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human/types/dist/tfjs.esm.d.ts

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export var Abs: string;
export var Acos: string;
export var Acosh: string;
export var AdadeltaOptimizer: {
new (learningRate: any, rho: any, epsilon3?: null): {
learningRate: any;
rho: any;
epsilon: any;
accumulatedGrads: any[];
accumulatedUpdates: any[];
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
rho: any;
epsilon: any;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
fromConfig(cls: any, config: any): any;
className: string;
};
export var AdagradOptimizer: {
new (learningRate: any, initialAccumulatorValue?: number): {
learningRate: any;
initialAccumulatorValue: number;
accumulatedGrads: any[];
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
initialAccumulatorValue: number;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
fromConfig(cls: any, config: any): any;
className: string;
};
export var AdamOptimizer: {
new (learningRate: any, beta1: any, beta2: any, epsilon3?: null): {
learningRate: any;
beta1: any;
beta2: any;
epsilon: any;
accumulatedFirstMoment: any[];
accumulatedSecondMoment: any[];
accBeta1: any;
accBeta2: any;
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
beta1: any;
beta2: any;
epsilon: any;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
fromConfig(cls: any, config: any): any;
className: string;
};
export var AdamaxOptimizer: {
new (learningRate: any, beta1: any, beta2: any, epsilon3?: null, decay?: number): {
learningRate: any;
beta1: any;
beta2: any;
epsilon: any;
decay: number;
accumulatedFirstMoment: any[];
accumulatedWeightedInfNorm: any[];
iteration: any;
accBeta1: any;
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<void>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
beta1: any;
beta2: any;
epsilon: any;
decay: number;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
fromConfig(cls: any, config: any): any;
className: string;
};
export var Add: string;
export var AddN: string;
export var All: string;
export var Any: string;
export var ArgMax: string;
export var ArgMin: string;
export var Asin: string;
export var Asinh: string;
export var Atan: string;
export var Atan2: string;
export var Atanh: string;
export var AvgPool: string;
export var AvgPool3D: string;
export var AvgPool3DGrad: string;
export var AvgPoolGrad: string;
export var BackendWasm: {
new (wasm: any): {
wasm: any;
dataIdNextNumber: number;
dataIdMap: {
backend: any;
dataMover: any;
data: WeakMap<object, any>;
dataIdsCount: number;
get(dataId: any): any;
set(dataId: any, value: any): void;
has(dataId: any): boolean;
delete(dataId: any): boolean;
numDataIds(): number;
};
write(values: any, shape: any, dtype: any): {
id: number;
};
numDataIds(): number;
time(f: any): Promise<{
kernelMs: number;
}>;
move(dataId: any, values: any, shape: any, dtype: any, refCount: any): void;
read(dataId: any): Promise<any>;
readSync(dataId: any): any;
disposeData(dataId: any, force?: boolean): boolean;
refCount(dataId: any): any;
incRef(dataId: any): void;
floatPrecision(): number;
getMemoryOffset(dataId: any): any;
dispose(): void;
memory(): {
unreliable: boolean;
};
makeOutput(shape: any, dtype: any, memoryOffset: any): {
dataId: {
id: number;
};
shape: any;
dtype: any;
};
typedArrayFromHeap({ shape, dtype, dataId }: {
shape: any;
dtype: any;
dataId: any;
}): Float32Array | Int32Array | Uint8Array;
timerAvailable(): boolean;
epsilon(): number;
};
};
export var BatchMatMul: string;
export var BatchToSpaceND: string;
export var Bincount: string;
export var BroadcastArgs: string;
export var BroadcastTo: string;
export var Callback: {
new (...args: any[]): {
model: {
[x: string]: any;
isTraining: boolean;
summary(lineLength: any, positions: any, printFn?: {
(...data: any[]): void;
(...data: any[]): void;
(message?: any, ...optionalParams: any[]): void;
}): void;
compile(args: any): void;
loss: any;
optimizer_: any;
isOptimizerOwned: boolean | undefined;
lossFunctions: any;
feedOutputNames: any[] | undefined;
feedOutputShapes: any[] | undefined;
feedLossFns: any[] | undefined;
metrics: any;
metricsNames: string[] | undefined;
metricsTensors: any[] | undefined;
collectedTrainableWeights: any;
checkTrainableWeightsConsistency(): void;
evaluate(x: any, y: any, args?: {}): any;
evaluateDataset(dataset: any, args: any): Promise<any>;
checkNumSamples(ins: any, batchSize: any, steps: any, stepsName?: string): any;
execute(inputs: any, outputs: any): any;
retrieveSymbolicTensors(symbolicTensorNames: any): any[];
predictLoop(ins: any, batchSize?: number, verbose?: boolean): any;
predict(x: any, args?: {}): any;
predictOnBatch(x: any): any;
standardizeUserDataXY(x: any, y: any, checkBatchAxis: boolean | undefined, batchSize: any): any[];
standardizeUserData(x: any, y: any, sampleWeight: any, classWeight: any, checkBatchAxis: boolean | undefined, batchSize: any): Promise<any[]>;
testLoop(f: any, ins: any, batchSize: any, verbose: number | undefined, steps: any): any;
getDedupedMetricsNames(): string[];
makeTrainFunction(): (data: any) => any[];
makeTestFunction(): void;
testFunction: ((data: any) => any) | undefined;
fit(x: any, y: any, args?: {}): Promise<any>;
fitDataset(dataset: any, args: any): Promise<any>;
trainOnBatch(x: any, y: any): Promise<any>;
getNamedWeights(config: any): {
name: any;
tensor: any;
}[];
stopTraining: any;
stopTraining_: any;
optimizer: any;
dispose(): any;
getLossIdentifiers(): any;
getMetricIdentifiers(): {};
getTrainingConfig(): {
loss: any;
metrics: {};
optimizer_config: {
class_name: any;
config: any;
};
};
loadTrainingConfig(trainingConfig: any): void;
save(handlerOrURL: any, config: any): Promise<any>;
setUserDefinedMetadata(userDefinedMetadata: any): void;
userDefinedMetadata: any;
getUserDefinedMetadata(): any;
} | null;
setModel(model2: any): void;
validationData: any;
setParams(params: any): void;
params: any;
onEpochBegin(epoch: any, logs: any): Promise<void>;
onEpochEnd(epoch: any, logs: any): Promise<void>;
onBatchBegin(batch: any, logs: any): Promise<void>;
onBatchEnd(batch: any, logs: any): Promise<void>;
onTrainBegin(logs: any): Promise<void>;
onTrainEnd(logs: any): Promise<void>;
};
};
export var CallbackList: {
new (callbacks2: any, queueLength?: number): {
callbacks: any;
queueLength: number;
append(callback: any): void;
setParams(params: any): void;
setModel(model2: any): void;
onEpochBegin(epoch: any, logs: any): Promise<void>;
onEpochEnd(epoch: any, logs: any): Promise<void>;
onBatchBegin(batch: any, logs: any): Promise<void>;
onBatchEnd(batch: any, logs: any): Promise<void>;
onTrainBegin(logs: any): Promise<void>;
onTrainEnd(logs: any): Promise<void>;
};
};
export var Cast: string;
export var Ceil: string;
export var ClipByValue: string;
export var Complex: string;
export var ComplexAbs: string;
export var Concat: string;
export var Conv2D: string;
export var Conv2DBackpropFilter: string;
export var Conv2DBackpropInput: string;
export var Conv3D: string;
export var Conv3DBackpropFilterV2: string;
export var Conv3DBackpropInputV2: string;
export var Cos: string;
export var Cosh: string;
export var CropAndResize: string;
export var Cumsum: string;
export var CustomCallback: {
new (args: any, yieldEvery: any): {
currentEpoch: number;
yieldEvery: any;
maybeWait(epoch: any, batch: any, logs: any): Promise<void>;
trainBegin: any;
trainEnd: any;
epochBegin: any;
epochEnd: any;
batchBegin: any;
batchEnd: any;
yield: any;
onEpochBegin(epoch: any, logs: any): Promise<void>;
onEpochEnd(epoch: any, logs: any): Promise<void>;
onBatchBegin(batch: any, logs: any): Promise<void>;
onBatchEnd(batch: any, logs: any): Promise<void>;
onTrainBegin(logs: any): Promise<void>;
onTrainEnd(logs: any): Promise<void>;
validationData: any;
setParams(params: any): void;
params: any;
setModel(model2: any): void;
};
};
export var DataStorage: {
new (backend2: any, dataMover: any): {
backend: any;
dataMover: any;
data: WeakMap<object, any>;
dataIdsCount: number;
get(dataId: any): any;
set(dataId: any, value: any): void;
has(dataId: any): boolean;
delete(dataId: any): boolean;
numDataIds(): number;
};
};
export var DenseBincount: string;
export var DepthToSpace: string;
export var DepthwiseConv2dNative: string;
export var DepthwiseConv2dNativeBackpropFilter: string;
export var DepthwiseConv2dNativeBackpropInput: string;
export var Diag: string;
export var Dilation2D: string;
export var Dilation2DBackpropFilter: string;
export var Dilation2DBackpropInput: string;
export var ENV: any;
export var EarlyStopping: {
new (args: any): {
monitor: any;
minDelta: number;
patience: any;
verbose: any;
mode: any;
baseline: any;
monitorFunc: typeof less2;
onTrainBegin(logs: any): Promise<void>;
wait: number | undefined;
stoppedEpoch: any;
best: any;
onEpochEnd(epoch: any, logs: any): Promise<void>;
onTrainEnd(logs: any): Promise<void>;
getMonitorValue(logs: any): any;
model: {
[x: string]: any;
isTraining: boolean;
summary(lineLength: any, positions: any, printFn?: {
(...data: any[]): void;
(...data: any[]): void;
(message?: any, ...optionalParams: any[]): void;
}): void;
compile(args: any): void;
loss: any;
optimizer_: any;
isOptimizerOwned: boolean | undefined;
lossFunctions: any;
feedOutputNames: any[] | undefined;
feedOutputShapes: any[] | undefined;
feedLossFns: any[] | undefined;
metrics: any;
metricsNames: string[] | undefined;
metricsTensors: any[] | undefined;
collectedTrainableWeights: any;
checkTrainableWeightsConsistency(): void;
evaluate(x: any, y: any, args?: {}): any;
evaluateDataset(dataset: any, args: any): Promise<any>;
checkNumSamples(ins: any, batchSize: any, steps: any, stepsName?: string): any;
execute(inputs: any, outputs: any): any;
retrieveSymbolicTensors(symbolicTensorNames: any): any[];
predictLoop(ins: any, batchSize?: number, verbose?: boolean): any;
predict(x: any, args?: {}): any;
predictOnBatch(x: any): any;
standardizeUserDataXY(x: any, y: any, checkBatchAxis: boolean | undefined, batchSize: any): any[];
standardizeUserData(x: any, y: any, sampleWeight: any, classWeight: any, checkBatchAxis: boolean | undefined, batchSize: any): Promise<any[]>;
testLoop(f: any, ins: any, batchSize: any, verbose: number | undefined, steps: any): any;
getDedupedMetricsNames(): string[];
makeTrainFunction(): (data: any) => any[];
makeTestFunction(): void;
testFunction: ((data: any) => any) | undefined;
fit(x: any, y: any, args?: {}): Promise<any>;
fitDataset(dataset: any, args: any): Promise<any>;
trainOnBatch(x: any, y: any): Promise<any>;
getNamedWeights(config: any): {
name: any;
tensor: any;
}[];
stopTraining: any;
stopTraining_: any;
optimizer: any;
dispose(): any;
getLossIdentifiers(): any;
getMetricIdentifiers(): {};
getTrainingConfig(): {
loss: any;
metrics: {};
optimizer_config: {
class_name: any;
config: any;
};
};
loadTrainingConfig(trainingConfig: any): void;
save(handlerOrURL: any, config: any): Promise<any>;
setUserDefinedMetadata(userDefinedMetadata: any): void;
userDefinedMetadata: any;
getUserDefinedMetadata(): any;
} | null;
setModel(model2: any): void;
validationData: any;
setParams(params: any): void;
params: any;
onEpochBegin(epoch: any, logs: any): Promise<void>;
onBatchBegin(batch: any, logs: any): Promise<void>;
onBatchEnd(batch: any, logs: any): Promise<void>;
};
};
export var Einsum: string;
export var Elu: string;
export var EluGrad: string;
export var Environment: {
new (global2: any): {
global: any;
flags: {};
flagRegistry: {};
urlFlags: {};
getQueryParams: typeof getQueryParams;
setPlatform(platformName: any, platform: any): void;
platformName: any;
platform: any;
registerFlag(flagName: any, evaluationFn: any, setHook: any): void;
getAsync(flagName: any): Promise<any>;
get(flagName: any): any;
getNumber(flagName: any): any;
getBool(flagName: any): any;
getFlags(): {};
readonly features: {};
set(flagName: any, value: any): void;
evaluateFlag(flagName: any): any;
setFlags(flags: any): void;
reset(): void;
populateURLFlags(): void;
};
};
export var Equal: string;
export var Erf: string;
export var Exp: string;
export var ExpandDims: string;
export var Expm1: string;
export var FFT: string;
export var Fill: string;
export var FlipLeftRight: string;
export var Floor: string;
export var FloorDiv: string;
export var FromPixels: string;
export var FusedBatchNorm: string;
export var FusedConv2D: string;
export var FusedDepthwiseConv2D: string;
export var GPGPUContext: {
new (gl: any): {
outputTexture: any;
program: any;
disposed: boolean;
vertexAttrsAreBound: boolean;
itemsToPoll: any[];
gl: any;
textureFloatExtension: any;
textureHalfFloatExtension: any;
colorBufferFloatExtension: any;
colorBufferHalfFloatExtension: any;
vertexBuffer: any;
indexBuffer: any;
framebuffer: any;
textureConfig: {
internalFormatFloat: any;
internalFormatHalfFloat: any;
internalFormatPackedHalfFloat: any;
internalFormatPackedFloat: any;
textureFormatFloat: any;
downloadTextureFormat: any;
downloadUnpackNumChannels: number;
defaultNumChannels: number;
textureTypeHalfFloat: any;
textureTypeFloat: any;
};
readonly debug: any;
dispose(): void;
createFloat32MatrixTexture(rows: any, columns: any): any;
createFloat16MatrixTexture(rows: any, columns: any): any;
createUnsignedBytesMatrixTexture(rows: any, columns: any): any;
uploadPixelDataToTexture(texture: any, pixels: any): void;
uploadDenseMatrixToTexture(texture: any, width: any, height: any, data: any): void;
createFloat16PackedMatrixTexture(rows: any, columns: any): any;
createPackedMatrixTexture(rows: any, columns: any): any;
deleteMatrixTexture(texture: any): void;
downloadByteEncodedFloatMatrixFromOutputTexture(texture: any, rows: any, columns: any): any;
downloadPackedMatrixFromBuffer(buffer2: any, batch: any, rows: any, columns: any, physicalRows: any, physicalCols: any): Float32Array;
downloadFloat32MatrixFromBuffer(buffer2: any, size: any): Float32Array;
createBufferFromTexture(texture: any, rows: any, columns: any): any;
createAndWaitForFence(): Promise<any>;
createFence(gl: any): {
query: any;
isFencePassed: () => any;
};
downloadMatrixFromPackedTexture(texture: any, physicalRows: any, physicalCols: any): any;
createProgram(fragmentShaderSource: any): any;
vertexShader: any;
deleteProgram(program: any): void;
setProgram(program: any): void;
getUniformLocation(program: any, uniformName: any, shouldThrow?: boolean): any;
getAttributeLocation(program: any, attribute: any): any;
getUniformLocationNoThrow(program: any, uniformName: any): any;
setInputMatrixTexture(inputMatrixTexture: any, uniformLocation: any, textureUnit: any): void;
setOutputMatrixTexture(outputMatrixTexture: any, rows: any, columns: any): void;
setOutputPackedMatrixTexture(outputPackedMatrixTexture: any, rows: any, columns: any): void;
setOutputMatrixWriteRegion(startRow: any, numRows: any, startColumn: any, numColumns: any): void;
setOutputPackedMatrixWriteRegion(startRow: any, numRows: any, startColumn: any, numColumns: any): void;
debugValidate(): void;
executeProgram(): void;
blockUntilAllProgramsCompleted(): void;
getQueryTimerExtension(): any;
disjointQueryTimerExtension: any;
getQueryTimerExtensionWebGL2(): any;
getQueryTimerExtensionWebGL1(): any;
beginQuery(): any;
endQuery(): void;
waitForQueryAndGetTime(query: any): Promise<number | null>;
getQueryTime(query: any, queryTimerVersion: any): number | null;
isQueryAvailable(query: any, queryTimerVersion: any): any;
disjoint: any;
pollFence(fenceContext: any): Promise<any>;
pollItems(): void;
addItemToPoll(isDoneFn: any, resolveFn: any): void;
bindTextureToFrameBuffer(texture: any): void;
unbindTextureToFrameBuffer(): void;
downloadMatrixDriver(texture: any, downloadAndDecode: any): any;
setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked: any, width: any, height: any): void;
setOutputMatrixWriteRegionDriver(x: any, y: any, width: any, height: any): void;
throwIfDisposed(): void;
throwIfNoProgram(): void;
};
};
export var GatherNd: string;
export var GatherV2: string;
export var GraphModel: {
new (modelUrl: any, loadOptions?: {}): {
modelUrl: any;
loadOptions: {};
version: string;
resourceManager: {
hashTableNameToHandle: {};
hashTableMap: {};
addHashTable(name: any, hashTable2: any): void;
getHashTableHandleByName(name: any): any;
getHashTableById(id: any): any;
dispose(): void;
};
readonly modelVersion: string;
readonly inputNodes: any;
readonly outputNodes: any;
readonly inputs: any;
readonly outputs: any;
readonly weights: any;
readonly metadata: any;
readonly modelSignature: any;
findIOHandler(): void;
handler: any;
load(): Promise<boolean>;
loadSync(artifacts: any): boolean;
artifacts: any;
signature: any;
executor: {
graph: any;
parent: any;
compiledMap: Map<any, any>;
_weightMap: {};
SEPERATOR: string;
_functions: any;
_functionExecutorMap: {};
_outputs: any;
_inputs: any;
_initNodes: any;
_signature: any;
readonly weightIds: any;
readonly functionExecutorMap: any;
weightMap: any;
_weightIds: any[] | undefined;
resourceManager: any;
_resourceManager: any;
readonly inputs: any;
readonly outputs: any;
readonly inputNodes: any;
readonly outputNodes: any;
readonly functions: {};
getCompilationKey(inputs: any, outputs: any): string;
compile(inputs: any, outputs: any): any[];
execute(inputs: any, outputs: any): any;
getFrozenTensorIds(tensorMap: any): Set<any>;
checkTensorForDisposal(nodeName: any, node: any, tensorMap: any, context: any, tensorsToKeep: any, outputNames: any, intermediateTensorConsumerCount: any): void;
executeAsync(inputs: any, outputs: any): Promise<any>;
_executeAsync(inputs: any, outputs: any, isFunctionExecution?: boolean, tensorArrayMap?: {}, tensorListMap?: {}): Promise<any>;
executeFunctionAsync(inputs: any, tensorArrayMap: any, tensorListMap: any): Promise<any>;
executeWithControlFlow(inputs: any, context: any, outputNames: any, isFunctionExecution: any): Promise<any>;
processStack(inputNodes: any, stack2: any, context: any, tensorMap: any, added: any, tensorsToKeep: any, outputNames: any, intermediateTensorConsumerCount: any, usedNodes: any): any[];
processChildNodes(node: any, stack2: any, context: any, tensorMap: any, added: any, usedNodes: any): void;
dispose(): void;
checkInputShapeAndType(inputs: any): void;
mapInputs(inputs: any): {};
checkInputs(inputs: any): void;
mapOutputs(outputs: any): any;
checkOutputs(outputs: any): void;
} | undefined;
initializer: {
graph: any;
parent: any;
compiledMap: Map<any, any>;
_weightMap: {};
SEPERATOR: string;
_functions: any;
_functionExecutorMap: {};
_outputs: any;
_inputs: any;
_initNodes: any;
_signature: any;
readonly weightIds: any;
readonly functionExecutorMap: any;
weightMap: any;
_weightIds: any[] | undefined;
resourceManager: any;
_resourceManager: any;
readonly inputs: any;
readonly outputs: any;
readonly inputNodes: any;
readonly outputNodes: any;
readonly functions: {};
getCompilationKey(inputs: any, outputs: any): string;
compile(inputs: any, outputs: any): any[];
execute(inputs: any, outputs: any): any;
getFrozenTensorIds(tensorMap: any): Set<any>;
checkTensorForDisposal(nodeName: any, node: any, tensorMap: any, context: any, tensorsToKeep: any, outputNames: any, intermediateTensorConsumerCount: any): void;
executeAsync(inputs: any, outputs: any): Promise<any>;
_executeAsync(inputs: any, outputs: any, isFunctionExecution?: boolean, tensorArrayMap?: {}, tensorListMap?: {}): Promise<any>;
executeFunctionAsync(inputs: any, tensorArrayMap: any, tensorListMap: any): Promise<any>;
executeWithControlFlow(inputs: any, context: any, outputNames: any, isFunctionExecution: any): Promise<any>;
processStack(inputNodes: any, stack2: any, context: any, tensorMap: any, added: any, tensorsToKeep: any, outputNames: any, intermediateTensorConsumerCount: any, usedNodes: any): any[];
processChildNodes(node: any, stack2: any, context: any, tensorMap: any, added: any, usedNodes: any): void;
dispose(): void;
checkInputShapeAndType(inputs: any): void;
mapInputs(inputs: any): {};
checkInputs(inputs: any): void;
mapOutputs(outputs: any): any;
checkOutputs(outputs: any): void;
} | undefined;
save(handlerOrURL: any, config: any): Promise<any>;
predict(inputs: any, config: any): any;
normalizeInputs(inputs: any): any;
normalizeOutputs(outputs: any): any[];
execute(inputs: any, outputs: any): any;
executeAsync(inputs: any, outputs: any): Promise<any>;
convertTensorMapToTensorsMap(map: any): {};
dispose(): void;
};
};
export var Greater: string;
export var GreaterEqual: string;
export var History: {
new (): {
onTrainBegin(logs: any): Promise<void>;
epoch: any[] | undefined;
history: {} | undefined;
onEpochEnd(epoch: any, logs: any): Promise<void>;
syncData(): Promise<void>;
validationData: any;
setParams(params: any): void;
params: any;
onEpochBegin(epoch: any, logs: any): Promise<void>;
onBatchBegin(batch: any, logs: any): Promise<void>;
onBatchEnd(batch: any, logs: any): Promise<void>;
onTrainEnd(logs: any): Promise<void>;
setModel(model2: any): void;
};
};
export var IFFT: string;
export var Identity: string;
export var Imag: string;
export var InputSpec: {
new (args: any): {
dtype: any;
shape: any;
ndim: any;
maxNDim: any;
minNDim: any;
axes: any;
};
};
export var IsFinite: string;
export var IsInf: string;
export var IsNan: string;
export var KernelBackend: {
new (): {
refCount(dataId: any): void;
incRef(dataId: any): void;
timerAvailable(): boolean;
time(f: any): void;
read(dataId: any): void;
readSync(dataId: any): void;
numDataIds(): void;
disposeData(dataId: any, force: any): void;
write(values: any, shape: any, dtype: any): void;
move(dataId: any, values: any, shape: any, dtype: any, refCount: any): void;
memory(): void;
floatPrecision(): void;
epsilon(): number;
dispose(): void;
};
};
export var LRN: string;
export var LRNGrad: string;
export var LayerVariable: {
new (val: any, dtype?: string, name?: string, trainable?: boolean, constraint?: null): {
dtype: string;
shape: any;
id: number;
originalName: string;
name: any;
trainable_: boolean;
constraint: any;
val: any;
read(): any;
write(newVal: any): any;
dispose(): void;
assertNotDisposed(): void;
trainable: boolean;
};
};
export var LayersModel: {
new (args: any): {
[x: string]: any;
isTraining: boolean;
summary(lineLength: any, positions: any, printFn?: {
(...data: any[]): void;
(...data: any[]): void;
(message?: any, ...optionalParams: any[]): void;
}): void;
compile(args: any): void;
loss: any;
optimizer_: any;
isOptimizerOwned: boolean | undefined;
lossFunctions: any;
feedOutputNames: any[] | undefined;
feedOutputShapes: any[] | undefined;
feedLossFns: any[] | undefined;
metrics: any;
metricsNames: string[] | undefined;
metricsTensors: any[] | undefined;
collectedTrainableWeights: any;
checkTrainableWeightsConsistency(): void;
evaluate(x: any, y: any, args?: {}): any;
evaluateDataset(dataset: any, args: any): Promise<any>;
checkNumSamples(ins: any, batchSize: any, steps: any, stepsName?: string): any;
execute(inputs: any, outputs: any): any;
retrieveSymbolicTensors(symbolicTensorNames: any): any[];
predictLoop(ins: any, batchSize?: number, verbose?: boolean): any;
predict(x: any, args?: {}): any;
predictOnBatch(x: any): any;
standardizeUserDataXY(x: any, y: any, checkBatchAxis: boolean | undefined, batchSize: any): any[];
standardizeUserData(x: any, y: any, sampleWeight: any, classWeight: any, checkBatchAxis: boolean | undefined, batchSize: any): Promise<any[]>;
testLoop(f: any, ins: any, batchSize: any, verbose: number | undefined, steps: any): any;
getDedupedMetricsNames(): string[];
makeTrainFunction(): (data: any) => any[];
makeTestFunction(): void;
testFunction: ((data: any) => any) | undefined;
fit(x: any, y: any, args?: {}): Promise<any>;
fitDataset(dataset: any, args: any): Promise<any>;
trainOnBatch(x: any, y: any): Promise<any>;
getNamedWeights(config: any): {
name: any;
tensor: any;
}[];
stopTraining: any;
stopTraining_: any;
optimizer: any;
dispose(): any;
getLossIdentifiers(): any;
getMetricIdentifiers(): {};
getTrainingConfig(): {
loss: any;
metrics: {};
optimizer_config: {
class_name: any;
config: any;
};
};
loadTrainingConfig(trainingConfig: any): void;
save(handlerOrURL: any, config: any): Promise<any>;
setUserDefinedMetadata(userDefinedMetadata: any): void;
userDefinedMetadata: any;
getUserDefinedMetadata(): any;
};
[x: string]: any;
className: string;
};
export var LeakyRelu: string;
export var Less: string;
export var LessEqual: string;
export var LinSpace: string;
export var Log: string;
export var Log1p: string;
export var LogSoftmax: string;
export var LogicalAnd: string;
export var LogicalNot: string;
export var LogicalOr: string;
export var MathBackendWebGL: {
new (gpgpu: any): {
pendingRead: WeakMap<object, any>;
pendingDisposal: WeakSet<object>;
dataRefCount: WeakMap<object, any>;
numBytesInGPU: number;
uploadWaitMs: number;
downloadWaitMs: number;
lastGlFlushTime: number;
warnedAboutMemory: boolean;
pendingDeletes: number;
disposed: boolean;
binaryCache: any;
gpgpu: any;
canvas: any;
gpgpuCreatedLocally: boolean;
textureManager: {
gpgpu: any;
numUsedTextures: number;
numFreeTextures: number;
_numBytesAllocated: number;
_numBytesFree: number;
freeTextures: {};
logEnabled: boolean;
usedTextures: {};
acquireTexture(shapeRC: any, usage: any, isPacked: any): any;
releaseTexture(texture: any, shape: any, logicalTexType: any, isPacked: any): void;
log(): void;
readonly numBytesAllocated: number;
readonly numBytesFree: number;
getNumUsedTextures(): number;
getNumFreeTextures(): number;
dispose(): void;
};
numMBBeforeWarning: number;
texData: {
backend: any;
dataMover: any;
data: WeakMap<object, any>;
dataIdsCount: number;
get(dataId: any): any;
set(dataId: any, value: any): void;
has(dataId: any): boolean;
delete(dataId: any): boolean;
numDataIds(): number;
};
nextDataId(): number;
numDataIds(): number;
write(values: any, shape: any, dtype: any): {
id: number;
};
refCount(dataId: any): any;
incRef(dataId: any): void;
decRef(dataId: any): void;
move(dataId: any, values: any, shape: any, dtype: any, refCount: any): void;
disposeIntermediateTensorInfo(tensorInfo: any): void;
readSync(dataId: any): any;
read(dataId: any): any;
bufferSync(t: any): {
dtype: any;
shape: any;
size: any;
values: any;
strides: any[];
set(value: any, ...locs: any[]): void;
get(...locs: any[]): any;
locToIndex(locs: any): any;
indexToLoc(index: any): any[];
readonly rank: any;
toTensor(): any;
};
checkNumericalProblems(values: any): void;
getValuesFromTexture(dataId: any): any;
timerAvailable(): boolean;
time(f: any): Promise<{
uploadWaitMs: number;
downloadWaitMs: number;
kernelMs: null;
wallMs: null;
}>;
programTimersStack: any[] | null | undefined;
activeTimers: any;
memory(): {
unreliable: boolean;
numBytesInGPU: number;
numBytesInGPUAllocated: number;
numBytesInGPUFree: number;
};
startTimer(): any;
endTimer(query: any): any;
getQueryTime(query: any): Promise<any>;
disposeData(dataId: any, force?: boolean): boolean;
releaseGPUData(dataId: any): void;
getTexture(dataId: any): any;
getDataInfo(dataId: any): any;
shouldExecuteOnCPU(inputs: any, sizeThreshold?: any): any;
getGPGPUContext(): any;
where(condition: any): any;
packedUnaryOp(x: any, op2: any, dtype: any): any;
abs(x: any): any;
makeTensorInfo(shape: any, dtype: any, values: any): {
dataId: {
id: number;
};
shape: any;
dtype: any;
};
makeOutput(shape: any, dtype: any, values: any): any;
unpackTensor(input2: any): any;
packTensor(input2: any): any;
packedReshape(input2: any, afterShape: any): {
dataId: any;
shape: any;
dtype: any;
};
decode(dataId: any): {
dtype: any;
shape: any;
dataId: any;
};
runWebGLProgram(program: any, inputs: any, outputDtype: any, customUniformValues: any, preventEagerUnpackingOfOutput?: boolean): any;
compileAndRun(program: any, inputs: any, outputDtype: any, customUniformValues: any, preventEagerUnpackingOfOutput?: boolean): any;
getAndSaveBinary(key: any, getBinary: any): any;
getTextureManager(): {
gpgpu: any;
numUsedTextures: number;
numFreeTextures: number;
_numBytesAllocated: number;
_numBytesFree: number;
freeTextures: {};
logEnabled: boolean;
usedTextures: {};
acquireTexture(shapeRC: any, usage: any, isPacked: any): any;
releaseTexture(texture: any, shape: any, logicalTexType: any, isPacked: any): void;
log(): void;
readonly numBytesAllocated: number;
readonly numBytesFree: number;
getNumUsedTextures(): number;
getNumFreeTextures(): number;
dispose(): void;
};
dispose(): void;
floatPrecision(): any;
floatPrecisionValue: any;
epsilon(): number;
uploadToGPU(dataId: any): void;
convertAndCacheOnCPU(dataId: any, float32Values: any): any;
acquireTexture(texShape: any, texType: any, dtype: any, isPacked: any): any;
computeBytes(shape: any, dtype: any): number;
};
nextDataId: number;
};
export var Max: string;
export var MaxPool: string;
export var MaxPool3D: string;
export var MaxPool3DGrad: string;
export var MaxPoolGrad: string;
export var MaxPoolWithArgmax: string;
export var Maximum: string;
export var Mean: string;
export var Min: string;
export var Minimum: string;
export var MirrorPad: string;
export var Mod: string;
export var MomentumOptimizer: {
new (learningRate: any, momentum: any, useNesterov?: boolean): {
learningRate: any;
momentum: any;
useNesterov: boolean;
accumulations: any[];
m: any;
applyGradients(variableGradients: any): void;
dispose(): void;
setMomentum(momentum: any): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
momentum: any;
useNesterov: boolean;
};
setLearningRate(learningRate: any): void;
c: any;
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
fromConfig(cls: any, config: any): any;
className: string;
};
export var Multinomial: string;
export var Multiply: string;
export var Neg: string;
export var NonMaxSuppressionV3: string;
export var NonMaxSuppressionV4: string;
export var NonMaxSuppressionV5: string;
export var NotEqual: string;
export var OP_SCOPE_SUFFIX: string;
export var OneHot: string;
export var OnesLike: string;
export var Optimizer: {
new (): {
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
dispose(): void;
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
getWeights(): Promise<void>;
setWeights(weightValues: any): Promise<void>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
fromConfig(cls: any, config: any): any;
};
export var Pack: string;
export var PadV2: string;
export var Pool: string;
export var Pow: string;
export var Prelu: string;
export var Prod: string;
export var RMSPropOptimizer: {
new (learningRate: any, decay?: number, momentum?: number, epsilon3?: null, centered?: boolean): {
learningRate: any;
decay: number;
momentum: number;
epsilon: any;
accumulatedMeanSquares: any[];
accumulatedMoments: any[];
accumulatedMeanGrads: any[];
centered: boolean;
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
decay: number;
momentum: number;
epsilon: any;
centered: boolean;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
fromConfig(cls: any, config: any): any;
className: string;
};
export var RNN: {
new (args: any): {
cell: any;
returnSequences: any;
returnState: any;
goBackwards: any;
_stateful: any;
unroll: any;
supportsMasking: boolean;
inputSpec: {
dtype: any;
shape: any;
ndim: any;
maxNDim: any;
minNDim: any;
axes: any;
}[];
stateSpec: any;
states_: any;
numConstants: any;
keptStates: any[];
getStates(): any;
setStates(states: any): void;
computeOutputShape(inputShape: any): any[];
computeMask(inputs: any, mask: any): any;
states: any;
build(inputShape: any): void;
resetStates(states: any, training?: boolean): void;
apply(inputs: any, kwargs: any): any;
call(inputs: any, kwargs: any): any;
getInitialState(inputs: any): any;
readonly trainableWeights: any;
readonly nonTrainableWeights: any;
setFastWeightInitDuringBuild(value: any): void;
getConfig(): any;
_callHook: any;
_addedWeightNames: any[];
id: number;
activityRegularizer: any;
_trainableWeights: any[];
_nonTrainableWeights: any[];
_losses: any[];
_updates: any[];
_built: boolean;
inboundNodes: any[];
outboundNodes: any[];
name: any;
trainable_: any;
batchInputShape: any;
dtype: any;
initialWeights: any;
_refCount: number | null;
fastWeightInitDuringBuild: boolean;
getNodeAtIndex(nodeIndex: any, attrName: any): any;
getInputAt(nodeIndex: any): any;
getOutputAt(nodeIndex: any): any;
readonly input: any;
readonly output: any;
readonly losses: any[];
calculateLosses(): any[];
readonly updates: any[];
built: boolean;
trainable: any;
readonly weights: any[];
readonly stateful: boolean;
assertInputCompatibility(inputs: any): void;
invokeCallHook(inputs: any, kwargs: any): void;
setCallHook(callHook: any): void;
clearCallHook(): void;
warnOnIncompatibleInputShape(inputShape: any): void;
readonly outputShape: any;
countParams(): number;
getWeights(trainableOnly?: boolean): any;
setWeights(weights: any): void;
addWeight(name: any, shape: any, dtype: any, initializer: any, regularizer: any, trainable: any, constraint: any): {
dtype: string;
shape: any;
id: number;
originalName: string;
name: any;
trainable_: boolean;
constraint: any;
val: any;
read(): any;
write(newVal: any): any;
dispose(): void;
assertNotDisposed(): void;
trainable: boolean;
};
addLoss(losses4: any): void;
addInboundNode(inputTensors: any, outputTensors: any, inputMasks: any, outputMasks: any, inputShapes: any, outputShapes: any, kwargs?: null): void;
disposeWeights(): number;
assertNotDisposed(): void;
dispose(): {
refCountAfterDispose: number;
numDisposedVariables: number;
};
};
fromConfig(cls: any, config: any, customObjects?: {}): any;
className: string;
nodeKey(layer: any, nodeIndex: any): string;
};
export var Range: string;
export var Rank: any;
export var Real: string;
export var RealDiv: string;
export var Reciprocal: string;
export var Reduction: any;
export var Relu: string;
export var Relu6: string;
export var Reshape: string;
export var ResizeBilinear: string;
export var ResizeBilinearGrad: string;
export var ResizeNearestNeighbor: string;
export var ResizeNearestNeighborGrad: string;
export var Reverse: string;
export var RotateWithOffset: string;
export var Round: string;
export var Rsqrt: string;
export var SGDOptimizer: {
new (learningRate: any): {
learningRate: any;
applyGradients(variableGradients: any): void;
setLearningRate(learningRate: any): void;
c: any;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
fromConfig(cls: any, config: any): any;
className: string;
};
export var ScatterNd: string;
export var Select: string;
export var Selu: string;
export var Sequential: any;
export var Sigmoid: string;
export var Sign: string;
export var Sin: string;
export var Sinh: string;
export var Slice: string;
export var Softmax: string;
export var Softplus: string;
export var SpaceToBatchND: string;
export var SparseFillEmptyRows: string;
export var SparseReshape: string;
export var SparseSegmentMean: string;
export var SparseSegmentSum: string;
export var SparseToDense: string;
export var SplitV: string;
export var Sqrt: string;
export var Square: string;
export var SquaredDifference: string;
export var Step: string;
export var StridedSlice: string;
export var StringNGrams: string;
export var StringSplit: string;
export var StringToHashBucketFast: string;
export var Sub: string;
export var Sum: string;
export var SymbolicTensor: {
new (dtype: any, shape: any, sourceLayer: any, inputs: any, callArgs: any, name: any, outputTensorIndex: any): {
dtype: any;
shape: any;
sourceLayer: any;
inputs: any;
callArgs: any;
outputTensorIndex: any;
id: number;
originalName: string | undefined;
name: any;
rank: any;
};
};
export var Tan: string;
export var Tanh: string;
export var Tensor: {
new (shape: any, dtype: any, dataId: any, id: any): {
kept: boolean;
isDisposedInternal: boolean;
shape: any;
dtype: any;
size: any;
strides: any[];
dataId: any;
id: any;
rankType: any;
readonly rank: any;
buffer(): Promise<any>;
bufferSync(): any;
array(): Promise<any>;
arraySync(): any;
data(): Promise<any>;
dataSync(): any;
bytes(): Promise<any>;
dispose(): void;
readonly isDisposed: boolean;
throwIfDisposed(): void;
print(verbose?: boolean): any;
clone(): any;
toString(verbose?: boolean): string;
cast(dtype: any): any;
variable(trainable: boolean | undefined, name: any, dtype: any): any;
};
};
export var TensorBuffer: {
new (shape: any, dtype: any, values: any): {
dtype: any;
shape: any;
size: any;
values: any;
strides: any[];
set(value: any, ...locs: any[]): void;
get(...locs: any[]): any;
locToIndex(locs: any): any;
indexToLoc(index: any): any[];
readonly rank: any;
toTensor(): any;
};
};
export var Tile: string;
export var TopK: string;
export var Transform: string;
export var Transpose: string;
export var Unique: string;
export var Unpack: string;
export var UnsortedSegmentSum: string;
export var Variable: {
new (initialValue: any, trainable: any, name: any, tensorId: any): {
trainable: any;
name: any;
assign(newValue: any): void;
dataId: any;
dispose(): void;
isDisposedInternal: boolean;
kept: boolean;
shape: any;
dtype: any;
size: any;
strides: any[];
id: any;
rankType: any;
readonly rank: any;
buffer(): Promise<any>;
bufferSync(): any;
array(): Promise<any>;
arraySync(): any;
data(): Promise<any>;
dataSync(): any;
bytes(): Promise<any>;
readonly isDisposed: boolean;
throwIfDisposed(): void;
print(verbose?: boolean): any;
clone(): any;
toString(verbose?: boolean): string;
cast(dtype: any): any;
variable(trainable: boolean | undefined, name: any, dtype: any): any;
};
};
export var ZerosLike: string;
export var _FusedMatMul: string;
export function abs(...args: any[]): any;
export namespace abs {
const name: string;
}
export function acos(...args: any[]): any;
export namespace acos { }
export function acosh(...args: any[]): any;
export namespace acosh { }
declare function add2(...args: any[]): any;
declare namespace add2 { }
export function addN(...args: any[]): any;
export namespace addN { }
export function all(...args: any[]): any;
export namespace all { }
export function any(...args: any[]): any;
export namespace any { }
export function argMax(...args: any[]): any;
export namespace argMax { }
export function argMin(...args: any[]): any;
export namespace argMin { }
export function asin(...args: any[]): any;
export namespace asin { }
export function asinh(...args: any[]): any;
export namespace asinh { }
export function atan(...args: any[]): any;
export namespace atan { }
export function atan2(...args: any[]): any;
export namespace atan2 { }
export function atanh(...args: any[]): any;
export namespace atanh { }
export function avgPool(...args: any[]): any;
export namespace avgPool { }
export function avgPool3d(...args: any[]): any;
export namespace avgPool3d { }
export function backend(): any;
declare var backend_util_exports: {};
export function basicLSTMCell(...args: any[]): any;
export namespace basicLSTMCell { }
export function batchNorm(...args: any[]): any;
export namespace batchNorm { }
export function batchNorm2d(...args: any[]): any;
export namespace batchNorm2d { }
export function batchNorm3d(...args: any[]): any;
export namespace batchNorm3d { }
export function batchNorm4d(...args: any[]): any;
export namespace batchNorm4d { }
export function batchToSpaceND(...args: any[]): any;
export namespace batchToSpaceND { }
export function bincount(...args: any[]): any;
export namespace bincount { }
export function booleanMaskAsync(tensor2: any, mask: any, axis: any): Promise<any>;
export function broadcastArgs(...args: any[]): any;
export namespace broadcastArgs { }
export function broadcastTo(...args: any[]): any;
export namespace broadcastTo { }
declare var browser_exports: {};
export function buffer(shape: any, dtype: string | undefined, values: any): {
dtype: any;
shape: any;
size: any;
values: any;
strides: any[];
set(value: any, ...locs: any[]): void;
get(...locs: any[]): any;
locToIndex(locs: any): any;
indexToLoc(index: any): any[];
readonly rank: any;
toTensor(): any;
};
export namespace callbacks {
export { earlyStopping };
}
export function cast(...args: any[]): any;
export namespace cast { }
export function ceil(...args: any[]): any;
export namespace ceil { }
export function clipByValue(...args: any[]): any;
export namespace clipByValue { }
export function clone(...args: any[]): any;
export namespace clone { }
export function complex(...args: any[]): any;
export namespace complex { }
export function concat(...args: any[]): any;
export namespace concat { }
export function concat1d(...args: any[]): any;
export namespace concat1d { }
export function concat2d(...args: any[]): any;
export namespace concat2d { }
export function concat3d(...args: any[]): any;
export namespace concat3d { }
export function concat4d(...args: any[]): any;
export namespace concat4d { }
declare var exports_constraints_exports: {};
export function conv1d(...args: any[]): any;
export namespace conv1d { }
export function conv2d(...args: any[]): any;
export namespace conv2d { }
export function conv2dTranspose(...args: any[]): any;
export namespace conv2dTranspose { }
export function conv3d(...args: any[]): any;
export namespace conv3d { }
export function conv3dTranspose(...args: any[]): any;
export namespace conv3dTranspose { }
export function copyRegisteredKernels(registeredBackendName: any, newBackendName: any): void;
export function cos(...args: any[]): any;
export namespace cos { }
export function cosh(...args: any[]): any;
export namespace cosh { }
export function cosineWindow(windowLength: any, a: any, b: any): any;
export function cumsum(...args: any[]): any;
export namespace cumsum { }
export function customGrad(f: any): any;
declare var dist_exports: {};
export function denseBincount(...args: any[]): any;
export namespace denseBincount { }
export function deprecationWarn(msg: any): void;
export function depthToSpace(...args: any[]): any;
export namespace depthToSpace { }
export function depthwiseConv2d(...args: any[]): any;
export namespace depthwiseConv2d { }
export function deregisterOp(name: any): void;
declare var device_util_exports: {};
export function diag(...args: any[]): any;
export namespace diag { }
export function dilation2d(...args: any[]): any;
export namespace dilation2d { }
export function disableDeprecationWarnings(): void;
export function dispose(container: any): void;
export function disposeVariables(): void;
export function div(...args: any[]): any;
export namespace div { }
export function divNoNan(...args: any[]): any;
export namespace divNoNan { }
export function dot(...args: any[]): any;
export namespace dot { }
export function dropout(...args: any[]): any;
export namespace dropout { }
export function einsum(...args: any[]): any;
export namespace einsum { }
export function elu(...args: any[]): any;
export namespace elu { }
export function enableDebugMode(): void;
export function enableProdMode(): void;
export function enclosingPowerOfTwo(value: any): number;
export function engine(): any;
export function env(): any;
export function equal(...args: any[]): any;
export namespace equal { }
export function erf(...args: any[]): any;
export namespace erf { }
export function exp(...args: any[]): any;
export namespace exp { }
export function expandDims(...args: any[]): any;
export namespace expandDims { }
export function expm1(...args: any[]): any;
export namespace expm1 { }
export function eye(...args: any[]): any;
export namespace eye { }
export function fft(...args: any[]): any;
export namespace fft { }
export function fill(shape: any, value: any, dtype: any): any;
export function findBackend(name: any): any;
export function findBackendFactory(name: any): any;
export function floor(...args: any[]): any;
export namespace floor { }
export function floorDiv(...args: any[]): any;
export namespace floorDiv { }
export function forceHalfFloat(): void;
declare var fused_ops_exports: {};
export function gather(...args: any[]): any;
export namespace gather { }
export function gatherND(...args: any[]): any;
export namespace gatherND { }
declare var gather_nd_util_exports: {};
export function getBackend(): any;
export function getGradient(kernelName: any): any;
export function getKernel(kernelName: any, backendName: any): any;
export function getKernelsForBackend(backendName: any): any[];
declare var gpgpu_util_exports: {};
export function grad(f: any): (x: any, dy: any) => any;
export function grads(f: any): (args: any, dy: any) => any;
export function greater(...args: any[]): any;
export namespace greater { }
export function greaterEqual(...args: any[]): any;
export namespace greaterEqual { }
export function ifft(...args: any[]): any;
export namespace ifft { }
export function imag(...args: any[]): any;
export namespace imag { }
export namespace image {
export { flipLeftRight };
export { grayscaleToRGB };
export { resizeNearestNeighbor };
export { resizeBilinear };
export { rotateWithOffset };
export { cropAndResize };
export { nonMaxSuppression };
export { nonMaxSuppressionAsync };
export { nonMaxSuppressionWithScore };
export { nonMaxSuppressionWithScoreAsync };
export { nonMaxSuppressionPadded };
export { nonMaxSuppressionPaddedAsync };
export { threshold };
export { transform };
}
export function inTopKAsync(predictions: any, targets: any, k?: number): Promise<any>;
declare var exports_initializers_exports: {};
export function input(config: any): any;
declare var io_exports: {};
export function irfft(...args: any[]): any;
export namespace irfft { }
declare function isFinite2(...args: any[]): any;
declare namespace isFinite2 { }
export function isInf(...args: any[]): any;
export namespace isInf { }
declare function isNaN2(...args: any[]): any;
declare namespace isNaN2 { }
export function keep(result: any): any;
declare var kernel_impls_exports: {};
declare var exports_layers_exports: {};
export function leakyRelu(...args: any[]): any;
export namespace leakyRelu { }
export function less(...args: any[]): any;
export namespace less { }
export function lessEqual(...args: any[]): any;
export namespace lessEqual { }
export namespace linalg {
export { bandPart };
export { gramSchmidt };
export { qr };
}
export function linspace(start: any, stop: any, num: any): any;
export function loadGraphModel(modelUrl: any, options?: {}): Promise<{
modelUrl: any;
loadOptions: {};
version: string;
resourceManager: {
hashTableNameToHandle: {};
hashTableMap: {};
addHashTable(name: any, hashTable2: any): void;
getHashTableHandleByName(name: any): any;
getHashTableById(id: any): any;
dispose(): void;
};
readonly modelVersion: string;
readonly inputNodes: any;
readonly outputNodes: any;
readonly inputs: any;
readonly outputs: any;
readonly weights: any;
readonly metadata: any;
readonly modelSignature: any;
findIOHandler(): void;
handler: any;
load(): Promise<boolean>;
loadSync(artifacts: any): boolean;
artifacts: any;
signature: any;
executor: {
graph: any;
parent: any;
compiledMap: Map<any, any>;
_weightMap: {};
SEPERATOR: string;
_functions: any;
_functionExecutorMap: {};
_outputs: any;
_inputs: any;
_initNodes: any;
_signature: any;
readonly weightIds: any;
readonly functionExecutorMap: any;
weightMap: any;
_weightIds: any[] | undefined;
resourceManager: any;
_resourceManager: any;
readonly inputs: any;
readonly outputs: any;
readonly inputNodes: any;
readonly outputNodes: any;
readonly functions: {};
getCompilationKey(inputs: any, outputs: any): string;
compile(inputs: any, outputs: any): any[];
execute(inputs: any, outputs: any): any;
getFrozenTensorIds(tensorMap: any): Set<any>;
checkTensorForDisposal(nodeName: any, node: any, tensorMap: any, context: any, tensorsToKeep: any, outputNames: any, intermediateTensorConsumerCount: any): void;
executeAsync(inputs: any, outputs: any): Promise<any>;
_executeAsync(inputs: any, outputs: any, isFunctionExecution?: boolean, tensorArrayMap?: {}, tensorListMap?: {}): Promise<any>;
executeFunctionAsync(inputs: any, tensorArrayMap: any, tensorListMap: any): Promise<any>;
executeWithControlFlow(inputs: any, context: any, outputNames: any, isFunctionExecution: any): Promise<any>;
processStack(inputNodes: any, stack2: any, context: any, tensorMap: any, added: any, tensorsToKeep: any, outputNames: any, intermediateTensorConsumerCount: any, usedNodes: any): any[];
processChildNodes(node: any, stack2: any, context: any, tensorMap: any, added: any, usedNodes: any): void;
dispose(): void;
checkInputShapeAndType(inputs: any): void;
mapInputs(inputs: any): {};
checkInputs(inputs: any): void;
mapOutputs(outputs: any): any;
checkOutputs(outputs: any): void;
} | undefined;
initializer: {
graph: any;
parent: any;
compiledMap: Map<any, any>;
_weightMap: {};
SEPERATOR: string;
_functions: any;
_functionExecutorMap: {};
_outputs: any;
_inputs: any;
_initNodes: any;
_signature: any;
readonly weightIds: any;
readonly functionExecutorMap: any;
weightMap: any;
_weightIds: any[] | undefined;
resourceManager: any;
_resourceManager: any;
readonly inputs: any;
readonly outputs: any;
readonly inputNodes: any;
readonly outputNodes: any;
readonly functions: {};
getCompilationKey(inputs: any, outputs: any): string;
compile(inputs: any, outputs: any): any[];
execute(inputs: any, outputs: any): any;
getFrozenTensorIds(tensorMap: any): Set<any>;
checkTensorForDisposal(nodeName: any, node: any, tensorMap: any, context: any, tensorsToKeep: any, outputNames: any, intermediateTensorConsumerCount: any): void;
executeAsync(inputs: any, outputs: any): Promise<any>;
_executeAsync(inputs: any, outputs: any, isFunctionExecution?: boolean, tensorArrayMap?: {}, tensorListMap?: {}): Promise<any>;
executeFunctionAsync(inputs: any, tensorArrayMap: any, tensorListMap: any): Promise<any>;
executeWithControlFlow(inputs: any, context: any, outputNames: any, isFunctionExecution: any): Promise<any>;
processStack(inputNodes: any, stack2: any, context: any, tensorMap: any, added: any, tensorsToKeep: any, outputNames: any, intermediateTensorConsumerCount: any, usedNodes: any): any[];
processChildNodes(node: any, stack2: any, context: any, tensorMap: any, added: any, usedNodes: any): void;
dispose(): void;
checkInputShapeAndType(inputs: any): void;
mapInputs(inputs: any): {};
checkInputs(inputs: any): void;
mapOutputs(outputs: any): any;
checkOutputs(outputs: any): void;
} | undefined;
save(handlerOrURL: any, config: any): Promise<any>;
predict(inputs: any, config: any): any;
normalizeInputs(inputs: any): any;
normalizeOutputs(outputs: any): any[];
execute(inputs: any, outputs: any): any;
executeAsync(inputs: any, outputs: any): Promise<any>;
convertTensorMapToTensorsMap(map: any): {};
dispose(): void;
}>;
export function loadLayersModel(pathOrIOHandler: any, options: any): Promise<any>;
export function localResponseNormalization(...args: any[]): any;
export namespace localResponseNormalization { }
declare function log5(...args: any[]): any;
declare namespace log5 { }
export function log1p(...args: any[]): any;
export namespace log1p { }
export function logSigmoid(...args: any[]): any;
export namespace logSigmoid { }
export function logSoftmax(...args: any[]): any;
export namespace logSoftmax { }
export function logSumExp(...args: any[]): any;
export namespace logSumExp { }
export function logicalAnd(...args: any[]): any;
export namespace logicalAnd { }
export function logicalNot(...args: any[]): any;
export namespace logicalNot { }
export function logicalOr(...args: any[]): any;
export namespace logicalOr { }
export function logicalXor(...args: any[]): any;
export namespace logicalXor { }
export namespace losses {
export { absoluteDifference };
export { computeWeightedLoss };
export { cosineDistance };
export { hingeLoss };
export { huberLoss };
export { logLoss };
export { meanSquaredError };
export { sigmoidCrossEntropy };
export { softmaxCrossEntropy };
}
export function matMul(...args: any[]): any;
export namespace matMul { }
declare var math_exports: {};
export function max(...args: any[]): any;
export namespace max { }
export function maxPool(...args: any[]): any;
export namespace maxPool { }
export function maxPool3d(...args: any[]): any;
export namespace maxPool3d { }
export function maxPoolWithArgmax(...args: any[]): any;
export namespace maxPoolWithArgmax { }
export function maximum(...args: any[]): any;
export namespace maximum { }
export function mean(...args: any[]): any;
export namespace mean { }
export function memory(): any;
export function meshgrid(x: any, y: any, { indexing }?: {
indexing?: string | undefined;
}): any[];
declare var exports_metrics_exports: {};
export function min(...args: any[]): any;
export namespace min { }
export function minimum(...args: any[]): any;
export namespace minimum { }
export function mirrorPad(...args: any[]): any;
export namespace mirrorPad { }
export function mod(...args: any[]): any;
export namespace mod { }
export function model(args: any): {
[x: string]: any;
isTraining: boolean;
summary(lineLength: any, positions: any, printFn?: {
(...data: any[]): void;
(...data: any[]): void;
(message?: any, ...optionalParams: any[]): void;
}): void;
compile(args: any): void;
loss: any;
optimizer_: any;
isOptimizerOwned: boolean | undefined;
lossFunctions: any;
feedOutputNames: any[] | undefined;
feedOutputShapes: any[] | undefined;
feedLossFns: any[] | undefined;
metrics: any;
metricsNames: string[] | undefined;
metricsTensors: any[] | undefined;
collectedTrainableWeights: any;
checkTrainableWeightsConsistency(): void;
evaluate(x: any, y: any, args?: {}): any;
evaluateDataset(dataset: any, args: any): Promise<any>;
checkNumSamples(ins: any, batchSize: any, steps: any, stepsName?: string): any;
execute(inputs: any, outputs: any): any;
retrieveSymbolicTensors(symbolicTensorNames: any): any[];
predictLoop(ins: any, batchSize?: number, verbose?: boolean): any;
predict(x: any, args?: {}): any;
predictOnBatch(x: any): any;
standardizeUserDataXY(x: any, y: any, checkBatchAxis: boolean | undefined, batchSize: any): any[];
standardizeUserData(x: any, y: any, sampleWeight: any, classWeight: any, checkBatchAxis: boolean | undefined, batchSize: any): Promise<any[]>;
testLoop(f: any, ins: any, batchSize: any, verbose: number | undefined, steps: any): any;
getDedupedMetricsNames(): string[];
makeTrainFunction(): (data: any) => any[];
makeTestFunction(): void;
testFunction: ((data: any) => any) | undefined;
fit(x: any, y: any, args?: {}): Promise<any>;
fitDataset(dataset: any, args: any): Promise<any>;
trainOnBatch(x: any, y: any): Promise<any>;
getNamedWeights(config: any): {
name: any;
tensor: any;
}[];
stopTraining: any;
stopTraining_: any;
optimizer: any;
dispose(): any;
getLossIdentifiers(): any;
getMetricIdentifiers(): {};
getTrainingConfig(): {
loss: any;
metrics: {};
optimizer_config: {
class_name: any;
config: any;
};
};
loadTrainingConfig(trainingConfig: any): void;
save(handlerOrURL: any, config: any): Promise<any>;
setUserDefinedMetadata(userDefinedMetadata: any): void;
userDefinedMetadata: any;
getUserDefinedMetadata(): any;
};
declare var exports_models_exports: {};
export function moments(...args: any[]): any;
export namespace moments { }
export function movingAverage(...args: any[]): any;
export namespace movingAverage { }
export function mul(...args: any[]): any;
export namespace mul { }
export function multiRNNCell(...args: any[]): any;
export namespace multiRNNCell { }
export function multinomial(...args: any[]): any;
export namespace multinomial { }
export function neg(...args: any[]): any;
export namespace neg { }
export function nextFrame(): Promise<any>;
export function norm(...args: any[]): any;
export namespace norm { }
export function notEqual(...args: any[]): any;
export namespace notEqual { }
export function oneHot(...args: any[]): any;
export namespace oneHot { }
declare function ones2(shape: any, dtype?: string): any;
export function onesLike(...args: any[]): any;
export namespace onesLike { }
export function op(f: any): {
(...args: any[]): any;
readonly name: string;
};
export function outerProduct(...args: any[]): any;
export namespace outerProduct { }
export function pad(...args: any[]): any;
export namespace pad { }
export function pad1d(...args: any[]): any;
export namespace pad1d { }
export function pad2d(...args: any[]): any;
export namespace pad2d { }
export function pad3d(...args: any[]): any;
export namespace pad3d { }
export function pad4d(...args: any[]): any;
export namespace pad4d { }
export function pool(...args: any[]): any;
export namespace pool { }
export function pow(...args: any[]): any;
export namespace pow { }
export function prelu(...args: any[]): any;
export namespace prelu { }
declare function print2(x: any, verbose?: boolean): void;
export function prod(...args: any[]): any;
export namespace prod { }
export function profile(f: any): any;
export function rand(...args: any[]): any;
export namespace rand { }
export function randomGamma(...args: any[]): any;
export namespace randomGamma { }
export function randomNormal(...args: any[]): any;
export namespace randomNormal { }
export function randomUniform(...args: any[]): any;
export namespace randomUniform { }
export function range(start: any, stop: any, step5?: number, dtype?: string): any;
export function ready(): any;
export function real(...args: any[]): any;
export namespace real { }
export function reciprocal(...args: any[]): any;
export namespace reciprocal { }
export function registerBackend(name: any, factory: any, priority?: number): any;
export function registerCallbackConstructor(verbosityLevel: any, callbackConstructor: any): void;
export function registerGradient(config: any): void;
export function registerKernel(config: any): void;
export function registerOp(name: any, opFunc: any): void;
declare var exports_regularizers_exports: {};
export function relu(...args: any[]): any;
export namespace relu { }
export function relu6(...args: any[]): any;
export namespace relu6 { }
export function removeBackend(name: any): void;
export function reshape(...args: any[]): any;
export namespace reshape { }
export function reverse(...args: any[]): any;
export namespace reverse { }
export function reverse1d(...args: any[]): any;
export namespace reverse1d { }
export function reverse2d(...args: any[]): any;
export namespace reverse2d { }
export function reverse3d(...args: any[]): any;
export namespace reverse3d { }
export function reverse4d(...args: any[]): any;
export namespace reverse4d { }
export function rfft(...args: any[]): any;
export namespace rfft { }
declare function round2(...args: any[]): any;
declare namespace round2 { }
export function rsqrt(...args: any[]): any;
export namespace rsqrt { }
export function scalar(value: any, dtype: any): any;
export function scatterND(...args: any[]): any;
export namespace scatterND { }
declare var scatter_nd_util_exports: {};
export function selu(...args: any[]): any;
export namespace selu { }
export function separableConv2d(...args: any[]): any;
export namespace separableConv2d { }
export function sequential(config: any): any;
declare var serialization_exports: {};
export function setBackend(backendName: any): any;
export function setPlatform(platformName: any, platform: any): void;
export function setWasmPath(path: any, usePlatformFetch?: boolean): void;
export function setWasmPaths(prefixOrFileMap: any, usePlatformFetch?: boolean): void;
export function setWebGLContext(webGLVersion: any, gl: any): void;
export function setdiff1dAsync(x: any, y: any): Promise<any[]>;
export function sigmoid(...args: any[]): any;
export namespace sigmoid { }
export function sign(...args: any[]): any;
export namespace sign { }
export namespace signal {
export { hammingWindow };
export { hannWindow };
export { frame };
export { stft };
}
export function sin(...args: any[]): any;
export namespace sin { }
export function sinh(...args: any[]): any;
export namespace sinh { }
export function slice(...args: any[]): any;
export namespace slice { }
export function slice1d(...args: any[]): any;
export namespace slice1d { }
export function slice2d(...args: any[]): any;
export namespace slice2d { }
export function slice3d(...args: any[]): any;
export namespace slice3d { }
export function slice4d(...args: any[]): any;
export namespace slice4d { }
declare var slice_util_exports: {};
export function softmax(...args: any[]): any;
export namespace softmax { }
export function softplus(...args: any[]): any;
export namespace softplus { }
export function spaceToBatchND(...args: any[]): any;
export namespace spaceToBatchND { }
export namespace sparse {
export { sparseFillEmptyRows };
export { sparseReshape };
export { sparseSegmentMean };
export { sparseSegmentSum };
}
export function sparseToDense(...args: any[]): any;
export namespace sparseToDense { }
export namespace spectral {
export { fft };
export { ifft };
export { rfft };
export { irfft };
}
export function split(...args: any[]): any;
export namespace split { }
export function sqrt(...args: any[]): any;
export namespace sqrt { }
export function square(...args: any[]): any;
export namespace square { }
export function squaredDifference(...args: any[]): any;
export namespace squaredDifference { }
export function squeeze(...args: any[]): any;
export namespace squeeze { }
export function stack(...args: any[]): any;
export namespace stack { }
export function step(...args: any[]): any;
export namespace step { }
export function stridedSlice(...args: any[]): any;
export namespace stridedSlice { }
export namespace string {
export { stringNGrams };
export { stringSplit };
export { stringToHashBucketFast };
}
export function sub(...args: any[]): any;
export namespace sub { }
declare function sum2(...args: any[]): any;
declare namespace sum2 { }
export function sumOutType(type: any): any;
export function tan(...args: any[]): any;
export namespace tan { }
declare function tanh2(...args: any[]): any;
declare namespace tanh2 { }
export function tensor(values: any, shape: any, dtype: any): any;
export function tensor1d(values: any, dtype: any): any;
export function tensor2d(values: any, shape: any, dtype: any): any;
export function tensor3d(values: any, shape: any, dtype: any): any;
export function tensor4d(values: any, shape: any, dtype: any): any;
export function tensor5d(values: any, shape: any, dtype: any): any;
export function tensor6d(values: any, shape: any, dtype: any): any;
declare var tensor_util_exports: {};
declare var test_util_exports: {};
export function tidy(nameOrFn: any, fn: any): any;
export function tile(...args: any[]): any;
export namespace tile { }
export function time(f: any): any;
export function topk(...args: any[]): any;
export namespace topk { }
export namespace train {
import sgd = OptimizerConstructors.sgd;
export { sgd };
import momentum = OptimizerConstructors.momentum;
export { momentum };
import adadelta = OptimizerConstructors.adadelta;
export { adadelta };
import adagrad = OptimizerConstructors.adagrad;
export { adagrad };
import rmsprop = OptimizerConstructors.rmsprop;
export { rmsprop };
import adamax = OptimizerConstructors.adamax;
export { adamax };
import adam = OptimizerConstructors.adam;
export { adam };
}
export function transpose(...args: any[]): any;
export namespace transpose { }
export function truncatedNormal(...args: any[]): any;
export namespace truncatedNormal { }
export function unique(...args: any[]): any;
export namespace unique { }
export function unregisterGradient(kernelName: any): void;
export function unregisterKernel(kernelName: any, backendName: any): void;
export function unsortedSegmentSum(...args: any[]): any;
export namespace unsortedSegmentSum { }
export function unstack(...args: any[]): any;
export namespace unstack { }
export function upcastType(typeA: any, typeB: any): any;
declare var util_exports: {};
export function valueAndGrad(f: any): (x: any, dy: any) => {
grad: any;
value: any;
};
export function valueAndGrads(f: any): (args: any, dy: any) => any;
export function variable(initialValue: any, trainable: boolean | undefined, name: any, dtype: any): any;
export function variableGrads(f: any, varList: any): {
value: any;
grads: {};
};
declare var version92: {
tfjs: string;
"tfjs-core": string;
"tfjs-data": string;
"tfjs-layers": string;
"tfjs-converter": string;
"tfjs-backend-cpu": string;
"tfjs-backend-webgl": string;
"tfjs-backend-wasm": string;
};
declare var version3: string;
declare var version: string;
declare var version2: string;
declare var version8: string;
declare var version5: string;
export namespace webgl {
export { forceHalfFloat };
}
declare var webgl_util_exports: {};
export function where(...args: any[]): any;
export namespace where { }
export function whereAsync(condition: any): Promise<any>;
export function zeros(shape: any, dtype?: string): any;
export function zerosLike(...args: any[]): any;
export namespace zerosLike { }
declare function less2(currVal: any, prevVal: any): boolean;
declare function getQueryParams(queryString: any): {};
declare function earlyStopping(args: any): {
monitor: any;
minDelta: number;
patience: any;
verbose: any;
mode: any;
baseline: any;
monitorFunc: typeof less2;
onTrainBegin(logs: any): Promise<void>;
wait: number | undefined;
stoppedEpoch: any;
best: any;
onEpochEnd(epoch: any, logs: any): Promise<void>;
onTrainEnd(logs: any): Promise<void>;
getMonitorValue(logs: any): any;
model: {
[x: string]: any;
isTraining: boolean;
summary(lineLength: any, positions: any, printFn?: {
(...data: any[]): void;
(...data: any[]): void;
(message?: any, ...optionalParams: any[]): void;
}): void;
compile(args: any): void;
loss: any;
optimizer_: any;
isOptimizerOwned: boolean | undefined;
lossFunctions: any;
feedOutputNames: any[] | undefined;
feedOutputShapes: any[] | undefined;
feedLossFns: any[] | undefined;
metrics: any;
metricsNames: string[] | undefined;
metricsTensors: any[] | undefined;
collectedTrainableWeights: any;
checkTrainableWeightsConsistency(): void;
evaluate(x: any, y: any, args?: {}): any;
evaluateDataset(dataset: any, args: any): Promise<any>;
checkNumSamples(ins: any, batchSize: any, steps: any, stepsName?: string): any;
execute(inputs: any, outputs: any): any;
retrieveSymbolicTensors(symbolicTensorNames: any): any[];
predictLoop(ins: any, batchSize?: number, verbose?: boolean): any;
predict(x: any, args?: {}): any;
predictOnBatch(x: any): any;
standardizeUserDataXY(x: any, y: any, checkBatchAxis: boolean | undefined, batchSize: any): any[];
standardizeUserData(x: any, y: any, sampleWeight: any, classWeight: any, checkBatchAxis: boolean | undefined, batchSize: any): Promise<any[]>;
testLoop(f: any, ins: any, batchSize: any, verbose: number | undefined, steps: any): any;
getDedupedMetricsNames(): string[];
makeTrainFunction(): (data: any) => any[];
makeTestFunction(): void;
testFunction: ((data: any) => any) | undefined;
fit(x: any, y: any, args?: {}): Promise<any>;
fitDataset(dataset: any, args: any): Promise<any>;
trainOnBatch(x: any, y: any): Promise<any>;
getNamedWeights(config: any): {
name: any;
tensor: any;
}[];
stopTraining: any;
stopTraining_: any;
optimizer: any;
dispose(): any;
getLossIdentifiers(): any;
getMetricIdentifiers(): {};
getTrainingConfig(): {
loss: any;
metrics: {};
optimizer_config: {
class_name: any;
config: any;
};
};
loadTrainingConfig(trainingConfig: any): void;
save(handlerOrURL: any, config: any): Promise<any>;
setUserDefinedMetadata(userDefinedMetadata: any): void;
userDefinedMetadata: any;
getUserDefinedMetadata(): any;
} | null;
setModel(model2: any): void;
validationData: any;
setParams(params: any): void;
params: any;
onEpochBegin(epoch: any, logs: any): Promise<void>;
onBatchBegin(batch: any, logs: any): Promise<void>;
onBatchEnd(batch: any, logs: any): Promise<void>;
};
declare function flipLeftRight(...args: any[]): any;
declare namespace flipLeftRight { }
declare function grayscaleToRGB(...args: any[]): any;
declare namespace grayscaleToRGB { }
declare function resizeNearestNeighbor(...args: any[]): any;
declare namespace resizeNearestNeighbor { }
declare function resizeBilinear(...args: any[]): any;
declare namespace resizeBilinear { }
declare function rotateWithOffset(...args: any[]): any;
declare namespace rotateWithOffset { }
declare function cropAndResize(...args: any[]): any;
declare namespace cropAndResize { }
declare function nonMaxSuppression(...args: any[]): any;
declare namespace nonMaxSuppression { }
declare function nonMaxSuppressionAsync(boxes: any, scores: any, maxOutputSize: any, iouThreshold?: number, scoreThreshold?: number): Promise<any>;
declare function nonMaxSuppressionWithScore(...args: any[]): any;
declare namespace nonMaxSuppressionWithScore { }
declare function nonMaxSuppressionWithScoreAsync(boxes: any, scores: any, maxOutputSize: any, iouThreshold?: number, scoreThreshold?: number, softNmsSigma?: number): Promise<{
selectedIndices: any;
selectedScores: any;
}>;
declare function nonMaxSuppressionPadded(...args: any[]): any;
declare namespace nonMaxSuppressionPadded { }
declare function nonMaxSuppressionPaddedAsync(boxes: any, scores: any, maxOutputSize: any, iouThreshold?: number, scoreThreshold?: number, padToMaxOutputSize?: boolean): Promise<{
selectedIndices: any;
validOutputs: any;
}>;
declare function threshold(...args: any[]): any;
declare namespace threshold { }
declare function transform(...args: any[]): any;
declare namespace transform { }
declare function bandPart(...args: any[]): any;
declare namespace bandPart { }
declare function gramSchmidt(...args: any[]): any;
declare namespace gramSchmidt { }
declare function qr(...args: any[]): any;
declare namespace qr { }
declare function absoluteDifference(...args: any[]): any;
declare namespace absoluteDifference { }
declare function computeWeightedLoss(...args: any[]): any;
declare namespace computeWeightedLoss { }
declare function cosineDistance(...args: any[]): any;
declare namespace cosineDistance { }
declare function hingeLoss(...args: any[]): any;
declare namespace hingeLoss { }
declare function huberLoss(...args: any[]): any;
declare namespace huberLoss { }
declare function logLoss(...args: any[]): any;
declare namespace logLoss { }
declare function meanSquaredError(...args: any[]): any;
declare namespace meanSquaredError { }
declare function sigmoidCrossEntropy(...args: any[]): any;
declare namespace sigmoidCrossEntropy { }
declare function softmaxCrossEntropy(...args: any[]): any;
declare namespace softmaxCrossEntropy { }
declare function hammingWindow(...args: any[]): any;
declare namespace hammingWindow { }
declare function hannWindow(...args: any[]): any;
declare namespace hannWindow { }
declare function frame(...args: any[]): any;
declare namespace frame { }
declare function stft(...args: any[]): any;
declare namespace stft { }
declare function sparseFillEmptyRows(...args: any[]): any;
declare namespace sparseFillEmptyRows { }
declare function sparseReshape(...args: any[]): any;
declare namespace sparseReshape { }
declare function sparseSegmentMean(...args: any[]): any;
declare namespace sparseSegmentMean { }
declare function sparseSegmentSum(...args: any[]): any;
declare namespace sparseSegmentSum { }
declare function stringNGrams(...args: any[]): any;
declare namespace stringNGrams { }
declare function stringSplit(...args: any[]): any;
declare namespace stringSplit { }
declare function stringToHashBucketFast(...args: any[]): any;
declare namespace stringToHashBucketFast { }
declare var OptimizerConstructors: {
new (): {};
sgd(learningRate: any): {
learningRate: any;
applyGradients(variableGradients: any): void;
setLearningRate(learningRate: any): void;
c: any;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
momentum(learningRate: any, momentum: any, useNesterov?: boolean): {
learningRate: any;
momentum: any;
useNesterov: boolean;
accumulations: any[];
m: any;
applyGradients(variableGradients: any): void;
dispose(): void;
setMomentum(momentum: any): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
momentum: any;
useNesterov: boolean;
};
setLearningRate(learningRate: any): void;
c: any;
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
rmsprop(learningRate: any, decay?: number, momentum?: number, epsilon3?: null, centered?: boolean): {
learningRate: any;
decay: number;
momentum: number;
epsilon: any;
accumulatedMeanSquares: any[];
accumulatedMoments: any[];
accumulatedMeanGrads: any[];
centered: boolean;
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
decay: number;
momentum: number;
epsilon: any;
centered: boolean;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
adam(learningRate?: number, beta1?: number, beta2?: number, epsilon3?: null): {
learningRate: any;
beta1: any;
beta2: any;
epsilon: any;
accumulatedFirstMoment: any[];
accumulatedSecondMoment: any[];
accBeta1: any;
accBeta2: any;
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
beta1: any;
beta2: any;
epsilon: any;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
adadelta(learningRate?: number, rho?: number, epsilon3?: null): {
learningRate: any;
rho: any;
epsilon: any;
accumulatedGrads: any[];
accumulatedUpdates: any[];
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
rho: any;
epsilon: any;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
adamax(learningRate?: number, beta1?: number, beta2?: number, epsilon3?: null, decay?: number): {
learningRate: any;
beta1: any;
beta2: any;
epsilon: any;
decay: number;
accumulatedFirstMoment: any[];
accumulatedWeightedInfNorm: any[];
iteration: any;
accBeta1: any;
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<void>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
beta1: any;
beta2: any;
epsilon: any;
decay: number;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
adagrad(learningRate: any, initialAccumulatorValue?: number): {
learningRate: any;
initialAccumulatorValue: number;
accumulatedGrads: any[];
applyGradients(variableGradients: any): void;
dispose(): void;
getWeights(): Promise<{
name: string;
tensor: any;
}[]>;
setWeights(weightValues: any): Promise<void>;
getConfig(): {
learningRate: any;
initialAccumulatorValue: number;
};
minimize(f: any, returnCost: boolean | undefined, varList: any): any;
readonly iterations: any;
iterations_: any;
incrementIterations(): void;
computeGradients(f: any, varList: any): {
value: any;
grads: {};
};
saveIterations(): Promise<{
name: string;
tensor: any;
}>;
extractIterations(weightValues: any): Promise<any>;
getClassName(): any;
};
};
export { add2 as add, backend_util_exports as backend_util, browser_exports as browser, exports_constraints_exports as constraints, dist_exports as data, device_util_exports as device_util, fused_ops_exports as fused, gather_nd_util_exports as gather_util, gpgpu_util_exports as gpgpu_util, exports_initializers_exports as initializers, io_exports as io, isFinite2 as isFinite, isNaN2 as isNaN, kernel_impls_exports as kernel_impls, exports_layers_exports as layers, log5 as log, math_exports as math, exports_metrics_exports as metrics, exports_models_exports as models, ones2 as ones, print2 as print, exports_regularizers_exports as regularizers, round2 as round, scatter_nd_util_exports as scatter_util, serialization_exports as serialization, slice_util_exports as slice_util, sum2 as sum, tanh2 as tanh, tensor_util_exports as tensor_util, test_util_exports as test_util, util_exports as util, version92 as version, version3 as version_converter, version as version_core, version2 as version_layers, version8 as version_wasm, version5 as version_webgl, webgl_util_exports as webgl_util };
//# sourceMappingURL=tfjs.esm.d.ts.map
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