/**
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* @license
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* Copyright 2018 Google LLC. 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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import * as tf from '../index';
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import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
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import { expectArraysClose } from '../test_util';
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describeWithFlags('RMSPropOptimizer', ALL_ENVS, () => {
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it('basic', async () => {
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const initialTensors = tf.memory().numTensors;
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const learningRate = 0.1;
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const moment = 0.1;
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const rho = 0.95;
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const optimizer = tf.train.rmsprop(learningRate, rho, moment);
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const x = tf.tensor1d([1, 2]).variable();
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const f = () => x.square().sum();
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let numTensors = tf.memory().numTensors;
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let cost = optimizer.minimize(f, /* returnCost */ true);
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// Cost & 2 accumulators should be the only additional arrays.
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expect(tf.memory().numTensors).toBe(numTensors + 3);
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// epsilon = 1e-8
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// newAccumulatedMeanSquare =
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// rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = (0.2)
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// newAccumulatedMoments = momentum * accumulatedMoments +
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// learning_rate * gradient / sqrt(newAccumulatedMeanSquare +
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// epsilon) = 0.1 * 0 + ((0.1 * 2) / sqrt(0.2 + 1e-8)) = 0.44721
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// x -= learningRate * newAccumulatedMoments
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//
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// de/dx = [2, 4]
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// accumulatedMeanSquare = [0, 0]
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// newAccumulatedMeanSquare = [.2, .8]
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// accumulatedMoments = [0, 0]
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// newAccumulatedMoments = [0.44721, 0.44721]
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// x = [0.55279, 1.55279]
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expectArraysClose(await x.data(), [0.55279, 1.55279]);
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cost.dispose();
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numTensors = tf.memory().numTensors;
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cost = optimizer.minimize(f, /* returnCost */ false);
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// x = [0.55279, 1.55279]
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// de/dx = [1.10558, 3.10558]
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// accumulatedMeanSquare = [0.2, 0.8]
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// newAccumulatedMeanSquare = [0.25105125, 1.242231]
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// accumulatedMoments = [0.44721, 0.44721]
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// newAccumulatedMoments = [0.26534, 0.32336]
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// x = [0.28745, 1.22943]
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// TODO: Fix numerical precision.
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expectArraysClose(await x.data(), [0.28745, 1.222943], 1e-2);
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// There should be no new additional Tensors.
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expect(tf.memory().numTensors).toBe(numTensors);
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expect(cost).toBe(null);
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x.dispose();
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optimizer.dispose();
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// The only additional tensor remaining is the argument to variable().
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expect(tf.memory().numTensors).toBe(initialTensors + 1);
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});
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it('gradient with centered momentum', async () => {
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const initialTensors = tf.memory().numTensors;
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const learningRate = 0.1;
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const moment = 0.1;
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const rho = 0.95;
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const eps = 1e-8;
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const optimizer = tf.train.rmsprop(learningRate, rho, moment, eps, true);
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const x = tf.tensor1d([1, 2]).variable();
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const f = () => x.square().sum();
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let numTensors = tf.memory().numTensors;
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let cost = optimizer.minimize(f, /* returnCost */ true);
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// Cost & 3 accumulators should be the only additional arrays.
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expect(tf.memory().numTensors).toBe(numTensors + 4);
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// epsilon = 1e-8
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// newAccumulatedMeanSquare =
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// rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = [.2, .8]
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// newAccumulatedMeanGrad =
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// rho * accumulatedMeanGrad + (1 - rho) * grad = [0.1, 0.2]
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// newAccumulatedMoments = momentum * accumulatedMoments +
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// learning_rate * gradient / sqrt(newAccumulatedMeanSquare
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// - newAccumulatedMeanGrad * 2 +
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// epsilon) = 0.1 * 0 + ((0.1 * 2)
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// / sqrt(0.2 - 0.01 + 1e-8)) = 0.458831
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// x -= learningRate * newAccumulatedMoments
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//
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// de/dx = [2, 4]
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// accumulatedMeanSquare = [0, 0]
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// newAccumulatedMeanSquare = [.2, .8]
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// newAccumulatedMeanGrad = [.1, .2]
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// accumulatedMoments = [0, 0]
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// newAccumulatedMoments = [0.45883, 0.458831]
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// x = [0.54117, 1.541169]
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expectArraysClose(await x.data(), [0.54117, 1.541169]);
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cost.dispose();
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numTensors = tf.memory().numTensors;
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cost = optimizer.minimize(f, /* returnCost */ false);
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// x = [0.54117, 1.541169]
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// de/dx = [1.08234, 3.082338]
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// accumulatedMeanSquare = [0.2, 0.8]
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// accumulatedMeanGrad = [.1, .2]
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// newAccumulatedMeanSquare = [0.248572, 1.235040]
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// newAccumulatedMeanGrad = [0.149117, 0.3441169]
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// accumulatedMoments = [0.45883, 0.458831]
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// newAccumulatedMoments = [0.273385, 0.3375766]
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// x = [0.267785, 1.2035924]
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// TODO: Fix numerical precision.
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expectArraysClose(await x.data(), [0.267785, 1.2035924], 1e-2);
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// There should be no new additional Tensors.
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expect(tf.memory().numTensors).toBe(numTensors);
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expect(cost).toBe(null);
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x.dispose();
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optimizer.dispose();
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// The only additional tensor remaining is the argument to variable().
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expect(tf.memory().numTensors).toBe(initialTensors + 1);
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});
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it('Save and load weights: centered = false', async () => {
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const learningRate = 0.1;
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const moment = 0.1;
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const rho = 0.95;
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const optimizer1 = tf.train.rmsprop(learningRate, rho, moment);
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const x = tf.tensor1d([1, 2]).variable();
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const f = () => x.square().sum();
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let cost = optimizer1.minimize(f, /* returnCost */ true);
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expectArraysClose(await cost.data(), 5);
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expectArraysClose(await x.data(), [0.5527865, 1.5527864]);
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const weights = await optimizer1.getWeights();
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// An iteration variable and two optimizer state variables.
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expect(weights.length).toEqual(3);
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const optimizer2 = tf.train.rmsprop(learningRate, rho, moment);
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await optimizer2.setWeights(weights);
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cost = optimizer2.minimize(f, /* returnCost */ true);
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expectArraysClose(await cost.data(), 2.7167187);
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expectArraysClose(await x.data(), [0.2874418, 1.2294267]);
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expect(optimizer2.iterations).toEqual(2);
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});
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it('Save, load weights and continue training: centered = true', async () => {
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const learningRate = 0.1;
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const moment = 0.1;
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const rho = 0.95;
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const epsilon = undefined;
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const centered = true;
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const optimizer1 = tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
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const x = tf.tensor1d([1, 2]).variable();
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const f = () => x.square().sum();
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let cost = optimizer1.minimize(f, /* returnCost */ true);
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expectArraysClose(await cost.data(), 5);
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expectArraysClose(await x.data(), [0.5411684, 1.5411685]);
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const weights = await optimizer1.getWeights();
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// An iteration variable and three optimizer state variables.
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expect(weights.length).toEqual(4);
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const optimizer2 = tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
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await optimizer2.setWeights(weights);
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cost = optimizer2.minimize(f, /* returnCost */ true);
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expectArraysClose(await cost.data(), 2.668063);
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expectArraysClose(await x.data(), [0.2677834, 1.2035918]);
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expect(optimizer2.iterations).toEqual(2);
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const optimizer3 = tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
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await optimizer3.setWeights(await optimizer2.getWeights());
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cost = optimizer3.minimize(f, /* returnCost */ true);
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expectArraysClose(await cost.data(), 1.520341);
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expect(optimizer3.iterations).toEqual(3);
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});
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it('serialization round-trip', () => {
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const originalOpt = tf.train.rmsprop(0.1, 0.5, 0.1, 1e-7, true);
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const reserialized = tf.RMSPropOptimizer.fromConfig(tf.RMSPropOptimizer, originalOpt.getConfig());
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expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
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});
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it('must define learning rate', () => {
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const learningRate = undefined;
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expect(() => tf.train.rmsprop(learningRate))
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.toThrowError(/learningRate for RMSPropOptimizer must be defined./);
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});
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});
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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('RMSPropOptimizer', ALL_ENVS, () => {\n  it('basic', async () => {\n    const initialTensors = tf.memory().numTensors;\n    const learningRate = 0.1;\n    const moment = 0.1;\n    const rho = 0.95;\n    const optimizer = tf.train.rmsprop(learningRate, rho, moment);\n\n    const x = tf.tensor1d([1, 2]).variable();\n\n    const f = () => x.square().sum();\n\n    let numTensors = tf.memory().numTensors;\n\n    let cost = optimizer.minimize(f as () => tf.Scalar, /* returnCost */ true);\n\n    // Cost & 2 accumulators should be the only additional arrays.\n    expect(tf.memory().numTensors).toBe(numTensors + 3);\n\n    // epsilon = 1e-8\n    // newAccumulatedMeanSquare =\n    //          rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = (0.2)\n    // newAccumulatedMoments = momentum * accumulatedMoments +\n    //          learning_rate * gradient / sqrt(newAccumulatedMeanSquare +\n    //          epsilon) = 0.1 * 0 + ((0.1 * 2) / sqrt(0.2 + 1e-8)) = 0.44721\n    // x -= learningRate * newAccumulatedMoments\n    //\n    // de/dx = [2, 4]\n    // accumulatedMeanSquare = [0, 0]\n    // newAccumulatedMeanSquare = [.2, .8]\n    // accumulatedMoments = [0, 0]\n    // newAccumulatedMoments = [0.44721, 0.44721]\n    // x = [0.55279, 1.55279]\n    expectArraysClose(await x.data(), [0.55279, 1.55279]);\n\n    cost.dispose();\n    numTensors = tf.memory().numTensors;\n\n    cost = optimizer.minimize(f as () => tf.Scalar, /* returnCost */ false);\n\n    // x = [0.55279, 1.55279]\n    // de/dx = [1.10558, 3.10558]\n    // accumulatedMeanSquare = [0.2, 0.8]\n    // newAccumulatedMeanSquare = [0.25105125, 1.242231]\n    // accumulatedMoments = [0.44721, 0.44721]\n    // newAccumulatedMoments = [0.26534, 0.32336]\n    // x = [0.28745, 1.22943]\n\n    // TODO: Fix numerical precision.\n    expectArraysClose(await x.data(), [0.28745, 1.222943], 1e-2);\n\n    // There should be no new additional Tensors.\n    expect(tf.memory().numTensors).toBe(numTensors);\n\n    expect(cost).toBe(null);\n\n    x.dispose();\n    optimizer.dispose();\n    // The only additional tensor remaining is the argument to variable().\n    expect(tf.memory().numTensors).toBe(initialTensors + 1);\n  });\n\n  it('gradient with centered momentum', async () => {\n    const initialTensors = tf.memory().numTensors;\n    const learningRate = 0.1;\n    const moment = 0.1;\n    const rho = 0.95;\n    const eps = 1e-8;\n    const optimizer = tf.train.rmsprop(learningRate, rho, moment, eps, true);\n\n    const x = tf.tensor1d([1, 2]).variable();\n\n    const f = () => x.square().sum();\n\n    let numTensors = tf.memory().numTensors;\n\n    let cost = optimizer.minimize(f as () => tf.Scalar, /* returnCost */ true);\n\n    // Cost & 3 accumulators should be the only additional arrays.\n    expect(tf.memory().numTensors).toBe(numTensors + 4);\n\n    // epsilon = 1e-8\n    // newAccumulatedMeanSquare =\n    //          rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = [.2, .8]\n    // newAccumulatedMeanGrad =\n    //          rho * accumulatedMeanGrad + (1 - rho) * grad = [0.1, 0.2]\n    // newAccumulatedMoments = momentum * accumulatedMoments +\n    //          learning_rate * gradient / sqrt(newAccumulatedMeanSquare\n    //            - newAccumulatedMeanGrad * 2 +\n    //              epsilon) = 0.1 * 0 + ((0.1 * 2)\n    //                / sqrt(0.2 - 0.01 + 1e-8)) = 0.458831\n    // x -= learningRate * newAccumulatedMoments\n    //\n    // de/dx = [2, 4]\n    // accumulatedMeanSquare = [0, 0]\n    // newAccumulatedMeanSquare = [.2, .8]\n    // newAccumulatedMeanGrad = [.1, .2]\n    // accumulatedMoments = [0, 0]\n    // newAccumulatedMoments = [0.45883, 0.458831]\n    // x = [0.54117, 1.541169]\n    expectArraysClose(await x.data(), [0.54117, 1.541169]);\n\n    cost.dispose();\n    numTensors = tf.memory().numTensors;\n\n    cost = optimizer.minimize(f as () => tf.Scalar, /* returnCost */ false);\n\n    // x = [0.54117, 1.541169]\n    // de/dx = [1.08234, 3.082338]\n    // accumulatedMeanSquare = [0.2, 0.8]\n    // accumulatedMeanGrad = [.1, .2]\n    // newAccumulatedMeanSquare = [0.248572, 1.235040]\n    // newAccumulatedMeanGrad = [0.149117, 0.3441169]\n    // accumulatedMoments = [0.45883, 0.458831]\n    // newAccumulatedMoments = [0.273385, 0.3375766]\n    // x = [0.267785, 1.2035924]\n\n    // TODO: Fix numerical precision.\n    expectArraysClose(await x.data(), [0.267785, 1.2035924], 1e-2);\n\n    // There should be no new additional Tensors.\n    expect(tf.memory().numTensors).toBe(numTensors);\n\n    expect(cost).toBe(null);\n\n    x.dispose();\n    optimizer.dispose();\n    // The only additional tensor remaining is the argument to variable().\n    expect(tf.memory().numTensors).toBe(initialTensors + 1);\n  });\n\n  it('Save and load weights: centered = false', async () => {\n    const learningRate = 0.1;\n    const moment = 0.1;\n    const rho = 0.95;\n    const optimizer1 = tf.train.rmsprop(learningRate, rho, moment);\n\n    const x = tf.tensor1d([1, 2]).variable();\n    const f: () => tf.Scalar = () => x.square().sum();\n\n    let cost = optimizer1.minimize(f, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 5);\n    expectArraysClose(await x.data(), [0.5527865, 1.5527864]);\n\n    const weights = await optimizer1.getWeights();\n    // An iteration variable and two optimizer state variables.\n    expect(weights.length).toEqual(3);\n\n    const optimizer2 = tf.train.rmsprop(learningRate, rho, moment);\n    await optimizer2.setWeights(weights);\n\n    cost = optimizer2.minimize(f, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 2.7167187);\n    expectArraysClose(await x.data(), [0.2874418, 1.2294267]);\n    expect(optimizer2.iterations).toEqual(2);\n  });\n\n  it('Save, load weights and continue training: centered = true', async () => {\n    const learningRate = 0.1;\n    const moment = 0.1;\n    const rho = 0.95;\n    const epsilon: number = undefined;\n    const centered = true;\n    const optimizer1 =\n        tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);\n\n    const x = tf.tensor1d([1, 2]).variable();\n    const f = () => x.square().sum();\n\n    let cost = optimizer1.minimize(f as () => tf.Scalar, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 5);\n    expectArraysClose(await x.data(), [0.5411684, 1.5411685]);\n\n    const weights = await optimizer1.getWeights();\n    // An iteration variable and three optimizer state variables.\n    expect(weights.length).toEqual(4);\n\n    const optimizer2 =\n        tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);\n    await optimizer2.setWeights(weights);\n\n    cost = optimizer2.minimize(f as () => tf.Scalar, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 2.668063);\n    expectArraysClose(await x.data(), [0.2677834, 1.2035918]);\n    expect(optimizer2.iterations).toEqual(2);\n\n    const optimizer3 =\n        tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);\n    await optimizer3.setWeights(await optimizer2.getWeights());\n    cost = optimizer3.minimize(f as () => tf.Scalar, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 1.520341);\n    expect(optimizer3.iterations).toEqual(3);\n  });\n\n  it('serialization round-trip', () => {\n    const originalOpt = tf.train.rmsprop(0.1, 0.5, 0.1, 1e-7, true);\n    const reserialized = tf.RMSPropOptimizer.fromConfig(\n        tf.RMSPropOptimizer, originalOpt.getConfig());\n    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());\n  });\n\n  it('must define learning rate', () => {\n    const learningRate: number = undefined;\n    expect(() => tf.train.rmsprop(learningRate))\n        .toThrowError(/learningRate for RMSPropOptimizer must be defined./);\n  });\n});\n"]}
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