"use strict";
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/**
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* @license
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* Copyright 2017 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (_) try {
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if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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var _this = this;
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("../index");
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var jasmine_util_1 = require("../jasmine_util");
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var test_util_1 = require("../test_util");
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jasmine_util_1.describeWithFlags('conv2dTranspose', jasmine_util_1.ALL_ENVS, function () {
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it('input=2x2x1,d2=1,f=2,s=1,p=0', function () { return __awaiter(_this, void 0, void 0, function () {
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var origInputDepth, origOutputDepth, inputShape, fSize, origPad, origStride, x, w, result, expected, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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origInputDepth = 1;
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origOutputDepth = 1;
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inputShape = [1, 1, origOutputDepth];
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fSize = 2;
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origPad = 0;
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origStride = 1;
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x = tf.tensor3d([2], inputShape);
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w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
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result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
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expected = [6, 2, 10, 0];
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expect(result.shape).toEqual([2, 2, 1]);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), expected]);
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return [2 /*return*/];
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}
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});
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}); });
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it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', function () { return __awaiter(_this, void 0, void 0, function () {
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var origInputDepth, origOutputDepth, inputShape, fSize, origPad, origStride, x, w, result, expected, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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origInputDepth = 1;
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origOutputDepth = 1;
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inputShape = [2, 1, 1, origOutputDepth];
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fSize = 2;
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origPad = 0;
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origStride = 1;
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x = tf.tensor4d([2, 3], inputShape);
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w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
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result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
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expected = [6, 2, 10, 0, 9, 3, 15, 0];
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expect(result.shape).toEqual([2, 2, 2, 1]);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), expected]);
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return [2 /*return*/];
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}
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});
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}); });
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// Reference (Python) TensorFlow code:
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//
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// ```py
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// import numpy as np
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// import tensorflow as tf
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//
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// tf.enable_eager_execution()
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//
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// x = tf.constant(np.array([[
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// [[-0.14656299], [0.32942239], [-1.90302866]],
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// [[-0.06487813], [-2.02637842], [-1.83669377]],
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// [[0.82650784], [-0.89249092], [0.01207666]]
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// ]]).astype(np.float32))
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// filt = tf.constant(np.array([
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// [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
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// [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
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// ]).astype(np.float32))
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//
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// with tf.GradientTape() as g:
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// g.watch(x)
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// g.watch(filt)
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// y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2])
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// print(y)
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// (x_grad, filt_grad) = g.gradient(y, [x, filt])
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//
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// print("x_grad = %s" % x_grad)
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// print("filt_grad = %s" % filt_grad)
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// ```
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it('gradient with clones input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid', function () { return __awaiter(_this, void 0, void 0, function () {
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var inputDepth, outputDepth, inputShape, filterSize, stride, pad, filterShape, x, filt, grads, dy, _a, xGrad, filtGrad, expectedXGrad, _b, _c, expectedFiltGrad, _d, _e;
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return __generator(this, function (_f) {
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switch (_f.label) {
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case 0:
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inputDepth = 1;
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outputDepth = 2;
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inputShape = [1, 3, 3, inputDepth];
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filterSize = 2;
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stride = 1;
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pad = 'valid';
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filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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x = tf.tensor4d([[
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[[-0.14656299], [0.32942239], [-1.90302866]],
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[[-0.06487813], [-2.02637842], [-1.83669377]],
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[[0.82650784], [-0.89249092], [0.01207666]]
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]], inputShape);
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filt = tf.tensor4d([
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[[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
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[[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
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], filterShape);
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grads = tf.grads(function (x, filter) {
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return tf.conv2dTranspose(x.clone(), filter.clone(), [1, 4, 4, outputDepth], stride, pad)
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.clone();
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});
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dy = tf.ones([1, 4, 4, outputDepth]);
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_a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
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expectedXGrad = tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947));
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, xGrad.data()];
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case 1:
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_c = [_f.sent()];
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return [4 /*yield*/, expectedXGrad.data()];
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case 2:
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_b.apply(void 0, _c.concat([_f.sent()]));
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expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599));
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_d = test_util_1.expectArraysClose;
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return [4 /*yield*/, filtGrad.data()];
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case 3:
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_e = [_f.sent()];
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return [4 /*yield*/, expectedFiltGrad.data()];
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case 4:
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_d.apply(void 0, _e.concat([_f.sent()]));
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return [2 /*return*/];
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}
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});
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}); });
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// Reference (Python) TensorFlow code:
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//
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// ```py
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// import numpy as np
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// import tensorflow as tf
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//
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// tf.enable_eager_execution()
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//
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// x = tf.constant(np.array([
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// [[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]
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// ]).astype(np.float32))
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// filt = tf.constant(np.array([
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// [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
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// [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
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// ]).astype(np.float32))
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//
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// with tf.GradientTape() as g:
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// g.watch(x)
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// g.watch(filt)
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// y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2], strides=(2,
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// 2)) print(y)
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// (x_grad, filt_grad) = g.gradient(y, [x, filt])
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//
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// print("x_grad = %s" % -x_grad)
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// print("filt_grad = %s" % -filt_grad)
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// ```
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it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', function () { return __awaiter(_this, void 0, void 0, function () {
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var inputDepth, outputDepth, inputShape, filterSize, stride, pad, filterShape, x, filt, grads, dy, _a, xGrad, filtGrad, expectedXGrad, _b, _c, expectedFiltGrad, _d, _e;
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return __generator(this, function (_f) {
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switch (_f.label) {
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case 0:
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inputDepth = 1;
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outputDepth = 2;
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inputShape = [1, 2, 2, inputDepth];
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filterSize = 2;
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stride = [2, 2];
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pad = 'valid';
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filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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x = tf.tensor4d([[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]], inputShape);
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filt = tf.tensor4d([
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[[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
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[[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
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], filterShape);
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grads = tf.grads(function (x, filter) {
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return tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad);
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});
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dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));
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_a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
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expectedXGrad = tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196));
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, xGrad.data()];
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case 1:
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_c = [_f.sent()];
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return [4 /*yield*/, expectedXGrad.data()];
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case 2:
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_b.apply(void 0, _c.concat([_f.sent()]));
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expect(xGrad.shape).toEqual([1, 2, 2, 1]);
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expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618));
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_d = test_util_1.expectArraysClose;
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return [4 /*yield*/, filtGrad.data()];
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case 3:
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_e = [_f.sent()];
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return [4 /*yield*/, expectedFiltGrad.data()];
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case 4:
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_d.apply(void 0, _e.concat([_f.sent()]));
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expect(filtGrad.shape).toEqual([2, 2, 2, 1]);
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return [2 /*return*/];
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}
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});
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}); });
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// Reference (Python) TensorFlow code:
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//
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// ```py
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// import numpy as np
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// import tensorflow as tf
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//
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// tf.enable_eager_execution()
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//
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// x = tf.constant(np.array([[
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// [[1.52433065], [-0.77053435], [-0.64562341]],
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// [[0.77962889], [1.58413887], [-0.25581856]],
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// [[-0.58966221], [0.05411662], [0.70749138]]
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// ]]).astype(np.float32))
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// filt = tf.constant(np.array([
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// [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
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// [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
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// ]).astype(np.float32))
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//
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// with tf.GradientTape() as g:
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// g.watch(x)
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// g.watch(filt)
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// y = tf.keras.backend.conv2d_transpose(
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// x, filt, [1, 3, 3, 2], strides=(1, 1), padding='same')
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// (x_grad, filt_grad) = g.gradient(y, [x, filt])
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//
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// print("x_grad = %s" % x_grad)
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// print("filt_grad = %s" % filt_grad)
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// ```
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it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', function () { return __awaiter(_this, void 0, void 0, function () {
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var inputDepth, outputDepth, inputShape, filterSize, stride, pad, filterShape, x, filt, grads, dy, _a, xGrad, filtGrad, _b, _c;
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return __generator(this, function (_d) {
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switch (_d.label) {
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case 0:
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inputDepth = 1;
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outputDepth = 2;
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inputShape = [1, 3, 3, inputDepth];
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filterSize = 2;
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stride = [1, 1];
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pad = 'same';
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filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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x = tf.tensor4d([[
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[[1.52433065], [-0.77053435], [-0.64562341]],
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[[0.77962889], [1.58413887], [-0.25581856]],
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[[-0.58966221], [0.05411662], [0.70749138]]
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]], inputShape);
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filt = tf.tensor4d([
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[[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
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[[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
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], filterShape);
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grads = tf.grads(function (x, filter) {
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return tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad);
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});
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dy = tf.ones([1, 3, 3, outputDepth]);
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_a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, xGrad.array()];
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case 1:
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_b.apply(void 0, [_d.sent(), [[
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[[1.30709858], [1.30709858], [-0.92814366]],
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[[1.30709858], [1.30709858], [-0.92814366]],
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[[1.19666437], [1.19666437], [-0.85476589]]
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]]]);
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_c = test_util_1.expectArraysClose;
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return [4 /*yield*/, filtGrad.array()];
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case 2:
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_c.apply(void 0, [_d.sent(), [
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[[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
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[[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
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]]);
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return [2 /*return*/];
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}
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});
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}); });
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// Reference (Python) TensorFlow code:
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//
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// ```py
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// import numpy as np
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// import tensorflow as tf
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//
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// tf.enable_eager_execution()
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//
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// x = tf.constant(np.array([[
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// [[1.52433065], [-0.77053435]], [[0.77962889], [1.58413887]],
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// ]]).astype(np.float32))
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// filt = tf.constant(np.array([
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// [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
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// [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
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// ]).astype(np.float32))
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//
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// with tf.GradientTape() as g:
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// g.watch(x)
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// g.watch(filt)
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// y = tf.keras.backend.conv2d_transpose(
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// x, filt, [1, 3, 3, 2], strides=(2, 2), padding='same')
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// print(y.shape)
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// (x_grad, filt_grad) = g.gradient(y, [x, filt])
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//
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// print("x_grad = %s" % x_grad)
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// print("filt_grad = %s" % filt_grad)
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// ```
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it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', function () { return __awaiter(_this, void 0, void 0, function () {
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var inputDepth, outputDepth, inputShape, filterSize, stride, pad, filterShape, x, filt, grads, dy, _a, xGrad, filtGrad, _b, _c;
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return __generator(this, function (_d) {
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switch (_d.label) {
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case 0:
|
inputDepth = 2;
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outputDepth = 2;
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inputShape = [1, 2, 2, inputDepth];
|
filterSize = 2;
|
stride = [2, 2];
|
pad = 'same';
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filterShape = [filterSize, filterSize, outputDepth, inputDepth];
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x = tf.tensor4d([[
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[[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],
|
[[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]
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]], inputShape);
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filt = tf.tensor4d([
|
[
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[[0.19529686, -0.79594708], [0.70314057, -0.06081263]],
|
[[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]
|
],
|
[
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[[0.51872197, -1.17569193], [1.28316791, -0.81225092]],
|
[[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]
|
]
|
], filterShape);
|
grads = tf.grads(function (x, filter) {
|
return tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad);
|
});
|
dy = tf.ones([1, 3, 3, outputDepth]);
|
_a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, xGrad.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [
|
1.54219678, -2.19204008, 2.70032732, -2.84470257, 0.66744391, -0.94274245,
|
0.89843743, -0.85675972
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]]);
|
expect(xGrad.shape).toEqual([1, 2, 2, 2]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, filtGrad.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [
|
-1.00204261, 0.27276259, -1.00204261, 0.27276259, -2.99976385, 0.66119574,
|
-2.99976385, 0.66119574, -1.86705711, 1.27211472, -1.86705711, 1.27211472,
|
-1.81506593, 1.00900095, -1.81506593, 1.00900095
|
]]);
|
expect(filtGrad.shape).toEqual([2, 2, 2, 2]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws when x is not rank 3', function () {
|
var origInputDepth = 1;
|
var origOutputDepth = 1;
|
var fSize = 2;
|
var origPad = 0;
|
var origStride = 1;
|
// tslint:disable-next-line:no-any
|
var x = tf.tensor2d([2, 2], [2, 1]);
|
var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
|
expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad); })
|
.toThrowError();
|
});
|
it('throws when weights is not rank 4', function () {
|
var origInputDepth = 1;
|
var origOutputDepth = 1;
|
var inputShape = [1, 1, origOutputDepth];
|
var fSize = 2;
|
var origPad = 0;
|
var origStride = 1;
|
var x = tf.tensor3d([2], inputShape);
|
// tslint:disable-next-line:no-any
|
var w = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);
|
expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad); })
|
.toThrowError();
|
});
|
it('throws when x depth does not match weights original output depth', function () {
|
var origInputDepth = 1;
|
var origOutputDepth = 2;
|
var wrongOrigOutputDepth = 3;
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var inputShape = [1, 1, origOutputDepth];
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var fSize = 2;
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var origPad = 0;
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var origStride = 1;
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var x = tf.tensor3d([2, 2], inputShape);
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var w = tf.randomNormal([fSize, fSize, origInputDepth, wrongOrigOutputDepth]);
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expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad); })
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.toThrowError();
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});
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it('throws when passed x as a non-tensor', function () {
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var origInputDepth = 1;
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var origOutputDepth = 1;
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var fSize = 2;
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var origPad = 0;
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var origStride = 1;
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var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
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expect(function () { return tf.conv2dTranspose({}, w, [2, 2, 1], origStride, origPad); })
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.toThrowError(/Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);
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});
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it('throws when passed filter as a non-tensor', function () {
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var origOutputDepth = 1;
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var inputShape = [1, 1, origOutputDepth];
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var origPad = 0;
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var origStride = 1;
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var x = tf.tensor3d([2], inputShape);
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expect(function () { return tf.conv2dTranspose(x, {}, [2, 2, 1], origStride, origPad); })
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.toThrowError(/Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);
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});
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it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
|
var origPad, origStride, x, w, result, expected, _a;
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return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
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origPad = 0;
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origStride = 1;
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x = [[[2]]];
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w = [[[[3]], [[1]]], [[[5]], [[0]]]];
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result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
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expected = [6, 2, 10, 0];
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expect(result.shape).toEqual([2, 2, 1]);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), expected]);
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return [2 /*return*/];
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}
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});
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}); });
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});
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//# sourceMappingURL=conv2d_transpose_test.js.map
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