@tensorflow/tfjs-core/dist/tape.js

Hardware-accelerated JavaScript library for machine intelligence

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var util = require("./util");
function getFilteredNodesXToY(tape, xs, y) {
    var tensorsFromX = {};
    var nodesFromX = {};
    for (var i = 0; i < xs.length; i++) {
        tensorsFromX[xs[i].id] = true;
    }
    for (var i = 0; i < tape.length; i++) {
        var node = tape[i];
        var nodeInputs = node.inputs;
        for (var inputName in nodeInputs) {
            var input = nodeInputs[inputName];
            var anyInputFromX = false;
            for (var j = 0; j < xs.length; j++) {
                if (tensorsFromX[input.id]) {
                    tensorsFromX[node.output.id] = true;
                    anyInputFromX = true;
                    nodesFromX[node.id] = true;
                    break;
                }
            }
            if (anyInputFromX) {
                break;
            }
        }
    }
    var tensorsLeadToY = {};
    tensorsLeadToY[y.id] = true;
    var nodesToY = {};
    for (var i = tape.length - 1; i >= 0; i--) {
        var node = tape[i];
        var nodeInputs = node.inputs;
        var outputs = [];
        outputs.push(node.output);
        for (var j = 0; j < outputs.length; j++) {
            if (tensorsLeadToY[outputs[j].id]) {
                for (var inputName in nodeInputs) {
                    tensorsLeadToY[nodeInputs[inputName].id] = true;
                    nodesToY[node.id] = true;
                }
                break;
            }
        }
    }
    var filteredTape = [];
    for (var i = 0; i < tape.length; i++) {
        var node = tape[i];
        if (nodesFromX[node.id] && nodesToY[node.id]) {
            var prunedInputs = {};
            for (var inputName in node.inputs) {
                var nodeInput = node.inputs[inputName];
                if (tensorsFromX[nodeInput.id]) {
                    prunedInputs[inputName] = nodeInput;
                }
            }
            var prunedNode = Object.assign({}, node);
            prunedNode.inputs = prunedInputs;
            prunedNode.output = node.output;
            filteredTape.push(prunedNode);
        }
    }
    return filteredTape;
}
exports.getFilteredNodesXToY = getFilteredNodesXToY;
function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape) {
    for (var i = filteredTape.length - 1; i >= 0; i--) {
        var node = filteredTape[i];
        var dy = tensorAccumulatedGradientMap[node.output.id];
        if (node.gradient == null) {
            throw new Error("Cannot compute gradient: gradient function not found " +
                ("for " + node.name + "."));
        }
        var inputGradients = node.gradient(dy);
        for (var inputName in node.inputs) {
            if (!(inputName in inputGradients)) {
                throw new Error("Cannot backprop through input " + inputName + ". " +
                    ("Available gradients found: " + Object.keys(inputGradients) + "."));
            }
            var dx = inputGradients[inputName]();
            var x = node.inputs[inputName];
            if (!util.arraysEqual(dx.shape, x.shape)) {
                throw new Error("Error in gradient for op " + node.name + ". The gradient of input " +
                    ("'" + inputName + "' has shape '" + dx.shape + "', which does not match ") +
                    ("the shape of the input '" + x.shape + "'"));
            }
            if (tensorAccumulatedGradientMap[x.id] == null) {
                tensorAccumulatedGradientMap[x.id] = dx;
            }
            else {
                var curGradient = tensorAccumulatedGradientMap[x.id];
                tensorAccumulatedGradientMap[x.id] = curGradient.add(dx);
                curGradient.dispose();
            }
        }
    }
}
exports.backpropagateGradients = backpropagateGradients;
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