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const n=await t.load();let r=n.modelTopology;null!=r.model_config&&(r=r.model_config);const o=null==i.strict||i.strict,l=null!=n.weightData&&null!=n.weightSpecs&&o,u=De(us(r),s,l),h=n.trainingConfig;null!=h&&u.loadTrainingConfig(h);null!=n.userDefinedMetadata&&u.setUserDefinedMetadata(n.userDefinedMetadata);if(null!=n.weightData){if(null==n.weightSpecs)throw new a("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:t,optimizerWeights:s}=function(t,s){const i=e.io.decodeWeights(t,s),n={},r=[];return s.forEach((t=>{"optimizer"===t.group?r.push({name:t.name,tensor:i[t.name]}):n[t.name]=i[t.name]})),{modelWeights:n,optimizerWeights:r}}(n.weightData,n.weightSpecs);u.loadWeights(t,o),null!=u.optimizer&&s.length>0&&await u.optimizer.setWeights(s),e.dispose(t),e.dispose(s.map((t=>t.tensor)))}return u}(t,void 0,s)}Ds.className="Functional",e.serialization.registerClass(Ds);class Fs extends Es{constructor(t){if(super({inputs:[],outputs:[]}),t=t||{},this.trainable=!0,this.built=!1,this.name=null!=t.name?t.name:D("sequential_"),null!=t.layers)for(const e of t.layers)this.add(e)}checkShape(t){if(t.inboundNodes[0].outputTensors[0].shape.some((t=>t<0)))throw new a(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){const e=t instanceof Fs||t instanceof Es;let s;if(e){if(s=t,1!==s.outputs.length)throw new a("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(1!==s.inputs.length)throw new a("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(0===this.outputs.length){if(0===t.inboundNodes.length){if(null==t.batchInputShape)throw new a("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const e=ee({batchShape:t.batchInputShape,dtype:t.dtype,name:t.name+"_input"});t.apply(e)}if(e)this.outputs=s.outputs,this.inputs=s.inputs;else{if(1!==t.inboundNodes.length)throw new a(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${t.name} which has ${t.inboundNodes.length} pre-existing inbound connections.`);if(1!==t.inboundNodes[0].outputTensors.length)throw new a("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[t.inboundNodes[0].outputTensors[0]],this.inputs=Qt(this.outputs[0])}this.inboundNodes=[],new Gt({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:h(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map((t=>t.shape)),outputShapes:this.outputs[0].shape})}else{const e=t.apply(this.outputs[0]);if(Array.isArray(e))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[e],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(t),this.built=!1}pop(){if(0===this.layers.length)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),0===this.layers.length)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const t=this.layers.length-1;this.layers[t].outboundNodes=[],this.outputs=[this.layers[t].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(t,e){return null==this.model&&this.build(),this.model.call(t,e)}build(t){if(Ut(t),0===this.inputs.length||0===this.outputs.length)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Es({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(t,e,s=console.log){this.built||this.build(),super.summary(t,e,s)}setWeights(t){null==this.model&&this.build(),this.model.setWeights(t)}evaluate(t,e,s={}){if(!this.built)throw new r("The model needs to be compiled before being used.");return this.model.evaluate(t,e,s)}async evaluateDataset(t,e){if(!this.built)throw new r("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return null==this.model&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return null==this.model&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return null==this.model?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,e,s={}){if(!this.built)throw new r("The model needs to be compiled before being used.");return this.model.fit(t,e,s)}async fitDataset(t,e){if(!this.built)throw new r("The model needs to be compiled before being used.");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,s,i={},n=!1){let r,l={};if(s instanceof Array){if(null==s[0].className||"Merge"===s[0].className)throw new a("Legacy serialization format not supported yet.");r=s}else e.util.assert(null!=s.layers,(()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.")),r=s.layers,delete s.layers,l=s;const u=new t(l);if(!(u instanceof Fs))throw new o(`Sequential.fromConfig called on non-Sequential input: ${u}`);for(const t of r){const e=De(t,void 0,n);n&&e.setFastWeightInitDuringBuild(!0),u.add(e)}return u}set stopTraining(t){if(null==this.model)throw new a("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(null==this.model)throw new a("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const t=[];for(const e of this.layers){const s={};s.className=e.getClassName(),s.config=e.getConfig(),t.push(s)}return{name:this.name,layers:t}}}function Os(t){return ee(t)}Fs.className="Sequential",e.serialization.registerClass(Fs);class Rs extends e.serialization.Serializable{getConfig(){return{}}}class _s extends Rs{apply(t,e=1){return function(t,e=1){if(1!==e)throw new o(`Support for alpha values other than 1 (${e}) is not implemented yet.`);return i.elu(t)}(t,e)}}_s.className="elu",e.serialization.registerClass(_s);class Ms extends Rs{apply(t){return i.selu(t)}}Ms.className="selu",e.serialization.registerClass(Ms);class Bs extends Rs{apply(t){return i.relu(t)}}Bs.className="relu",e.serialization.registerClass(Bs);class Ws extends Rs{apply(t){return e.tidy((()=>i.minimum(6,i.relu(t))))}}Ws.className="relu6",e.serialization.registerClass(Ws);class Us extends Rs{apply(t){return t}}Us.className="linear",e.serialization.registerClass(Us);class Ps extends Rs{apply(t){return i.sigmoid(t)}}Ps.className="sigmoid",e.serialization.registerClass(Ps);class js extends Rs{apply(t){return function(t){return e.tidy((()=>{const e=i.add(.5,i.mul(.2,t));return i.clipByValue(e,0,1)}))}(t)}}js.className="hardSigmoid",e.serialization.registerClass(js);class Vs extends Rs{apply(t){return i.softplus(t)}}Vs.className="softplus",e.serialization.registerClass(Vs);class qs extends Rs{apply(t){return function(t){return e.tidy((()=>i.div(t,i.add(i.abs(t),1))))}(t)}}qs.className="softsign",e.serialization.registerClass(qs);class Js extends Rs{apply(t){return i.tanh(t)}}Js.className="tanh",e.serialization.registerClass(Js);class Hs extends Rs{apply(t,e=-1){return i.softmax(t,e)}}Hs.className="softmax",e.serialization.registerClass(Hs);class Zs extends Rs{apply(t,e=-1){return i.logSoftmax(t,e)}}Zs.className="logSoftmax",e.serialization.registerClass(Zs);class Ks extends Rs{apply(t,s=1){return e.tidy((()=>i.mul(i.sigmoid(i.mul(t,s)),t)))}}Ks.className="swish",e.serialization.registerClass(Ks);class Gs extends Rs{apply(t){return e.tidy((()=>i.mul(t,i.tanh(i.softplus(t)))))}}function Ys(t){return t.getClassName()}function Xs(t,s={}){return z(t,e.serialization.SerializationMap.getMap().classNameMap,s,"activation")}function Qs(t){if(null==t){const t={className:"linear",config:{}};return Xs(t)}if("string"==typeof t){const e={};return e.className=t,e.config={},Xs(e)}return t instanceof Rs?t:Xs(t)}function ti(t){if(null!=t&&"object"!=typeof t)throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${t}`)}Gs.className="mish",e.serialization.registerClass(Gs);class ei extends e.serialization.Serializable{}class si extends ei{constructor(t){super(),ti(t),this.l1=null==t||null==t.l1?.01:t.l1,this.l2=null==t||null==t.l2?.01:t.l2,this.hasL1=0!==this.l1,this.hasL2=0!==this.l2}apply(t){return e.tidy((()=>{let s=e.zeros([1]);return this.hasL1&&(s=e.add(s,e.sum(i.mul(this.l1,e.abs(t))))),this.hasL2&&(s=e.add(s,e.sum(i.mul(this.l2,ct(t))))),i.reshape(s,[])}))}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}}si.className="L1L2",e.serialization.registerClass(si);const ii={l1l2:"L1L2"};function ni(t){return b(t)}function ri(t,s={}){return z(t,e.serialization.SerializationMap.getMap().classNameMap,s,"regularizer")}function ai(t){if(null==t)return null;if("string"==typeof t){return ri({className:t in ii?ii[t]:t,config:{}})}return t instanceof ei?t:ri(t)}class oi extends Xt{constructor(t){super(null==t?{}:t),this.supportsMasking=!0,null!=t&&(this.maxValue=t.maxValue)}call(t,s){t=Wt(t);let i=e.relu(t);return null!=this.maxValue&&(i=e.clipByValue(i,0,this.maxValue)),i}computeOutputShape(t){return t}getConfig(){const t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}}oi.className="ReLU",e.serialization.registerClass(oi);class li extends Xt{constructor(t){super(null==t?{}:t),this.DEFAULT_ALPHA=.3,null==t&&(t={}),this.alpha=null==t.alpha?this.DEFAULT_ALPHA:t.alpha}call(t,s){const i=Wt(t);return e.leakyRelu(i,this.alpha)}computeOutputShape(t){return t}getConfig(){const t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}}li.className="LeakyReLU",e.serialization.registerClass(li);class ui extends Xt{constructor(t){if(super(null==t?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",null==t&&(t={}),this.supportsMasking=!0,this.alphaInitializer=_t(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ai(t.alphaRegularizer),this.alphaConstraint=be(t.alphaConstraint),null==t.sharedAxes)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else{if("number"!=typeof t.sharedAxes)throw new a(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`);this.sharedAxes=[t.sharedAxes]}}build(t){const e=(t=Ut(t)).slice(1);if(null!=this.sharedAxes)for(const t of this.sharedAxes)e[t-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const s={};if(null!=this.sharedAxes)for(let e=1;e<t.length;++e)s[e]=t[e];this.inputSpec=[new Ht({ndim:t.length,axes:s})],this.built=!0}call(t,s){return t=Wt(t),e.prelu(t,this.alpha.read())}getConfig(){const t={alphaInitializer:Rt(this.alphaInitializer),alphaRegularizer:ni(this.alphaRegularizer),alphaConstraint:me(this.alphaConstraint),sharedAxes:this.sharedAxes},e=super.getConfig();return Object.assign(t,e),t}}ui.className="PReLU",e.serialization.registerClass(ui);class hi extends Xt{constructor(t){if(super(null==t?{}:t),this.DEFAULT_ALPHA=1,null==t&&(t={}),null!=t.alpha&&t.alpha!==this.DEFAULT_ALPHA)throw new o(`Non-default alpha value (${t.alpha}) is not supported by the ELU layer yet.`);this.alpha=null==t.alpha?this.DEFAULT_ALPHA:t.alpha}call(t,s){const i=Wt(t);return e.elu(i)}computeOutputShape(t){return t}getConfig(){const t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}}hi.className="ELU",e.serialization.registerClass(hi);class ci extends Xt{constructor(t){super(null==t?{}:t),this.DEFAULT_THETA=1,null==t&&(t={}),this.theta=null==t.theta?this.DEFAULT_THETA:t.theta}call(t,s){const i=Wt(t);return e.mul(i,e.cast(e.greater(i,this.theta),"float32"))}computeOutputShape(t){return t}getConfig(){const t={theta:this.theta},e=super.getConfig();return Object.assign(t,e),t}}ci.className="ThresholdedReLU",e.serialization.registerClass(ci);class pi extends Xt{constructor(t){super(null==t?{}:t),this.DEFAULT_AXIS=1,null==t&&(t={}),this.softmax=(new Hs).apply,this.axis=null==t.axis?this.DEFAULT_AXIS:t.axis}call(t,e){const s=Wt(t);return this.softmax(s,this.axis)}computeOutputShape(t){return t}getConfig(){const t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}}function di(t,e,s){if("number"==typeof t)return h(t,e);if(t.length!==e)throw new a(`The ${s} argument must be an integer or tuple of ${e} integers. Received: ${t.length} elements.`);for(let n=0;n<e;++n){const r=t[n];if((i=r)!==parseInt(i.toString(),10))throw new a(`The ${s} argument must be an integer or tuple of ${e} integers. Received: ${JSON.stringify(t)} including a non-integer number ${r}`)}return t;var i}function gi(t,e,s,i,n=1){if(null==t)return t;let r;return r="same"===s?t:t-(e+(e-1)*(n-1))+1,Math.floor((r+i-1)/i)}function fi(t,e,s,i){if(null==t)return null;if("valid"===i)t=t*e+G([s-e,0]);else{if("same"!==i)throw new a(`Unsupport padding mode: ${i}.`);t*=e}return t}function mi(t,s){return e.tidy((()=>(B(s),"channelsFirst"===s?i.transpose(t,[0,2,3,1]):t)))}function yi(t,s){return e.tidy((()=>(B(s),"channelsFirst"===s?i.transpose(t,[0,2,3,4,1]):t)))}function bi(t,s,n,r=[1,1],l="valid",u,h,c=null){return e.tidy((()=>{if(null==u&&(u="channelsLast"),B(u),3!==t.rank&&4!==t.rank)throw new a(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${t.rank}.`);if(3!==s.rank&&4!==s.rank)throw new a(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${t.rank}.`);let e=mi(t,u);if("causal"===l)throw new o("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return e=i.fused.conv2d({x:e,filter:s,strides:r,pad:"same"===l?"same":"valid",dilations:h,dataFormat:"NHWC",bias:n,activation:c}),"channelsFirst"===u&&(e=i.transpose(e,[0,3,1,2])),e}))}pi.className="Softmax",e.serialization.registerClass(pi);class wi extends Xt{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",wi.verifyArgs(e),this.rank=t,A(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new o(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=di(e.kernelSize,t,"kernelSize"),this.strides=di(null==e.strides?1:e.strides,t,"strides"),this.padding=null==e.padding?"valid":e.padding,W(this.padding),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,B(this.dataFormat),this.activation=Qs(e.activation),this.useBias=null==e.useBias||e.useBias,this.biasInitializer=_t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=be(e.biasConstraint),this.biasRegularizer=ai(e.biasRegularizer),this.activityRegularizer=ai(e.activityRegularizer),this.dilationRate=di(null==e.dilationRate?1:e.dilationRate,t,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new a(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(2===this.rank){if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate];else if(2!==this.dilationRate.length)throw new a(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(3===this.rank)if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(3!==this.dilationRate.length)throw new a(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}static verifyArgs(t){if(c("kernelSize"in t,"required key 'kernelSize' not in config"),"number"!=typeof t.kernelSize&&!C(t.kernelSize,"number",1,3))throw new a(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){const t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ys(this.activation),useBias:this.useBias,biasInitializer:Rt(this.biasInitializer),biasRegularizer:ni(this.biasRegularizer),activityRegularizer:ni(this.activityRegularizer),biasConstraint:me(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}class zi extends wi{constructor(t,e){super(t,e),this.kernel=null,zi.verifyArgs(e),this.filters=e.filters,A(this.filters,"filters"),this.kernelInitializer=_t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=be(e.kernelConstraint),this.kernelRegularizer=ai(e.kernelRegularizer)}build(t){t=Ut(t);const e="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[e])throw new a(`The channel dimension of the input should be defined. Found ${t[e]}`);const s=t[e],i=this.kernelSize.concat([s,this.filters]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:s}}],this.built=!0}call(t,s){return e.tidy((()=>{let s;t=Wt(t);const n=null==this.bias?null:this.bias.read(),r=I(this.activation.getClassName());if(null!=r&&2===this.rank)s=bi(t,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(1===this.rank)s=function(t,s,n,r=1,l="valid",u,h=1){return e.tidy((()=>{if(null==u&&(u="channelsLast"),B(u),3!==t.shape.length)throw new a(`The input of a conv1dWithBias operation should be 3, but is ${t.shape.length} instead.`);if(3!==s.shape.length)throw new a(`The kernel for a conv1dWithBias operation should be 3, but is ${s.shape.length} instead`);if(null!=n&&1!==n.shape.length)throw new a(`The bias for a conv1dWithBias operation should be 1, but is ${s.shape.length} instead`);if("channelsFirst"===u&&(t=i.transpose(t,[0,2,1])),"causal"===l)throw new o("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let e=i.conv1d(t,s,r,"same"===l?"same":"valid","NWC",h);return null!=n&&(e=dt(e,n)),e}))}(t,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)s=bi(t,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new o("convolutions greater than 3D are not implemented yet.");s=function(t,s,n,r=[1,1,1],l="valid",u,h){return e.tidy((()=>{if(null==u&&(u="channelsLast"),B(u),4!==t.rank&&5!==t.rank)throw new a(`conv3dWithBias expects input to be of rank 4 or 5, but received ${t.rank}.`);if(4!==s.rank&&5!==s.rank)throw new a(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${t.rank}.`);let e=yi(t,u);if("causal"===l)throw new o("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return e=i.conv3d(e,s,r,"same"===l?"same":"valid","NDHWC",h),null!=n&&(e=dt(e,n)),"channelsFirst"===u&&(e=i.transpose(e,[0,4,1,2,3])),e}))}(t,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(s=this.activation.apply(s))}return s}))}computeOutputShape(t){t=Ut(t);const e=[],s="channelsLast"===this.dataFormat?t.slice(1,t.length-1):t.slice(2);for(let t=0;t<s.length;++t){const i=gi(s[t],this.kernelSize[t],this.padding,this.strides[t],"number"==typeof this.dilationRate?this.dilationRate:this.dilationRate[t]);e.push(i)}let i=[t[0]];return"channelsLast"===this.dataFormat?(i=i.concat(e),i.push(this.filters)):(i.push(this.filters),i=i.concat(e)),i}getConfig(){const t={filters:this.filters,kernelInitializer:Rt(this.kernelInitializer),kernelRegularizer:ni(this.kernelRegularizer),kernelConstraint:me(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||"number"!=typeof t.filters||t.filters<1)throw new a(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}}class ki extends zi{constructor(t){super(2,t),ki.verifyArgs(t)}getConfig(){const t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if("number"!=typeof t.kernelSize&&!C(t.kernelSize,"number",1,2))throw new a(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}}ki.className="Conv2D",e.serialization.registerClass(ki);class Si extends zi{constructor(t){super(3,t),Si.verifyArgs(t)}getConfig(){const t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if("number"!=typeof t.kernelSize&&(!Array.isArray(t.kernelSize)||1!==t.kernelSize.length&&3!==t.kernelSize.length))throw new a(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}}Si.className="Conv3D",e.serialization.registerClass(Si);class vi extends ki{constructor(t){if(super(t),this.inputSpec=[new Ht({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new a(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(4!==(t=Ut(t)).length)throw new a("Input should have rank 4; Received input shape: "+JSON.stringify(t));const e="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[e])throw new a("The channel dimension of the inputs should be defined. Found `None`.");const s=t[e],i=this.kernelSize.concat([this.filters,s]);this.kernel=this.addWeight("kernel",i,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ht({ndim:4,axes:{[e]:s}})],this.built=!0}call(t,e){return i.tidy((()=>{let e=Wt(t);if(4!==e.shape.length)throw new a(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${e.shape.length}`);const s=e.shape,n=s[0];let r,o;"channelsFirst"===this.dataFormat?(r=2,o=3):(r=1,o=2);const l=s[r],u=s[o],h=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],g=[n,fi(l,p,h,this.padding),fi(u,d,c,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(e=i.transpose(e,[0,2,3,1]));let f=i.conv2dTranspose(e,this.kernel.read(),g,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(f=i.transpose(f,[0,3,1,2])),null!=this.bias&&(f=dt(f,this.bias.read(),this.dataFormat)),null!=this.activation&&(f=this.activation.apply(f)),f}))}computeOutputShape(t){const e=(t=Ut(t)).slice();let s,i,n;"channelsFirst"===this.dataFormat?(s=1,i=2,n=3):(s=3,i=1,n=2);const r=this.kernelSize[0],a=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return e[s]=this.filters,e[i]=fi(e[i],o,r,this.padding),e[n]=fi(e[n],l,a,this.padding),e}getConfig(){const t=super.getConfig();return delete t.dilationRate,t}}vi.className="Conv2DTranspose",e.serialization.registerClass(vi);class Ni extends Si{constructor(t){if(super(t),this.inputSpec=[new Ht({ndim:5})],"same"!==this.padding&&"valid"!==this.padding)throw new a(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(5!==(t=Ut(t)).length)throw new a("Input should have rank 5; Received input shape: "+JSON.stringify(t));const e="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[e])throw new a("The channel dimension of the inputs should be defined. Found `None`.");const s=t[e],i=this.kernelSize.concat([this.filters,s]);this.kernel=this.addWeight("kernel",i,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ht({ndim:5,axes:{[e]:s}})],this.built=!0}call(t,e){return i.tidy((()=>{let e=Wt(t);if(5!==e.shape.length)throw new a(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${e.shape.length}`);const s=e.shape,n=s[0];let r,o,l;"channelsFirst"===this.dataFormat?(l=2,r=3,o=4):(l=1,r=2,o=3);const u=s[l],h=s[r],c=s[o],p=this.kernelSize[0],d=this.kernelSize[1],g=this.kernelSize[2],f=this.strides[0],m=this.strides[1],y=this.strides[2],b=[n,fi(u,f,p,this.padding),fi(h,m,d,this.padding),fi(c,y,g,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(e=i.transpose(e,[0,2,3,4,1]));let w=i.conv3dTranspose(e,this.kernel.read(),b,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(w=i.transpose(w,[0,4,1,2,3])),null!==this.bias&&(w=dt(w,this.bias.read(),this.dataFormat)),null!==this.activation&&(w=this.activation.apply(w)),w}))}computeOutputShape(t){const e=(t=Ut(t)).slice();let s,i,n,r;"channelsFirst"===this.dataFormat?(s=1,i=2,n=3,r=4):(s=4,i=1,n=2,r=3);const a=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],h=this.strides[1],c=this.strides[2];return e[s]=this.filters,e[i]=fi(e[i],u,a,this.padding),e[n]=fi(e[n],h,o,this.padding),e[r]=fi(e[r],c,l,this.padding),e}getConfig(){const t=super.getConfig();return delete t.dilationRate,t}}Ni.className="Conv3DTranspose",e.serialization.registerClass(Ni);class Ci extends zi{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,null==e.filters)throw new a("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=e.kernelInitializer||null!=e.kernelRegularizer||null!=e.kernelConstraint)throw new a("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=e.padding&&"same"!==e.padding&&"valid"!==e.padding)throw new a(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=_t(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ai(e.depthwiseRegularizer),this.depthwiseConstraint=be(e.depthwiseConstraint),this.pointwiseInitializer=_t(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ai(e.pointwiseRegularizer),this.pointwiseConstraint=be(e.pointwiseConstraint)}build(t){if((t=Ut(t)).length<this.rank+2)throw new a(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);const e="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[e]||t[e]<0)throw new a(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);const s=t[e],i=this.kernelSize.concat([s,this.depthMultiplier]),n=[];for(let t=0;t<this.rank;++t)n.push(1);n.push(s*this.depthMultiplier,this.filters);const r=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",i,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,r,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",n,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,r,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,r,this.biasConstraint):this.bias=null,this.inputSpec=[new Ht({ndim:this.rank+2,axes:{[e]:s}})],this.built=!0}call(t,s){return e.tidy((()=>{let e;if(t=Wt(t),1===this.rank)throw new o("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(t=i.transpose(t,[0,2,3,1])),e=i.separableConv2d(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(e=dt(e,this.bias.read(),this.dataFormat)),null!=this.activation&&(e=this.activation.apply(e)),"channelsFirst"===this.dataFormat&&(e=i.transpose(e,[0,3,1,2])),e}))}getConfig(){const t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Rt(this.depthwiseInitializer),t.pointwiseInitializer=Rt(this.pointwiseInitializer),t.depthwiseRegularizer=ni(this.depthwiseRegularizer),t.pointwiseRegularizer=ni(this.pointwiseRegularizer),t.depthwiseConstraint=me(this.depthwiseConstraint),t.pointwiseConstraint=me(this.pointwiseConstraint),t}}Ci.className="SeparableConv";class Ai extends Ci{constructor(t){super(2,t)}}Ai.className="SeparableConv2D",e.serialization.registerClass(Ai);class xi extends zi{constructor(t){super(1,t),xi.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){const t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if("number"!=typeof t.kernelSize&&!C(t.kernelSize,"number",1,1))throw new a(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}}xi.className="Conv1D",e.serialization.registerClass(xi);class Ii extends Xt{constructor(t){super(t),"number"==typeof t.cropping?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:"number"==typeof t.cropping[0]?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=void 0===t.dataFormat?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return"channelsFirst"===this.dataFormat?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,s){return e.tidy((()=>{if(t=Wt(t),"channelsLast"===this.dataFormat){const e=nt(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return nt(e,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{const e=nt(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return nt(e,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}}))}getConfig(){const t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}Ii.className="Cropping2D",e.serialization.registerClass(Ii);class Li extends Xt{constructor(t){var e;super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=null==t.size?this.DEFAULT_SIZE:t.size,this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,B(this.dataFormat),this.interpolation=null==t.interpolation?"nearest":t.interpolation,e=this.interpolation,N(F,"InterpolationFormat",e)}computeOutputShape(t){if("channelsFirst"===this.dataFormat){const e=null==t[2]?null:this.size[0]*t[2],s=null==t[3]?null:this.size[1]*t[3];return[t[0],t[1],e,s]}{const e=null==t[1]?null:this.size[0]*t[1],s=null==t[2]?null:this.size[1]*t[2];return[t[0],e,s,t[3]]}}call(t,e){return i.tidy((()=>{let e=Wt(t);const s=e.shape;if("channelsFirst"===this.dataFormat){e=i.transpose(e,[0,2,3,1]);const t=this.size[0]*s[2],n=this.size[1]*s[3],r="nearest"===this.interpolation?i.image.resizeNearestNeighbor(e,[t,n]):i.image.resizeBilinear(e,[t,n]);return i.transpose(r,[0,3,1,2])}{const t=this.size[0]*s[1],n=this.size[1]*s[2];return"nearest"===this.interpolation?i.image.resizeNearestNeighbor(e,[t,n]):i.image.resizeBilinear(e,[t,n])}}))}getConfig(){const t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}}Li.className="UpSampling2D",e.serialization.registerClass(Li);class Ti extends wi{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=_t(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=be(t.depthwiseConstraint),this.depthwiseRegularizer=ai(t.depthwiseRegularizer)}build(t){if((t=Ut(t)).length<4)throw new a(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);const e="channelsFirst"===this.dataFormat?1:3;if(null==t[e]||t[e]<0)throw new a(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);const s=t[e],i=[this.kernelSize[0],this.kernelSize[1],s,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",i,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[s*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,s){return e.tidy((()=>{let s=function(t,s,n=[1,1],r="valid",o,l){return e.tidy((()=>{null==o&&(o="channelsLast"),B(o);let e=mi(t,o);if(4!==t.rank)throw new a(`Input for depthwiseConv2d is required to be 4-D, but is instead ${t.rank}-D`);if(4!==s.rank)throw new a(`depthwiseKernel is required to be 4-D, but is instead ${s.rank}-D`);return e=i.depthwiseConv2d(e,s,n,"same"===r?"same":"valid","NHWC",l),"channelsFirst"===o&&(e=i.transpose(e,[0,3,1,2])),e}))}(t=Wt(t),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(s=dt(s,this.bias.read(),this.dataFormat)),null!=this.activation&&(s=this.activation.apply(s)),s}))}computeOutputShape(t){t=Ut(t);const e="channelsFirst"===this.dataFormat?t[2]:t[1],s="channelsFirst"===this.dataFormat?t[3]:t[2],i="channelsFirst"===this.dataFormat?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,n=gi(e,this.kernelSize[0],this.padding,this.strides[0]),r=gi(s,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[t[0],i,n,r]:[t[0],n,r,i]}getConfig(){const t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Rt(this.depthwiseInitializer),t.depthwiseRegularizer=ni(this.depthwiseRegularizer),t.depthwiseConstraint=me(this.depthwiseRegularizer),t}}function Ei(t,e,s,i){if(Array.isArray(t)){if(null!=e||null!=s)throw new a("When inputs is an array, neither initialState or constants should be provided");null!=i&&(s=t.slice(t.length-i,t.length),t=t.slice(0,t.length-i)),t.length>1&&(e=t.slice(1,t.length)),t=t[0]}function n(t){return null==t||Array.isArray(t)?t:[t]}return{inputs:t,initialState:e=n(e),constants:s=n(s)}}function Di(t,e,s,n=!1,r,l,u=!1,h=!1){return i.tidy((()=>{const c=e.shape.length;if(c<3)throw new a(`Input should be at least 3D, but is ${c}D.`);const p=[1,0].concat(Y(2,c));if(e=i.transpose(e,p),null!=l)throw new o("The rnn() functoin of the deeplearn.js backend does not support constants yet.");u&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=r&&((r=i.cast(i.cast(r,"bool"),"float32")).rank===c-1&&(r=i.expandDims(r,-1)),r=i.transpose(r,p)),n&&(e=i.reverse(e,0),null!=r&&(r=i.reverse(r,0)));const d=[];let g,f=s;const m=e.shape[0],y=i.unstack(e);let b,w;null!=r&&(b=i.unstack(r));for(let e=0;e<m;++e){const s=y[e],n=i.tidy((()=>t(s,f)));if(null==r)g=n[0],f=n[1];else{const t=i.tidy((()=>{const t=b[e],s=i.sub(i.onesLike(t),t);return{output:i.add(i.mul(n[0],t),i.mul(f[0],s)),newStates:f.map(((e,r)=>i.add(i.mul(n[1][r],t),i.mul(e,s))))}}));g=t.output,f=t.newStates}h&&d.push(g)}if(h){const t=1;w=i.stack(d,t)}return[g,w,f]}))}Ti.className="DepthwiseConv2D",e.serialization.registerClass(Ti);class $i extends Xt{constructor(t){let e;if(super(t),null==t.cell)throw new a("cell property is missing for the constructor of RNN.");if(e=Array.isArray(t.cell)?new Ui({cells:t.cell}):t.cell,null==e.stateSize)throw new a("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=null!=t.returnSequences&&t.returnSequences,this.returnState=null!=t.returnState&&t.returnState,this.goBackwards=null!=t.goBackwards&&t.goBackwards,this._stateful=null!=t.stateful&&t.stateful,this.unroll=null!=t.unroll&&t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ht({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(null==this.states_){return Y(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map((t=>null))}return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Mt(t)&&(t=t[0]);let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);const s=e[0];let i;if(i=this.returnSequences?[t[0],t[1],s]:[t[0],s],this.returnState){const s=[];for(const i of e)s.push([t[0],i]);return[i].concat(s)}return i}computeMask(t,e){return i.tidy((()=>{Array.isArray(e)&&(e=e[0]);const t=this.returnSequences?e:null;if(this.returnState){const e=this.states.map((t=>null));return[t].concat(e)}return t}))}get states(){if(null==this.states_){const t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let s=0;s<t;++s)e.push(null);return e}return this.states_}set states(t){this.states_=t}build(t){if(null!=this.numConstants)throw new o("Constants support is not implemented in RNN yet.");Mt(t)&&(t=t[0]);const s=this.stateful?t[0]:null,i=t.slice(2);this.inputSpec[0]=new Ht({shape:[s,null,...i]});const n=[t[0]].concat(t.slice(2));let r;if(this.cell.build(n),r=Array.isArray(this.cell.stateSize)?this.cell.stateSize:[this.cell.stateSize],null!=this.stateSpec){if(!e.util.arraysEqual(this.stateSpec.map((t=>t.shape[t.shape.length-1])),r))throw new a(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map((t=>new Ht({shape:[null,t]})));this.stateful&&this.resetStates()}resetStates(t,s=!1){e.tidy((()=>{if(!this.stateful)throw new n("Cannot call resetStates() on an RNN Layer that is not stateful.");const r=this.inputSpec[0].shape[0];if(null==r)throw new a("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==this.states_)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((t=>i.zeros([r,t]))):this.states_=[i.zeros([r,this.cell.stateSize])];else if(null==t)i.dispose(this.states_),null!=this.keptStates&&(i.dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((t=>i.zeros([r,t]))):this.states_[0]=i.zeros([r,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new a(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);!0===s?this.keptStates.push(this.states_.slice()):i.dispose(this.states_);for(let s=0;s<this.states_.length;++s){const i=t[s],n=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[r,n];if(!e.util.arraysEqual(i.shape,o))throw new a(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map((t=>i.keep(t.clone())))}))}apply(t,e){let s=null==e?null:e.initialState,i=null==e?null:e.constants;null==e&&(e={});const n=Ei(t,s,i,this.numConstants);t=n.inputs,s=n.initialState,i=n.constants;let r=[],a=[];if(null!=s){e.initialState=s,r=r.concat(s),this.stateSpec=[];for(const t of s)this.stateSpec.push(new Ht({shape:t.shape}));a=a.concat(this.stateSpec)}null!=i&&(e.constants=i,r=r.concat(i),this.numConstants=i.length);if(r[0]instanceof Zt){const s=[t].concat(r),i=this.inputSpec.concat(a),n=this.inputSpec;this.inputSpec=i;const o=super.apply(s,e);return this.inputSpec=n,o}return super.apply(t,e)}call(t,s){return e.tidy((()=>{const e=null==s?null:s.mask,i=null==s?null:s.training;let n=null==s?null:s.initialState;t=Wt(t),null==n&&(n=this.stateful?this.states_:this.getInitialState(t));const r=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(n.length!==r)throw new a(`RNN Layer has ${r} state(s) but was passed ${n.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const o={training:i},l=Di(((t,e)=>{const s=this.cell.call([t].concat(e),o);return[s[0],s.slice(1)]}),t,n,this.goBackwards,e,null,this.unroll,this.returnSequences),u=l[0],h=l[1],c=l[2];this.stateful&&this.resetStates(c,i);const p=this.returnSequences?h:u;return this.returnState?[p].concat(c):p}))}getInitialState(t){return e.tidy((()=>{let e=i.zeros(t.shape);return e=i.sum(e,[1,2]),e=et(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((t=>t>1?ot(e,[1,t]):e)):this.cell.stateSize>1?[ot(e,[1,this.cell.stateSize])]:[e]}))}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){const t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(e.numConstants=this.numConstants);const s=this.cell.getConfig();return this.getClassName()===$i.className&&(e.cell={className:this.cell.getClassName(),config:s}),Object.assign({},s,t,e)}static fromConfig(t,e,s={}){const i=De(e.cell,s);return new t(Object.assign(e,{cell:i}))}}$i.className="RNN",e.serialization.registerClass($i);class Fi extends Xt{}class Oi extends Fi{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,A(this.units,"units"),this.activation=Qs(null==t.activation?this.DEFAULT_ACTIVATION:t.activation),this.useBias=null==t.useBias||t.useBias,this.kernelInitializer=_t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=_t(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=_t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ai(t.kernelRegularizer),this.recurrentRegularizer=ai(t.recurrentRegularizer),this.biasRegularizer=ai(t.biasRegularizer),this.kernelConstraint=be(t.kernelConstraint),this.recurrentConstraint=be(t.recurrentConstraint),this.biasConstraint=be(t.biasConstraint),this.dropout=K([1,G([0,null==t.dropout?0:t.dropout])]),this.recurrentDropout=K([1,G([0,null==t.recurrentDropout?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Ut(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,s){return e.tidy((()=>{if(2!==t.length)throw new a(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let e=t[1];t=t[0];const n=null!=s.training&&s.training;let r;0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=Pi({ones:()=>i.onesLike(t),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=Pi({ones:()=>i.onesLike(e),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));const o=this.dropoutMask,l=this.recurrentDropoutMask;r=ut(null!=o?i.mul(t,o):t,this.kernel.read()),null!=this.bias&&(r=dt(r,this.bias.read())),null!=l&&(e=i.mul(e,l));let u=i.add(r,ut(e,this.recurrentKernel.read()));return null!=this.activation&&(u=this.activation.apply(u)),[u,u]}))}getConfig(){const t=super.getConfig(),e={units:this.units,activation:Ys(this.activation),useBias:this.useBias,kernelInitializer:Rt(this.kernelInitializer),recurrentInitializer:Rt(this.recurrentInitializer),biasInitializer:Rt(this.biasInitializer),kernelRegularizer:ni(this.kernelRegularizer),recurrentRegularizer:ni(this.recurrentRegularizer),biasRegularizer:ni(this.biasRegularizer),activityRegularizer:ni(this.activityRegularizer),kernelConstraint:me(this.kernelConstraint),recurrentConstraint:me(this.recurrentConstraint),biasConstraint:me(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},t,e)}}Oi.className="SimpleRNNCell",e.serialization.registerClass(Oi);class Ri extends $i{constructor(t){t.cell=new Oi(t),super(t)}call(t,s){return e.tidy((()=>{null!=this.cell.dropoutMask&&(i.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(i.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const e=null==s?null:s.mask,n=null==s?null:s.training,r=null==s?null:s.initialState;return super.call(t,{mask:e,training:n,initialState:r})}))}static fromConfig(t,e){return new t(e)}}Ri.className="SimpleRNN",e.serialization.registerClass(Ri);class _i extends Fi{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new a("GRUCell does not support reset_after parameter set to true.");this.units=t.units,A(this.units,"units"),this.activation=Qs(void 0===t.activation?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Qs(void 0===t.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=null==t.useBias||t.useBias,this.kernelInitializer=_t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=_t(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=_t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ai(t.kernelRegularizer),this.recurrentRegularizer=ai(t.recurrentRegularizer),this.biasRegularizer=ai(t.biasRegularizer),this.kernelConstraint=be(t.kernelConstraint),this.recurrentConstraint=be(t.recurrentConstraint),this.biasConstraint=be(t.biasConstraint),this.dropout=K([1,G([0,null==t.dropout?0:t.dropout])]),this.recurrentDropout=K([1,G([0,null==t.recurrentDropout?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){const e=(t=Ut(t))[t.length-1];this.kernel=this.addWeight("kernel",[e,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,s){return e.tidy((()=>{if(2!==t.length)throw new a(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);const e=null!=s.training&&s.training;let n=t[1];t=t[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=Pi({ones:()=>i.onesLike(t),rate:this.dropout,training:e,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=Pi({ones:()=>i.onesLike(n),rate:this.recurrentDropout,training:e,count:3,dropoutFunc:this.dropoutFunc}));const r=this.dropoutMask,o=this.recurrentDropoutMask;let l,u,h;0<this.dropout&&this.dropout<1&&(t=i.mul(t,r[0]));let c=ut(t,this.kernel.read());this.useBias&&(c=dt(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=i.mul(n,o[0]));const p=this.recurrentKernel.read(),[d,g]=i.split(p,[2*this.units,this.units],p.rank-1),f=ut(n,d),[m,y,b]=i.split(c,3,c.rank-1),[w,z]=i.split(f,2,f.rank-1);l=this.recurrentActivation.apply(i.add(m,w)),u=this.recurrentActivation.apply(i.add(y,z));const k=ut(i.mul(u,n),g);h=this.activation.apply(i.add(b,k));const S=i.add(i.mul(l,n),i.mul(i.add(1,i.neg(l)),h));return[S,S]}))}getConfig(){const t=super.getConfig(),e={units:this.units,activation:Ys(this.activation),recurrentActivation:Ys(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Rt(this.kernelInitializer),recurrentInitializer:Rt(this.recurrentInitializer),biasInitializer:Rt(this.biasInitializer),kernelRegularizer:ni(this.kernelRegularizer),recurrentRegularizer:ni(this.recurrentRegularizer),biasRegularizer:ni(this.biasRegularizer),activityRegularizer:ni(this.activityRegularizer),kernelConstraint:me(this.kernelConstraint),recurrentConstraint:me(this.recurrentConstraint),biasConstraint:me(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},t,e)}}_i.className="GRUCell",e.serialization.registerClass(_i);class Mi extends $i{constructor(t){0===t.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new _i(t),super(t)}call(t,s){return e.tidy((()=>{null!=this.cell.dropoutMask&&(i.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(i.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const e=null==s?null:s.mask,n=null==s?null:s.training,r=null==s?null:s.initialState;return super.call(t,{mask:e,training:n,initialState:r})}))}static fromConfig(t,e){return 0===e.implmentation&&(e.implementation=1),new t(e)}}Mi.className="GRU",e.serialization.registerClass(Mi);class Bi extends Fi{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,A(this.units,"units"),this.activation=Qs(void 0===t.activation?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Qs(void 0===t.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=null==t.useBias||t.useBias,this.kernelInitializer=_t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=_t(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=_t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=ai(t.kernelRegularizer),this.recurrentRegularizer=ai(t.recurrentRegularizer),this.biasRegularizer=ai(t.biasRegularizer),this.kernelConstraint=be(t.kernelConstraint),this.recurrentConstraint=be(t.recurrentConstraint),this.biasConstraint=be(t.biasConstraint),this.dropout=K([1,G([0,null==t.dropout?0:t.dropout])]),this.recurrentDropout=K([1,G([0,null==t.recurrentDropout?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;const s=(t=Ut(t))[t.length-1];let i;if(this.kernel=this.addWeight("kernel",[s,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){const t=this.biasInitializer,s=this.units;i=new((e=class extends bt{apply(e,i){const n=t.apply([s]),r=(new zt).apply([s]),a=t.apply([2*s]);return at(at(n,r),a)}}).className="CustomInit",e)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,i,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,s){return e.tidy((()=>{const e=null!=s.training&&s.training;if(3!==t.length)throw new a(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=t[1];const r=t[2];t=t[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=Pi({ones:()=>i.onesLike(t),rate:this.dropout,training:e,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=Pi({ones:()=>i.onesLike(n),rate:this.recurrentDropout,training:e,count:4,dropoutFunc:this.dropoutFunc}));const o=this.dropoutMask,l=this.recurrentDropoutMask;let u,h,c,p;0<this.dropout&&this.dropout<1&&(t=i.mul(t,o[0]));let d=ut(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=i.mul(n,l[0])),d=i.add(d,ut(n,this.recurrentKernel.read())),this.useBias&&(d=dt(d,this.bias.read()));const[g,f,m,y]=i.split(d,4,d.rank-1);u=this.recurrentActivation.apply(g),h=this.recurrentActivation.apply(f),c=i.add(i.mul(h,r),i.mul(u,this.activation.apply(m))),p=this.recurrentActivation.apply(y);const b=i.mul(p,this.activation.apply(c));return[b,b,c]}))}getConfig(){const t=super.getConfig(),e={units:this.units,activation:Ys(this.activation),recurrentActivation:Ys(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Rt(this.kernelInitializer),recurrentInitializer:Rt(this.recurrentInitializer),biasInitializer:Rt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ni(this.kernelRegularizer),recurrentRegularizer:ni(this.recurrentRegularizer),biasRegularizer:ni(this.biasRegularizer),activityRegularizer:ni(this.activityRegularizer),kernelConstraint:me(this.kernelConstraint),recurrentConstraint:me(this.recurrentConstraint),biasConstraint:me(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},t,e)}}Bi.className="LSTMCell",e.serialization.registerClass(Bi);class Wi extends $i{constructor(t){0===t.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Bi(t),super(t)}call(t,s){return e.tidy((()=>{null!=this.cell.dropoutMask&&(i.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(i.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const e=null==s?null:s.mask,n=null==s?null:s.training,r=null==s?null:s.initialState;return super.call(t,{mask:e,training:n,initialState:r})}))}static fromConfig(t,e){return 0===e.implmentation&&(e.implementation=1),new t(e)}}Wi.className="LSTM",e.serialization.registerClass(Wi);class Ui extends Fi{constructor(t){super(t),this.cells=t.cells}get stateSize(){const t=[];for(const e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,s){return e.tidy((()=>{let e=t.slice(1);const i=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?i.push(e.splice(0,t.stateSize.length)):i.push(e.splice(0,1));i.reverse();const n=[];let r;for(let a=0;a<this.cells.length;++a){const o=this.cells[a];e=i[a],r=0===a?[t[0]].concat(e):[r[0]].concat(e),r=o.call(r,s),n.push(r.slice(1))}e=[];for(const t of n.slice().reverse())e.push(...t);return[r[0]].concat(e)}))}build(t){let e;Mt(t)&&(t=t[0]),this.cells.forEach(((s,i)=>{j(`RNNCell_${i}`,(()=>{s.build(t),e=Array.isArray(s.stateSize)?s.stateSize[0]:s.stateSize,t=[t[0],e]}))})),this.built=!0}getConfig(){const t=super.getConfig(),e={cells:this.cells.map((t=>({className:t.getClassName(),config:t.getConfig()})))};return Object.assign({},t,e)}static fromConfig(t,e,s={}){const i=[];for(const t of e.cells)i.push(De(t,s));return new t({cells:i})}get trainableWeights(){if(!this.trainable)return[];const t=[];for(const e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){const t=[];for(const e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e.concat(t)}return t}getWeights(){const t=[];for(const e of this.cells)t.push(...e.weights);return qt(t)}setWeights(t){const e=[];for(const s of this.cells){const i=s.weights.length,n=t.splice(i);for(let t=0;t<s.weights.length;++t)e.push([s.weights[t],n[t]])}Jt(e)}}function Pi(t){const{ones:e,rate:s,training:n=!1,count:r=1,dropoutFunc:a}=t,o=()=>null!=a?a(e(),s):gt(e(),s),l=()=>ft(o,e,n);if(!r||r<=1)return i.keep(l().clone());return Array(r).fill(void 0).map(l).map((t=>i.keep(t.clone())))}Ui.className="StackedRNNCells",e.serialization.registerClass(Ui);var ji=function(t,e){var s={};for(var i in t)Object.prototype.hasOwnProperty.call(t,i)&&e.indexOf(i)<0&&(s[i]=t[i]);if(null!=t&&"function"==typeof Object.getOwnPropertySymbols){var n=0;for(i=Object.getOwnPropertySymbols(t);n<i.length;n++)e.indexOf(i[n])<0&&Object.prototype.propertyIsEnumerable.call(t,i[n])&&(s[i[n]]=t[i[n]])}return s};class Vi extends $i{constructor(t){if(t.unroll)throw new o("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new o("It is not possible at the moment to stack convolutional cells.");super(t),this.inputSpec=[new Ht({ndim:5})]}call(t,e){return i.tidy((()=>{if(null!=this.cell.dropoutMask&&(i.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(i.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new a("ConvRNN2D cell does not support constants");const s=null==e?null:e.mask,n=null==e?null:e.training,r=null==e?null:e.initialState;return super.call(t,{mask:s,training:n,initialState:r})}))}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return i.tidy((()=>{const{stateSize:e}=this.cell,s=t.shape,n=this.computeSingleOutputShape(s),r=[n[0],...n.slice(2)],a=i.zeros(r);return Array.isArray(e)?Array(e.length).fill(a):[a]}))}resetStates(t,s=!1){i.tidy((()=>{if(!this.stateful)throw new n("Cannot call resetStates() on an RNN Layer that is not stateful.");const r=this.inputSpec[0].shape,o=this.computeSingleOutputShape(r),l=[o[0],...o.slice(2)];if(null==r[0])throw new a("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==this.getStates())Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>i.zeros(l))):this.states_=[i.zeros(l)];else if(null==t)i.dispose(this.states_),null!=this.keptStates&&(i.dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>i.zeros(l))):this.states_[0]=i.zeros(l);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new a(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);s?this.keptStates.push(this.states_.slice()):i.dispose(this.states_);for(let s=0;s<this.states_.length;++s){const i=t[s],n=l;if(!e.util.arraysEqual(i.shape,n))throw new a(`State ${s} is incompatible with layer ${this.name}: expected shape=${n}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map((t=>i.keep(t.clone())))}))}computeSingleOutputShape(t){const{dataFormat:e,filters:s,kernelSize:i,padding:n,strides:r,dilationRate:a}=this.cell,o="channelsFirst"===e,l=t[o?3:2],u=t[o?4:3],h=gi(l,i[0],n,r[0],a[0]),c=gi(u,i[1],n,r[1],a[1]);return[...t.slice(0,2),...o?[s,h,c]:[h,c,s]]}}Vi.className="ConvRNN2D";class qi extends Bi{constructor(t){const{filters:e,kernelSize:s,strides:i,padding:n,dataFormat:r,dilationRate:a}=t;super(Object.assign({},t,{units:e})),this.filters=e,A(this.filters,"filters"),this.kernelSize=di(s,2,"kernelSize"),this.kernelSize.forEach((t=>A(t,"kernelSize"))),this.strides=di(i||1,2,"strides"),this.strides.forEach((t=>A(t,"strides"))),this.padding=n||"valid",W(this.padding),this.dataFormat=r||"channelsLast",B(this.dataFormat),this.dilationRate=di(a||1,2,"dilationRate"),this.dilationRate.forEach((t=>A(t,"dilationRate")))}build(t){var e;t=Ut(t);const s="channelsFirst"===this.dataFormat?1:t.length-1;if(null==t[s])throw new a(`The channel dimension of the input should be defined. Found ${t[s]}`);const n=t[s],r=this.kernelSize.concat([n,4*this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const o=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let t;if(this.unitForgetBias){const s=this.biasInitializer,n=this.filters;t=new((e=class extends bt{apply(t,e){return rt([s.apply([n]),i.ones([n]),s.apply([2*n])])}}).className="CustomInit",e)}else t=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,t,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return i.tidy((()=>{if(3!==t.length)throw new a(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);const s=e.training||!1,n=t[0],r=t[1],o=t[2];0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=Pi({ones:()=>i.onesLike(n),rate:this.dropout,training:s,count:4,dropoutFunc:this.dropoutFunc}));const l=this.dropoutMask,u=(t,e,s)=>e&&e[s]?i.mul(e[s],t):t;let h=u(n,l,0),c=u(n,l,1),p=u(n,l,2),d=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=Pi({ones:()=>i.onesLike(r),rate:this.recurrentDropout,training:s,count:4,dropoutFunc:this.dropoutFunc}));const g=this.recurrentDropoutMask;let f=u(r,g,0),m=u(r,g,1),y=u(r,g,2),b=u(r,g,3);const[w,z,k,S]=i.split(this.kernel.read(),4,3),[v,N,C,A]=this.useBias?i.split(this.bias.read(),4):[null,null,null,null];h=this.inputConv(h,w,v,this.padding),c=this.inputConv(c,z,N,this.padding),p=this.inputConv(p,k,C,this.padding),d=this.inputConv(d,S,A,this.padding);const[x,I,L,T]=i.split(this.recurrentKernel.read(),4,3);f=this.recurrentConv(f,x),m=this.recurrentConv(m,I),y=this.recurrentConv(y,L),b=this.recurrentConv(b,T);const E=this.recurrentActivation.apply(i.add(h,f)),D=this.recurrentActivation.apply(i.add(c,m)),$=i.add(i.mul(D,o),i.mul(E,this.activation.apply(i.add(p,y)))),F=i.mul(this.recurrentActivation.apply(i.add(d,b)),this.activation.apply($));return[F,F,$]}))}getConfig(){const t=super.getConfig(),e=ji(t,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},e,s)}inputConv(t,e,s,n){const r=i.conv2d(t,e,this.strides,n||"valid","channelsFirst"===this.dataFormat?"NCHW":"NHWC",this.dilationRate);return s?dt(r,s,this.dataFormat):r}recurrentConv(t,e){return i.conv2d(t,e,1,"same","channelsFirst"===this.dataFormat?"NCHW":"NHWC")}}qi.className="ConvLSTM2DCell",i.serialization.registerClass(qi);class Ji extends Vi{constructor(t){const e=new qi(t);super(Object.assign({},t,{cell:e}))}static fromConfig(t,e){return new t(e)}}Ji.className="ConvLSTM2D",i.serialization.registerClass(Ji);class Hi extends Xt{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(null==this.noiseShape)return this.noiseShape;const e=t.shape,s=[];for(let t=0;t<this.noiseShape.length;++t)s.push(null==this.noiseShape[t]?e[t]:this.noiseShape[t]);return s}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);const e=Wt(t);if(0<this.rate&&this.rate<1){const t=null!=s.training&&s.training,i=this.getNoiseShape(e);return ft((()=>gt(e,this.rate,i,this.seed)),(()=>e),t)}return t}))}getConfig(){const t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}}Hi.className="Dropout",e.serialization.registerClass(Hi);class Zi extends Hi{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){const e=t.shape;return[e[0],1,e[2]]}}Zi.className="SpatialDropout1D",e.serialization.registerClass(Zi);class Ki extends Xt{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",null==t.batchInputShape&&null==t.inputShape&&null!=t.inputDim){let e=null;null!=t.batchSize&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,A(this.units,"units"),this.activation=Qs(t.activation),null!=t.useBias&&(this.useBias=t.useBias),this.kernelInitializer=_t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=_t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=be(t.kernelConstraint),this.biasConstraint=be(t.biasConstraint),this.kernelRegularizer=ai(t.kernelRegularizer),this.biasRegularizer=ai(t.biasRegularizer),this.activityRegularizer=ai(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){const e=(t=Ut(t))[t.length-1];null==this.kernel&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){const e=(t=Ut(t)).slice();return e[e.length-1]=this.units,e}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);const e=Wt(t),i=I(this.activation.getClassName());let n;return null!=i?n=ut(e,this.kernel.read(),i,this.bias?this.bias.read():null):(n=ut(e,this.kernel.read()),null!=this.bias&&(n=dt(n,this.bias.read())),null!=this.activation&&(n=this.activation.apply(n))),n}))}getConfig(){const t={units:this.units,activation:Ys(this.activation),useBias:this.useBias,kernelInitializer:Rt(this.kernelInitializer),biasInitializer:Rt(this.biasInitializer),kernelRegularizer:ni(this.kernelRegularizer),biasRegularizer:ni(this.biasRegularizer),activityRegularizer:ni(this.activityRegularizer),kernelConstraint:me(this.kernelConstraint),biasConstraint:me(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}Ki.className="Dense",e.serialization.registerClass(Ki);class Gi extends Xt{constructor(t){super(t=t||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Ut(t);for(const e of t.slice(1))if(null==e)throw new a(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Z(t,1)]}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);let n=Wt(t);if("channelsFirst"===this.dataFormat&&n.rank>1){const t=[0];for(let e=2;e<n.rank;++e)t.push(e);t.push(1),n=e.transpose(n,t)}return function(t){if(t.rank<=1)throw new a(`batchFlatten requires a minimum rank of 2. Got rank: ${t.rank}.`);const e=[t.shape[0],Z(t.shape,1)];return i.reshape(t,e)}(n)}))}getConfig(){const t={};null!=this.dataFormat&&(t.dataFormat=this.dataFormat);const e=super.getConfig();return Object.assign(t,e),t}}Gi.className="Flatten",e.serialization.registerClass(Gi);class Yi extends Xt{constructor(t){super(t),this.supportsMasking=!0,this.activation=Qs(t.activation)}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);const e=Wt(t);return this.activation.apply(e)}))}getConfig(){const t={activation:Ys(this.activation)},e=super.getConfig();return Object.assign(t,e),t}}Yi.className="Activation",e.serialization.registerClass(Yi);class Xi extends Xt{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,s){return e.tidy((()=>{return t=Wt(t),s=t,i=this.n,e.tidy((()=>{if(2!==s.shape.length)throw new a(`repeat() expects a rank-2 tensor, but received a rank-${s.shape.length} tensor.`);return ot(et(s,1),[1,i,1])}));var s,i}))}getConfig(){const t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}}Xi.className="RepeatVector",e.serialization.registerClass(Xi);class Qi extends Xt{constructor(t){super(t),this.targetShape=t.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(t){return t<0||null==t}fixUnknownDimension(t,e){const s="Total size of new array must be unchanged.",i=e.slice();let n=1,r=null;for(let t=0;t<i.length;++t){const e=i[t];if(this.isUnknown(e)){if(null!==r)throw new a("Can only specifiy one unknown dimension.");r=t}else n*=e}const o=Z(t);if(null!==r){if(0===n||o%n!=0)throw new a(s);i[r]=o/n}else if(o!==n)throw new a(s);return i}computeOutputShape(t){let e=!1;for(let s=0;s<t.length;++s)if(this.isUnknown(t[s])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);const i=Wt(t),n=i.shape,r=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return e.reshape(i,r)}))}getConfig(){const t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}}Qi.className="Reshape",e.serialization.registerClass(Qi);class tn extends Xt{constructor(t){if(super(t),null==t.dims)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);const s=Y(1,t.dims.length+1);if(!e.util.arraysEqual(t.dims.slice().sort(),s))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ht({ndim:this.dims.length+1})]}computeOutputShape(t){const e=(t=Ut(t)).slice();return this.dims.forEach(((s,i)=>{e[i+1]=t[s]})),e}call(t,s){return e.transpose(Wt(t),this.dimsIncludingBatch)}getConfig(){const t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}}tn.className="Permute",e.serialization.registerClass(tn);class en extends Xt{constructor(t){super(null==t?{}:t),this.supportsMasking=!0,this.maskValue=null!=t?null==t.maskValue?0:t.maskValue:0}computeOutputShape(t){return t}getConfig(){const t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,s){const i=Wt(t);return e.any(e.notEqual(i,this.maskValue),-1)}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);const i=Wt(t),n=e.any(e.notEqual(i,this.maskValue),-1,!0);return e.mul(i,e.cast(n,i.dtype))}))}}en.className="Masking",e.serialization.registerClass(en);class sn extends Xt{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==t.batchInputShape&&null==t.inputShape){let e=null;null!=t.batchSize&&(e=t.batchSize),null==t.inputLength?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(g(t.inputLength))}this.inputDim=t.inputDim,A(this.inputDim,"inputDim"),this.outputDim=t.outputDim,A(this.outputDim,"outputDim"),this.embeddingsInitializer=_t(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ai(t.embeddingsRegularizer),this.activityRegularizer=ai(t.activityRegularizer),this.embeddingsConstraint=be(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,s){return e.tidy((()=>this.maskZero?(t=Wt(t),e.notEqual(t,e.zerosLike(t))):null))}computeOutputShape(t){if(t=Ut(t),null==this.inputLength)return[...t,this.outputDim];const e=g(this.inputLength);if(e.length!==t.length-1)throw new a(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let s=0;for(let i=0;i<e.length;++i){const n=e[i],r=t[i+1];if(null!=n&&null!=r&&n!==r)throw new a(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);null==n&&(e[s]=r),s++}}return[t[0],...e,this.outputDim]}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);let i=Wt(t);"int32"!==i.dtype&&(i=tt(i,"int32"));const n=ht(this.embeddings.read(),e.reshape(i,[i.size]));return e.reshape(n,Ut(this.computeOutputShape(i.shape)))}))}getConfig(){const t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Rt(this.embeddingsInitializer),embeddingsRegularizer:ni(this.embeddingsRegularizer),activityRegularizer:ni(this.activityRegularizer),embeddingsConstraint:me(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}}sn.className="Embedding",e.serialization.registerClass(sn);class nn extends Xt{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new o}computeElementwiseOpOutputShape(t,e){if(null==t||null==e)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(0===e.length)return t;const s=t.slice(0,t.length-e.length);for(let i=0;i<e.length;++i){const n=t[t.length-e.length+i],r=e[i];if(null==n||null==r||n<0||r<0)s.push(null);else if(1===n)s.push(r);else if(1===r)s.push(n);else{if(n!==r)throw new a("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));s.push(n)}}return s}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Ut(t)]),t.length<2)throw new a(`A merge layer should be called on an Array of at least 2 inputs. Got ${t.length} input(s).`);let e=[];for(const s of t)null!=s&&null!==s[0]&&e.push(s[0]);if(e=S(e),e.length>1)throw new a(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let s=null==t[0]?null:t[0].slice(1);for(let e=1;e<t.length;++e){const i=null==t[e]?null:t[e].slice(1);s=this.computeElementwiseOpOutputShape(s,i)}const i=t.map((t=>t.length));-1===t.indexOf(null)&&1===S(i).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,s){return e.tidy((()=>{if(this.reshapeRequired){const e=[],s=t.map((t=>t.rank));if(-1===s.indexOf(null)){const i=G(s);for(let s of t){const t=s.rank;for(let e=0;e<i-t;++e)s=et(s,1);e.push(s)}return this.mergeFunction(e)}{let s=!1;for(const n of t){const t=n.rank;if(null==t){const t=n.shape,r=t[0],a=t.slice(1).concat([r]);let o=i.reshape(n,[r].concat(Z(t.slice(1))));o=i.transpose(o,[1,0]),o=i.reshape(o,a),e.push(o),s=!0}else if(t>1){const r=Y(1,t).concat([0]);e.push(i.transpose(n,r)),s=!0}else e.push(n)}let n=this.mergeFunction(e);const r=n.rank;if(s)if(null==r){const t=n.shape,e=t[t.length-1],s=[e].concat(t.slice(0,t.length-1));n=i.reshape(i.transpose(i.reshape(n,[-1,e]),[1,0]),s)}else if(r>1){const t=[r-1].concat(Y(0,r-1));n=i.transpose(n,t)}return n}}return this.mergeFunction(t)}))}computeOutputShape(t){let e;e=null==t[0]?null:t[0].slice(1);for(let s=1;s<t.length;++s){const i=null==t[s]?null:t[s].slice(1);e=this.computeElementwiseOpOutputShape(e,i)}let s=[];for(const e of t)null!=e&&null!==e[0]&&s.push(e[0]);return s=S(s),e=1===s.length?s.concat(e):[null].concat(e),e}computeMask(t,e){return i.tidy((()=>{if(null==e)return null;if(!Array.isArray(e))throw new a("`mask` should be an Array");if(!Array.isArray(t))throw new a("`inputs` should be an Array");if(e.length!==t.length)throw new a(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every((t=>null==t)))return null;let s=(e=e.map((t=>null==t?t:i.expandDims(t,0))))[0];for(let t=1;t<e.length-1;++t)s=i.logicalAnd(s,e[t]);return s}))}}class rn extends nn{constructor(t){super(t)}mergeFunction(t){return e.tidy((()=>{let e=t[0].clone();for(let s=1;s<t.length;++s)e=i.add(e,t[s]);return e}))}}rn.className="Add",e.serialization.registerClass(rn);class an extends nn{constructor(t){super(t)}mergeFunction(t){return e.tidy((()=>{let e=t[0].clone();for(let s=1;s<t.length;++s)e=i.mul(e,t[s]);return e}))}}an.className="Multiply",e.serialization.registerClass(an);class on extends nn{constructor(t){super(t)}mergeFunction(t){return e.tidy((()=>{let e=t[0].clone();for(let s=1;s<t.length;++s)e=i.add(e,t[s]);return i.mul(1/t.length,e)}))}}on.className="Average",e.serialization.registerClass(on);class ln extends nn{constructor(t){super(t)}mergeFunction(t){return e.tidy((()=>{let e=t[0];for(let s=1;s<t.length;++s)e=i.maximum(e,t[s]);return e}))}}ln.className="Maximum",e.serialization.registerClass(ln);class un extends nn{constructor(t){super(t)}mergeFunction(t){return e.tidy((()=>{let e=t[0];for(let s=1;s<t.length;++s)e=i.minimum(e,t[s]);return e}))}}un.className="Minimum",e.serialization.registerClass(un);class hn extends nn{constructor(t){super(t),this.DEFAULT_AXIS=-1,null==t&&(t={}),this.axis=null==t.axis?this.DEFAULT_AXIS:t.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){if(!Array.isArray(t)||!Array.isArray(t[0])||1===t.length)throw new a("A `Concatenate` layer should be called on a list of at least 2 inputs");let s=!0;for(const e of t)if(null!=e){s=!1;break}if(s)return;const i=[];for(let s=0;s<t.length;++s){const n=t[s].slice();n.splice(this.axis,1);let r=!1;for(const t of i)if(e.util.arraysEqual(t,n)){r=!0;break}r||i.push(n)}if(i.length>1)throw new a("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return e.tidy((()=>rt(t,this.axis)))}computeOutputShape(t){if(!Array.isArray(t)||!Array.isArray(t[0]))throw new a("A `Concatenate` layer should be called on a list of inputs.");const e=t,s=e[0].slice(),i=this.axis<0?s.length+this.axis:this.axis;for(const t of e.slice(1)){if(null==s[i]||null==t[i]){s[i]=null;break}s[i]+=t[i]}return s}computeMask(t,e){if(null==e)return null;if(!Array.isArray(e))throw new a("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new a("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new a(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return i.tidy((()=>{let s=!0;if(e.forEach((t=>{null==t||(s=!1)})),s)return null;const n=[];for(let s=0;s<t.length;++s)null==e[s]?n.push(i.cast(i.onesLike(t[s]),"bool")):e[s].rank<t[s].rank?n.push(i.expandDims(e[s],-1)):n.push(e[s]);const r=i.concat(n,this.axis);return i.all(r,-1,!1)}))}getConfig(){const t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}}function cn(t,e){for(;t<0;)t+=e;return t}hn.className="Concatenate",e.serialization.registerClass(hn);class pn extends nn{constructor(t){super(t),this.axes=t.axes,this.normalize=null!=t.normalize&&t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){i.util.assert(Array.isArray(t)&&2===t.length&&Array.isArray(t[0])&&Array.isArray(t[1]),(()=>"A `Dot` layer should be called on a list of exactly 2 inputs."));const e=t[0],s=t[1];if(e.length>3||s.length>3)throw new o("Dot layer does not support tensors of 4D or higher rank yet.");const n=this.interpretAxes(e,s);if(e[n[0]]!==s[n[1]])throw new a(`Dimension incompatibility: ${e[n[0]]} !== ${s[n[1]]}`)}mergeFunction(t){if(2!==t.length)throw new a(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e,s=t[0],n=t[1];return e=Array.isArray(this.axes)?this.axes.map(((e,s)=>cn(e,t[s].shape.length))):[cn(this.axes,s.shape.length),cn(this.axes,n.shape.length)],this.normalize&&(s=$e(s,e[0]),n=$e(n,e[1])),function(t,e,s){if(t.shape.length>3||e.shape.length>3)throw new o("batchDot is not implemented for tensors of 4D or higher rank yet");if(i.util.assert(t.shape.length>=2,(()=>`batchDot requires the rank of x to be >= 2, but got ${t.shape.length}`)),i.util.assert(t.shape.length>=2,(()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`)),"number"==typeof s&&(s=[s,s]),"complex64"===t.dtype||"complex64"===e.dtype)throw new o("batchDot is not implemented for complex64-type Tensors yet.");const n=t.shape.length,r=e.shape.length;null==s&&(s=[n-1,r-2]);const a=s;return i.tidy((()=>{let s,o;if(n>r){s=n-r;const t=[];for(let e=0;e<s;++e)t.push(1);e=i.reshape(e,e.shape.concat(t))}else if(r>n){s=r-n;const e=[];for(let t=0;t<s;++t)e.push(1);t=i.reshape(t,t.shape.concat(e))}else s=0;if(2===t.shape.length&&2===e.shape.length)o=a[0]===a[1]?i.sum(i.mul(t,e),a[0]):i.sum(i.mul(i.transpose(t,[1,0]),e),a[1]);else{const s=a[0]!==t.shape.length-1,n=a[1]===e.shape.length-1;o=i.matMul(t,e,s,n)}if(s>0){let t;t=n>r?n+r-3:n-1;const e=[];for(let i=t;i<t+s;++i)e.push(i);o=i.squeeze(o,e)}return 1===o.shape.length&&(o=i.expandDims(o,1)),o}))}(s,n,e)}interpretAxes(t,e){let s;return s=Array.isArray(this.axes)?this.axes:[cn(this.axes,t.length),cn(this.axes,e.length)],s}computeOutputShape(t){i.util.assert(Array.isArray(t)&&2===t.length&&Array.isArray(t[0])&&Array.isArray(t[1]),(()=>"A `Dot` layer should be called on a list of exactly 2 inputs."));const e=t[0].slice(),s=t[1].slice();if(e.length>3||s.length>3)throw new o("Dot layer does not support tensors of 4D or higher rank yet.");const n=this.interpretAxes(e,s);e.splice(n[0],1),s.splice(n[1],1),s.splice(0,1);const r=e.concat(s);return 1===r.length&&r.push(1),r}computeMask(t,e){return null}getConfig(){const t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}}pn.className="Dot",e.serialization.registerClass(pn);class dn extends Xt{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){const t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);const i=Wt(t);return ft((()=>e.add(lt(i.shape,0,this.stddev),i)),(()=>i),s.training||!1)}))}}dn.className="GaussianNoise",e.serialization.registerClass(dn);class gn extends Xt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){const t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s);const i=Wt(t);if(this.rate>0&&this.rate<1){return ft((()=>{const t=Math.sqrt(this.rate/(1-this.rate));return e.mul(i,lt(i.shape,1,t))}),(()=>i),s.training||!1)}return i}))}}gn.className="GaussianDropout",e.serialization.registerClass(gn);class fn extends Xt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||Wt(t).shape}computeOutputShape(t){return t}getConfig(){const t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,s){return e.tidy((()=>{if(this.rate<1&&this.rate>0){const i=this._getNoiseShape(t),n=()=>{const s=Wt(t),n=-1.7580993408473766;let r=e.greaterEqual(e.randomUniform(i),this.rate);r=tt(r,"float32");const a=((1-this.rate)*(1+this.rate*n**2))**-.5,o=-a*n*this.rate,l=e.add(e.mul(s,r),e.mul(e.add(r,-1),n));return e.add(e.mul(l,a),o)};return ft(n,(()=>Wt(t)),s.training||!1)}return t}))}}function mn(t,e,s,n,r,a=.001){let l;if(2===t.rank)l=i.batchNorm2d(t,e,s,n,r,a);else if(3===t.rank)l=i.batchNorm3d(t,e,s,n,r,a);else{if(4!==t.rank)throw new o(`batchNormalization is not implemented for array of rank ${t.rank} yet`);l=i.batchNorm4d(t,e,s,n,r,a)}return l}function yn(t,s,n,r,a=.001){return e.util.arraysEqual(r.slice().sort(),Y(0,t.rank-1))?function(t,s,n,r,a=.001){return e.tidy((()=>{const e=i.moments(t,r),o=e.mean,l=e.variance;return[mn(t,o,l,n,s,a),o,l]}))}(t,s,n,r,a):function(t,s,n,r,a=.001){return e.tidy((()=>{const o=i.moments(t,r),l=o.mean,u=o.variance,h=[];for(const e of Y(0,t.rank))-1!==r.indexOf(e)?h.push(1):h.push(t.shape[e]);const c=e.reshape(l,h),p=e.reshape(u,h),d=null==s?null:e.reshape(s,h),g=null==n?null:e.reshape(n,h);return[mn(t,c,p,g,d,a),l,u]}))}(t,s,n,r,a)}fn.className="AlphaDropout",e.serialization.registerClass(fn);class bn extends Xt{constructor(t){null==t&&(t={}),super(t),this.supportsMasking=!0,this.axis=null==t.axis?-1:t.axis,this.momentum=null==t.momentum?.99:t.momentum,this.epsilon=null==t.epsilon?.001:t.epsilon,this.center=null==t.center||t.center,this.scale=null==t.scale||t.scale,this.betaInitializer=_t(t.betaInitializer||"zeros"),this.gammaInitializer=_t(t.gammaInitializer||"ones"),this.movingMeanInitializer=_t(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=_t(t.movingVarianceInitializer||"ones"),this.betaConstraint=be(t.betaConstraint),this.gammaConstraint=be(t.gammaConstraint),this.betaRegularizer=ai(t.betaRegularizer),this.gammaRegularizer=ai(t.gammaRegularizer)}build(t){t=Ut(t);const e=this.axis>=0?this.axis:this.axis+t.length,s=t[e];if(null==s)throw new a(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ht({ndim:t.length,axes:{[e]:s}})];const i=[s];this.scale&&(this.gamma=this.addWeight("gamma",i,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",i,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",i,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",i,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,s){return e.tidy((()=>{const n=null!=s.training&&s.training,r=Wt(t),a=r.shape,o=a.length,l=Y(0,o),u=this.axis>=0?this.axis:this.axis+o;l.splice(u,1);const c=h(1,o);c[u]=a[u];const p=l.slice();p.sort();const d=!e.util.arraysEqual(p,Y(0,o).slice(0,o-1));if(!n)return(()=>{if(d){const t=e.reshape(this.movingMean.read(),c),s=e.reshape(this.movingVariance.read(),c),i=this.center?e.reshape(this.beta.read(),c):null,n=this.scale?e.reshape(this.gamma.read(),c):null;return mn(r,t,s,i,n,this.epsilon)}return mn(r,this.movingMean.read(),this.movingVariance.read(),null==this.beta?null:this.beta.read(),null==this.gamma?null:this.gamma.read(),this.epsilon)})();const[g,f,m]=yn(r,this.gamma.read(),this.beta.read(),l,this.epsilon),y=(t,e,s)=>{i.tidy((()=>{const n=1-s,r=t.read(),a=i.mul(i.sub(r,e),n);t.write(i.sub(r,a))}))};return(()=>{y(this.movingMean,f,this.momentum),y(this.movingVariance,m,this.momentum)})(),g}))}getConfig(){const t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Rt(this.betaInitializer),gammaInitializer:Rt(this.gammaInitializer),movingMeanInitializer:Rt(this.movingMeanInitializer),movingVarianceInitializer:Rt(this.movingVarianceInitializer),betaRegularizer:ni(this.betaRegularizer),gammaRegularizer:ni(this.gammaRegularizer),betaConstraint:me(this.betaConstraint),gammaConstraint:me(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}}bn.className="BatchNormalization",e.serialization.registerClass(bn);class wn extends Xt{constructor(t){if(null==t&&(t={}),super(t),this.axis=null==t.axis?-1:t.axis,"number"==typeof this.axis){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else{if(!Array.isArray(this.axis))throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}this.epsilon=null==t.epsilon?.001:t.epsilon,this.center=null==t.center||t.center,this.scale=null==t.scale||t.scale,this.betaInitializer=_t(t.betaInitializer||"zeros"),this.gammaInitializer=_t(t.gammaInitializer||"ones"),this.betaRegularizer=ai(t.betaRegularizer),this.gammaRegularizer=ai(t.gammaRegularizer),this.supportsMasking=!0}build(t){const e=(t=Ut(t)).length;"number"==typeof this.axis&&(this.axis=[this.axis]);for(let t=0;t<this.axis.length;++t)this.axis[t]<0&&(this.axis[t]+=e);for(const t of this.axis)if(t<0||t>=e)throw new Error(`Invalid axis: ${t}`);if(this.axis.length!==S(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const s=this.axis.map((e=>t[e]));this.scale?this.gamma=this.addWeight("gamma",s,"float32",this.gammaInitializer,this.gammaRegularizer,true):this.gamma=null,this.center?this.beta=this.addWeight("beta",s,"float32",this.betaInitializer,this.betaRegularizer,true):this.beta=null,this.built=!0}call(t,s){const n=Wt(t),r=n.shape,a=r.length;return e.tidy((()=>{let{mean:t,variance:s}=e.moments(n,this.axis,!0);const o=h(1,a);for(const t of this.axis)o[t]=r[t];const l=t=>null!=t&&t.shape.length!==a?i.reshape(t,o):t;let u=this.scale?l(this.gamma.read()):null,c=this.center?l(this.beta.read()):null;const p=[],d=[];for(let t=0;t<a;++t)-1!==this.axis.indexOf(t)?(p.push(r[t]),d.push(1)):(p.push(1),d.push(r[t]));return t=i.tile(t,p),s=i.tile(s,p),null!=u&&(u=i.tile(u,d)),null!=c&&(c=i.tile(c,d)),mn(n,t,s,c,u,this.epsilon)}))}getConfig(){const t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Rt(this.betaInitializer),gammaInitializer:Rt(this.gammaInitializer),betaRegularizer:ni(this.betaRegularizer),gammaRegularizer:ni(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}}wn.className="LayerNormalization",e.serialization.registerClass(wn);class zn extends Xt{constructor(t){if(null==t&&(t={}),super(t),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,null==t.padding)this.padding=[[1,1],[1,1]];else if("number"==typeof t.padding)this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,2!==t.padding.length)throw new a(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,s;if("number"==typeof t.padding[0])e=[t.padding[0],t.padding[0]],s=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,2!==t.padding[0].length)throw new a(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],2!==t.padding[1].length)throw new a(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);s=t.padding[1]}this.padding=[e,s]}this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(t){let e,s;return t=Ut(t),"channelsFirst"===this.dataFormat?(e=null!=t[2]&&t[2]>=0?t[2]+this.padding[0][0]+this.padding[0][1]:null,s=null!=t[3]&&t[3]>=0?t[3]+this.padding[1][0]+this.padding[1][1]:null,[t[0],t[1],e,s]):(e=null!=t[1]&&t[1]>=0?t[1]+this.padding[0][0]+this.padding[0][1]:null,s=null!=t[2]&&t[2]>=0?t[2]+this.padding[1][0]+this.padding[1][1]:null,[t[0],e,s,t[3]])}call(t,s){return e.tidy((()=>{return s=Wt(t),n=this.padding,r=this.dataFormat,e.tidy((()=>{if(4!==s.rank)throw new a(`temporalPadding expects input tensor to be 4-D, but received a ${s.rank}-D tensor.`);if(null==n&&(n=[[1,1],[1,1]]),2!==n.length||2!==n[0].length||2!==n[1].length)throw new a("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==r&&(r="channelsLast"),"channelsLast"!==r&&"channelsFirst"!==r)throw new a(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let t;return t="channelsFirst"===r?[[0,0],[0,0],n[0],n[1]]:[[0,0],n[0],n[1],[0,0]],i.pad(s,t)}));var s,n,r}))}getConfig(){const t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}function kn(t,s,n,r,a,o){return e.tidy((()=>{let e;B(a),U(o),W(r),null==n&&(n=[1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==o&&(o="max"),t=mi(t,a);const l="same"===r?"same":"valid";return e="max"===o?i.maxPool(t,s,n,l):i.avgPool(t,s,n,l),"channelsFirst"===a&&(e=i.transpose(e,[0,3,1,2])),e}))}function Sn(t,s,n,r,a,o){return e.tidy((()=>{let e;B(a),U(o),W(r),null==n&&(n=[1,1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==o&&(o="max"),t=yi(t,a);const l="same"===r?"same":"valid";return e="max"===o?i.maxPool3d(t,s,n,l):i.avgPool3d(t,s,n,l),"channelsFirst"===a&&(e=i.transpose(e,[0,4,1,2,3])),e}))}zn.className="ZeroPadding2D",e.serialization.registerClass(zn);class vn extends Xt{constructor(t){if(null==t.poolSize&&(t.poolSize=2),super(t),"number"==typeof t.poolSize)this.poolSize=[t.poolSize];else{if(!Array.isArray(t.poolSize)||1!==t.poolSize.length||"number"!=typeof t.poolSize[0])throw new a(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);this.poolSize=t.poolSize}if(A(this.poolSize,"poolSize"),null==t.strides)this.strides=this.poolSize;else if("number"==typeof t.strides)this.strides=[t.strides];else{if(!Array.isArray(t.strides)||1!==t.strides.length||"number"!=typeof t.strides[0])throw new a(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);this.strides=t.strides}A(this.strides,"strides"),this.padding=null==t.padding?"valid":t.padding,W(this.padding),this.inputSpec=[new Ht({ndim:3})]}computeOutputShape(t){const e=gi((t=Ut(t))[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,s){return e.tidy((()=>{this.invokeCallHook(t,s),t=et(Wt(t),2);const e=this.poolingFunction(Wt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return i.squeeze(e,[2])}))}getConfig(){const t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}}class Nn extends vn{constructor(t){super(t)}poolingFunction(t,e,s,i,n){return B(n),W(i),kn(t,e,s,i,n,"max")}}Nn.className="MaxPooling1D",e.serialization.registerClass(Nn);class Cn extends vn{constructor(t){super(t)}poolingFunction(t,e,s,i,n){return B(n),W(i),kn(t,e,s,i,n,"avg")}}Cn.className="AveragePooling1D",e.serialization.registerClass(Cn);class An extends Xt{constructor(t){if(null==t.poolSize&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],null==t.strides)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(2!==t.strides.length)throw new a(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];A(this.poolSize,"poolSize"),A(this.strides,"strides"),this.padding=null==t.padding?"valid":t.padding,this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,B(this.dataFormat),W(this.padding),this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(t){t=Ut(t);let e="channelsFirst"===this.dataFormat?t[2]:t[1],s="channelsFirst"===this.dataFormat?t[3]:t[2];return e=gi(e,this.poolSize[0],this.padding,this.strides[0]),s=gi(s,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[t[0],t[1],e,s]:[t[0],e,s,t[3]]}call(t,s){return e.tidy((()=>(this.invokeCallHook(t,s),this.poolingFunction(Wt(t),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class xn extends An{constructor(t){super(t)}poolingFunction(t,e,s,i,n){return B(n),W(i),kn(t,e,s,i,n,"max")}}xn.className="MaxPooling2D",e.serialization.registerClass(xn);class In extends An{constructor(t){super(t)}poolingFunction(t,e,s,i,n){return B(n),W(i),kn(t,e,s,i,n,"avg")}}In.className="AveragePooling2D",e.serialization.registerClass(In);class Ln extends Xt{constructor(t){if(null==t.poolSize&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],null==t.strides)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(3!==t.strides.length)throw new a(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];A(this.poolSize,"poolSize"),A(this.strides,"strides"),this.padding=null==t.padding?"valid":t.padding,this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,B(this.dataFormat),W(this.padding),this.inputSpec=[new Ht({ndim:5})]}computeOutputShape(t){t=Ut(t);let e="channelsFirst"===this.dataFormat?t[2]:t[1],s="channelsFirst"===this.dataFormat?t[3]:t[2],i="channelsFirst"===this.dataFormat?t[4]:t[3];return e=gi(e,this.poolSize[0],this.padding,this.strides[0]),s=gi(s,this.poolSize[1],this.padding,this.strides[1]),i=gi(i,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[t[0],t[1],e,s,i]:[t[0],e,s,i,t[4]]}call(t,s){return e.tidy((()=>(this.invokeCallHook(t,s),this.poolingFunction(Wt(t),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class Tn extends Ln{constructor(t){super(t)}poolingFunction(t,e,s,i,n){return B(n),W(i),Sn(t,e,s,i,n,"max")}}Tn.className="MaxPooling3D",e.serialization.registerClass(Tn);class En extends Ln{constructor(t){super(t)}poolingFunction(t,e,s,i,n){return B(n),W(i),Sn(t,e,s,i,n,"avg")}}En.className="AveragePooling3D",e.serialization.registerClass(En);class Dn extends Xt{constructor(t){super(t),this.inputSpec=[new Ht({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new o}}class $n extends Dn{constructor(t){super(t||{})}call(t,s){return e.tidy((()=>{const e=Wt(t);return i.mean(e,1)}))}}$n.className="GlobalAveragePooling1D",e.serialization.registerClass($n);class Fn extends Dn{constructor(t){super(t||{})}call(t,s){return e.tidy((()=>{const e=Wt(t);return i.max(e,1)}))}}Fn.className="GlobalMaxPooling1D",e.serialization.registerClass(Fn);class On extends Xt{constructor(t){super(t),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,B(this.dataFormat),this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(t){return"channelsLast"===this.dataFormat?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new o}getConfig(){const t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class Rn extends On{call(t,s){return e.tidy((()=>{const e=Wt(t);return"channelsLast"===this.dataFormat?i.mean(e,[1,2]):i.mean(e,[2,3])}))}}Rn.className="GlobalAveragePooling2D",e.serialization.registerClass(Rn);class _n extends On{call(t,s){return e.tidy((()=>{const e=Wt(t);return"channelsLast"===this.dataFormat?i.max(e,[1,2]):i.max(e,[2,3])}))}}_n.className="GlobalMaxPooling2D",e.serialization.registerClass(_n);class Mn extends Xt{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return null!=this.layer&&this.layer.trainable}set trainable(t){null!=this.layer&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){const t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,s={}){const i=De(e.layer,s);delete e.layer;const n={layer:i};return Object.assign(n,e),new t(n)}}class Bn extends Mn{constructor(t){super(t),this.supportsMasking=!0}build(t){if((t=Ut(t)).length<3)throw new a(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];const e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){const e=[(t=Ut(t))[0]].concat(t.slice(2)),s=this.layer.computeOutputShape(e),i=t[1];return[s[0],i].concat(s.slice(1))}call(t,s){return e.tidy((()=>Di(((t,e)=>[Wt(this.layer.call(t,s)),[]]),t=Wt(t),[],!1,null,null,!1,!0)[1]))}}Bn.className="TimeDistributed",e.serialization.registerClass(Bn);class Wn extends Mn{constructor(t){super(t);const e=t.layer.getConfig(),s={};s.className=t.layer.getClassName(),s.config=e,this.forwardLayer=De(s),e.goBackwards=!0!==e.goBackwards;const i={};var n;if(i.className=t.layer.getClassName(),i.config=e,this.backwardLayer=De(i),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===t.mergeMode?"concat":t.mergeMode,n=this.mergeMode,N(_,"BidirectionalMergeMode",n),t.weights)throw new o("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,null!=this.forwardLayer&&(this.forwardLayer.trainable=t),null!=this.backwardLayer&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){const e=t.length,s=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,s)),this.backwardLayer.setWeights(t.slice(s))}computeOutputShape(t){let e,s,i,n=this.forwardLayer.computeOutputShape(t);return Array.isArray(n)&&Array.isArray(n[0])||(n=[n]),this.returnState?(i=n.slice(1),e=n[0]):e=n[0],"concat"===this.mergeMode?(e[e.length-1]*=2,s=[e]):s=null==this.mergeMode?[e,e.slice()]:[e],this.returnState?null==this.mergeMode?s.concat(i).concat(i.slice()):[e].concat(i).concat(i.slice()):d(s)}apply(t,e){let s=null==e?null:e.initialState,i=null==e?null:e.constants;null==e&&(e={});const n=Ei(t,s,i,this.numConstants);if(t=n.inputs,s=n.initialState,i=n.constants,Array.isArray(t)&&(s=t.slice(1),t=t[0]),(null==s||0===s.length)&&null==i)return super.apply(t,e);const r=[],l=[];if(null!=s){const t=s.length;if(t%2>0)throw new a("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=s,r.push(...s);const i=s.map((t=>new Ht({shape:t.shape})));this.forwardLayer.stateSpec=i.slice(0,t/2),this.backwardLayer.stateSpec=i.slice(t/2),l.push(...i)}if(null!=i)throw new o("Support for constants in Bidirectional layers is not implemented yet.");const u=r[0]instanceof Zt;for(const t of r)if(t instanceof Zt!==u)throw new a("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){const s=[t].concat(r),i=this.inputSpec.concat(l),n=this.inputSpec;this.inputSpec=i;const a=super.apply(s,e);return this.inputSpec=n,a}return super.apply(t,e)}call(t,s){return e.tidy((()=>{const e=s.initialState;let n,r,a,o;if(null==e)n=this.forwardLayer.call(t,s),r=this.backwardLayer.call(t,s);else{const i=e.slice(0,e.length/2),a=e.slice(e.length/2);n=this.forwardLayer.call(t,Object.assign(s,{initialState:i})),r=this.backwardLayer.call(t,Object.assign(s,{initialState:a}))}return this.returnState&&(Array.isArray(n)&&(a=n.slice(1).concat(r.slice(1))),n=n[0],r=r[0]),this.returnSequences&&(r=i.reverse(r,1)),"concat"===this.mergeMode?o=rt([n,r]):"sum"===this.mergeMode?o=i.add(n,r):"ave"===this.mergeMode?o=i.mul(.5,i.add(n,r)):"mul"===this.mergeMode?o=i.mul(n,r):null==this.mergeMode&&(o=[n,r]),this.returnState?null==this.mergeMode?o.concat(a):[o].concat(a):o}))}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){j(this.forwardLayer.name,(()=>{this.forwardLayer.build(t)})),j(this.backwardLayer.name,(()=>{this.backwardLayer.build(t)})),this.built=!0}computeMask(t,e){let s;if(Array.isArray(e)&&(e=e[0]),s=this.returnSequences?null==this.mergeMode?[e,e]:e:null==this.mergeMode?[null,null]:null,this.returnState){const t=this.forwardLayer.states.map((t=>null));return Array.isArray(s)?s.concat(t).concat(t):[s].concat(t).concat(t)}return s}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),null!=this.forwardLayer&&this.forwardLayer.setFastWeightInitDuringBuild(t),null!=this.backwardLayer&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){const t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){const s=De(e.layer);if(delete e.layer,null!=e.numConstants)throw new o("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const i=e;return i.layer=s,new t(i)}}function Un(t){return new Cn(t)}function Pn(t){return new In(t)}function jn(t){return new En(t)}function Vn(t){return new Fn(t)}function qn(t){return new _n(t)}function Jn(t){return new Nn(t)}function Hn(t){return new xn(t)}Wn.className="Bidirectional",e.serialization.registerClass(Wn);var Zn={__proto__:null,inputLayer:function(t){return new te(t)},elu:function(t){return new hi(t)},reLU:function(t){return new oi(t)},leakyReLU:function(t){return new li(t)},prelu:function(t){return new ui(t)},softmax:function(t){return new pi(t)},thresholdedReLU:function(t){return new ci(t)},conv1d:function(t){return new xi(t)},conv2d:function(t){return new ki(t)},conv2dTranspose:function(t){return new vi(t)},conv3d:function(t){return new Si(t)},conv3dTranspose:function(t){return new Ni(t)},separableConv2d:function(t){return new Ai(t)},cropping2D:function(t){return new Ii(t)},upSampling2d:function(t){return new Li(t)},depthwiseConv2d:function(t){return new Ti(t)},activation:function(t){return new Yi(t)},dense:function(t){return new Ki(t)},dropout:function(t){return new Hi(t)},spatialDropout1d:function(t){return new Zi(t)},flatten:function(t){return new Gi(t)},repeatVector:function(t){return new Xi(t)},reshape:function(t){return new Qi(t)},permute:function(t){return new tn(t)},embedding:function(t){return new sn(t)},add:function(t){return new rn(t)},average:function(t){return new on(t)},concatenate:function(t){return new hn(t)},maximum:function(t){return new ln(t)},minimum:function(t){return new un(t)},multiply:function(t){return new an(t)},dot:function(t){return new pn(t)},batchNormalization:function(t){return new bn(t)},layerNormalization:function(t){return new wn(t)},zeroPadding2d:function(t){return new zn(t)},averagePooling1d:Un,avgPool1d:function(t){return Un(t)},avgPooling1d:function(t){return Un(t)},averagePooling2d:Pn,avgPool2d:function(t){return Pn(t)},avgPooling2d:function(t){return Pn(t)},averagePooling3d:jn,avgPool3d:function(t){return jn(t)},avgPooling3d:function(t){return jn(t)},globalAveragePooling1d:function(t){return new $n(t)},globalAveragePooling2d:function(t){return new 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Yn={__proto__:null,l1l2:function(t){return new si(t)},l1:function(t){return ti(e=t),new si({l1:null!=e?e.l1:null,l2:0});var e},l2:function(t){return ti(e=t),new si({l2:null!=e?e.l2:null,l1:0});var e}};class Xn extends Ne{constructor(){super(...arguments),this.model=null}setModel(t){if(!(t instanceof Es))throw new Error("model must be a LayersModel, not some other Container");this.model=t}}function Qn(t,e){return t<e}function tr(t,e){return t>e}class er extends Xn{constructor(t){if(super(),null==t&&(t={}),t.restoreBestWeights)throw new o("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=t.monitor||"val_loss",this.minDelta=Math.abs(t.minDelta||0),this.patience=t.patience||0,this.verbose=t.verbose||0,this.mode=t.mode||"auto",this.baseline=t.baseline,-1===["auto","min","max"].indexOf(this.mode)&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. 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