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- "use strict";
- Object.defineProperty(exports, "__esModule", { value: true });
- var tslib_1 = require("tslib");
- var tf = require("@tensorflow/tfjs-core");
- var convLayer_1 = require("./convLayer");
- function residual(x, params) {
- var out = convLayer_1.conv(x, params.conv1);
- out = convLayer_1.convNoRelu(out, params.conv2);
- out = tf.add(out, x);
- out = tf.relu(out);
- return out;
- }
- exports.residual = residual;
- function residualDown(x, params) {
- var out = convLayer_1.convDown(x, params.conv1);
- out = convLayer_1.convNoRelu(out, params.conv2);
- var pooled = tf.avgPool(x, 2, 2, 'valid');
- var zeros = tf.zeros(pooled.shape);
- var isPad = pooled.shape[3] !== out.shape[3];
- var isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
- if (isAdjustShape) {
- var padShapeX = tslib_1.__spreadArrays(out.shape);
- padShapeX[1] = 1;
- var zerosW = tf.zeros(padShapeX);
- out = tf.concat([out, zerosW], 1);
- var padShapeY = tslib_1.__spreadArrays(out.shape);
- padShapeY[2] = 1;
- var zerosH = tf.zeros(padShapeY);
- out = tf.concat([out, zerosH], 2);
- }
- pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;
- out = tf.add(pooled, out);
- out = tf.relu(out);
- return out;
- }
- exports.residualDown = residualDown;
- //# sourceMappingURL=residualLayer.js.map
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