/**
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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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import { Optimizer } from './optimizer';
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import { SGDOptimizer } from './sgd_optimizer';
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describeWithFlags('optimizer', ALL_ENVS, () => {
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it('basic', async () => {
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const initialTensors = tf.memory().numTensors;
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const learningRate = .1;
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const optimizer = tf.train.sgd(learningRate);
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const x = tf.scalar(4).variable();
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const bias = tf.scalar(1).variable();
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const strayVariable = tf.scalar(-1).variable();
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let numTensors = tf.memory().numTensors;
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// tslint:disable-next-line: no-unnecessary-type-assertion
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const f = () => x.square().add(bias);
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let cost = optimizer.minimize(f, /* returnCost */ true);
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// Cost should be the only additional arrays.
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expect(tf.memory().numTensors).toBe(numTensors + 1);
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// de/dx = 2x
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const expectedX1 = -2 * 4 * learningRate + 4;
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// de/db = 1
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const expectedBias1 = -1 * learningRate + 1;
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expectArraysClose(await x.data(), [expectedX1]);
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expectArraysClose(await bias.data(), [expectedBias1]);
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expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);
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// The stray variable should remain unchanged.
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expectArraysClose(await strayVariable.data(), [-1]);
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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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// There should be no new additional Tensors.
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expect(tf.memory().numTensors).toBe(numTensors);
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const expectedX2 = -2 * expectedX1 * learningRate + expectedX1;
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const expectedBias2 = -learningRate + expectedBias1;
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expectArraysClose(await x.data(), [expectedX2]);
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expectArraysClose(await bias.data(), [expectedBias2]);
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expect(cost).toBe(null);
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// The stray variable should remain unchanged.
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expectArraysClose(await strayVariable.data(), [-1]);
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optimizer.dispose();
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x.dispose();
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bias.dispose();
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strayVariable.dispose();
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// The only additional tensors remaining are the arguments to variable().
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expect(tf.memory().numTensors).toBe(initialTensors + 3);
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});
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it('varList array of all variables', async () => {
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const learningRate = .1;
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const optimizer = new SGDOptimizer(learningRate);
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const x = tf.scalar(4).variable();
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const bias = tf.scalar(1).variable();
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const strayVariable = tf.scalar(-1).variable();
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const varList = [x, bias];
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// tslint:disable-next-line: no-unnecessary-type-assertion
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const f = () => x.square().add(bias);
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let cost = optimizer.minimize(f, /* returnCost */ true, varList);
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// de/dx = 2x
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const expectedX1 = -2 * 4 * learningRate + 4;
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// de/db = 1
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const expectedBias1 = -1 * learningRate + 1;
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expectArraysClose(await x.data(), [expectedX1]);
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expectArraysClose(await bias.data(), [expectedBias1]);
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expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);
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// The stray variable should remain unchanged.
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expectArraysClose(await strayVariable.data(), [-1]);
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cost = optimizer.minimize(f, /* returnCost */ false, varList);
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const expectedX2 = -2 * expectedX1 * learningRate + expectedX1;
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const expectedBias2 = -learningRate + expectedBias1;
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expectArraysClose(await x.data(), [expectedX2]);
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expectArraysClose(await bias.data(), [expectedBias2]);
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// The stray variable should remain unchanged.
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expectArraysClose(await strayVariable.data(), [-1]);
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expect(cost).toBe(null);
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});
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it('varList empty array of variables throws error', () => {
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const learningRate = .1;
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const optimizer = new SGDOptimizer(learningRate);
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const x = tf.scalar(4).variable();
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const bias = tf.scalar(1).variable();
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// Stray variable.
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tf.scalar(-1).variable();
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const varList = [];
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// tslint:disable-next-line: no-unnecessary-type-assertion
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const f = () => x.square().add(bias);
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expect(() => optimizer.minimize(f, /* returnCost */ true, varList))
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.toThrowError();
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});
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it('varList subset of variables update', async () => {
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const learningRate = .1;
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const optimizer = new SGDOptimizer(learningRate);
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const x = tf.scalar(4).variable();
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const bias = tf.scalar(1).variable();
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const strayVariable = tf.scalar(-1).variable();
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const varList = [x];
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// tslint:disable-next-line: no-unnecessary-type-assertion
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const f = () => x.square().add(bias);
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let cost = optimizer.minimize(f, /* returnCost */ true, varList);
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// de/dx = 2x
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const expectedValue1 = -2 * 4 * learningRate + 4;
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expectArraysClose(await x.data(), [expectedValue1]);
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// bias should remain unchanged.
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expectArraysClose(await bias.data(), [1]);
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expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);
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// The stray variable should remain unchanged.
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expectArraysClose(await strayVariable.data(), [-1]);
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cost = optimizer.minimize(f, /* returnCost */ false, varList);
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const expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;
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expectArraysClose(await x.data(), [expectedValue2]);
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// Bias still should remain unchanged.
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expectArraysClose(await bias.data(), [1]);
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expect(cost).toBe(null);
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// The stray variable should remain unchanged.
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expectArraysClose(await strayVariable.data(), [-1]);
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});
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it('only bias trainable', async () => {
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const learningRate = .1;
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const optimizer = new SGDOptimizer(learningRate);
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const trainable = false;
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const x = tf.scalar(4).variable(trainable);
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const bias = tf.scalar(1).variable();
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const strayVariable = tf.scalar(-1).variable();
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// tslint:disable-next-line: no-unnecessary-type-assertion
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const f = () => x.square().add(bias);
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let cost = optimizer.minimize(f, /* returnCost */ true);
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// x should not have been updated.
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expectArraysClose(await x.data(), [4]);
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// de/db = 1
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const expectedBias1 = -1 * learningRate + 1;
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expectArraysClose(await bias.data(), [expectedBias1]);
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expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);
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// The stray variable should remain unchanged.
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expectArraysClose(await strayVariable.data(), [-1]);
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cost = optimizer.minimize(f, /* returnCost */ false);
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// x should not have been updated.
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expectArraysClose(await x.data(), [4]);
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const expectedBias2 = -learningRate + expectedBias1;
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expectArraysClose(await bias.data(), [expectedBias2]);
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expect(cost).toBe(null);
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// The stray variable should remain unchanged.
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expectArraysClose(await strayVariable.data(), [-1]);
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});
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it('only bias trainable, only x in varList throws error', () => {
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const learningRate = .1;
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const optimizer = new SGDOptimizer(learningRate);
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const trainable = false;
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const x = tf.scalar(4).variable(trainable);
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const bias = tf.scalar(1).variable();
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// stray variable.
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tf.scalar(-1).variable();
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const varList = [x];
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// tslint:disable-next-line: no-unnecessary-type-assertion
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const f = () => x.square().add(bias);
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expect(() => optimizer.minimize(f, /* returnCost */ true, varList))
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.toThrowError();
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});
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it('instanceof Optimizer', () => {
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const learningRate = .1;
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const optimizer = new SGDOptimizer(learningRate);
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expect(optimizer instanceof Optimizer).toBe(true);
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});
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it('throws error when f returns a non-scalar', () => {
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const learningRate = .1;
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const optimizer = new SGDOptimizer(learningRate);
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const x = tf.tensor1d([1, 2]).variable();
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const f = () => x.square();
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// tslint:disable-next-line:no-any
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expect(() => optimizer.minimize(f)).toThrowError();
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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 {Variable} from '../tensor';\nimport {expectArraysClose} from '../test_util';\n\nimport {Optimizer} from './optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\ndescribeWithFlags('optimizer', ALL_ENVS, () => {\n  it('basic', async () => {\n    const initialTensors = tf.memory().numTensors;\n    const learningRate = .1;\n    const optimizer = tf.train.sgd(learningRate);\n\n    const x = tf.scalar(4).variable();\n    const bias = tf.scalar(1).variable();\n    const strayVariable = tf.scalar(-1).variable();\n\n    let numTensors = tf.memory().numTensors;\n\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const f = () => x.square().add(bias) as tf.Scalar;\n\n    let cost = optimizer.minimize(f, /* returnCost */ true);\n\n    // Cost should be the only additional arrays.\n    expect(tf.memory().numTensors).toBe(numTensors + 1);\n\n    // de/dx = 2x\n    const expectedX1 = -2 * 4 * learningRate + 4;\n    // de/db = 1\n    const expectedBias1 = -1 * learningRate + 1;\n    expectArraysClose(await x.data(), [expectedX1]);\n    expectArraysClose(await bias.data(), [expectedBias1]);\n    expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);\n    // The stray variable should remain unchanged.\n    expectArraysClose(await strayVariable.data(), [-1]);\n\n    cost.dispose();\n    numTensors = tf.memory().numTensors;\n\n    cost = optimizer.minimize(f, /* returnCost */ false);\n    // There should be no new additional Tensors.\n    expect(tf.memory().numTensors).toBe(numTensors);\n\n    const expectedX2 = -2 * expectedX1 * learningRate + expectedX1;\n    const expectedBias2 = -learningRate + expectedBias1;\n    expectArraysClose(await x.data(), [expectedX2]);\n    expectArraysClose(await bias.data(), [expectedBias2]);\n    expect(cost).toBe(null);\n    // The stray variable should remain unchanged.\n    expectArraysClose(await strayVariable.data(), [-1]);\n\n    optimizer.dispose();\n    x.dispose();\n    bias.dispose();\n    strayVariable.dispose();\n    // The only additional tensors remaining are the arguments to variable().\n    expect(tf.memory().numTensors).toBe(initialTensors + 3);\n  });\n\n  it('varList array of all variables', async () => {\n    const learningRate = .1;\n    const optimizer = new SGDOptimizer(learningRate);\n\n    const x = tf.scalar(4).variable();\n    const bias = tf.scalar(1).variable();\n    const strayVariable = tf.scalar(-1).variable();\n    const varList = [x, bias];\n\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const f = () => x.square().add(bias) as tf.Scalar;\n\n    let cost = optimizer.minimize(f, /* returnCost */ true, varList);\n\n    // de/dx = 2x\n    const expectedX1 = -2 * 4 * learningRate + 4;\n    // de/db = 1\n    const expectedBias1 = -1 * learningRate + 1;\n    expectArraysClose(await x.data(), [expectedX1]);\n    expectArraysClose(await bias.data(), [expectedBias1]);\n    expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);\n    // The stray variable should remain unchanged.\n    expectArraysClose(await strayVariable.data(), [-1]);\n\n    cost = optimizer.minimize(f, /* returnCost */ false, varList);\n\n    const expectedX2 = -2 * expectedX1 * learningRate + expectedX1;\n    const expectedBias2 = -learningRate + expectedBias1;\n    expectArraysClose(await x.data(), [expectedX2]);\n    expectArraysClose(await bias.data(), [expectedBias2]);\n    // The stray variable should remain unchanged.\n    expectArraysClose(await strayVariable.data(), [-1]);\n    expect(cost).toBe(null);\n  });\n\n  it('varList empty array of variables throws error', () => {\n    const learningRate = .1;\n    const optimizer = new SGDOptimizer(learningRate);\n\n    const x = tf.scalar(4).variable();\n    const bias = tf.scalar(1).variable();\n    // Stray variable.\n    tf.scalar(-1).variable();\n    const varList: Variable[] = [];\n\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const f = () => x.square().add(bias) as tf.Scalar;\n\n    expect(() => optimizer.minimize(f, /* returnCost */ true, varList))\n        .toThrowError();\n  });\n\n  it('varList subset of variables update', async () => {\n    const learningRate = .1;\n    const optimizer = new SGDOptimizer(learningRate);\n\n    const x = tf.scalar(4).variable();\n    const bias = tf.scalar(1).variable();\n    const strayVariable = tf.scalar(-1).variable();\n    const varList = [x];\n\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const f = () => x.square().add(bias) as tf.Scalar;\n\n    let cost = optimizer.minimize(f, /* returnCost */ true, varList);\n\n    // de/dx = 2x\n    const expectedValue1 = -2 * 4 * learningRate + 4;\n    expectArraysClose(await x.data(), [expectedValue1]);\n    // bias should remain unchanged.\n    expectArraysClose(await bias.data(), [1]);\n    expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);\n    // The stray variable should remain unchanged.\n    expectArraysClose(await strayVariable.data(), [-1]);\n\n    cost = optimizer.minimize(f, /* returnCost */ false, varList);\n\n    const expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;\n    expectArraysClose(await x.data(), [expectedValue2]);\n    // Bias still should remain unchanged.\n    expectArraysClose(await bias.data(), [1]);\n    expect(cost).toBe(null);\n    // The stray variable should remain unchanged.\n    expectArraysClose(await strayVariable.data(), [-1]);\n  });\n\n  it('only bias trainable', async () => {\n    const learningRate = .1;\n    const optimizer = new SGDOptimizer(learningRate);\n\n    const trainable = false;\n    const x = tf.scalar(4).variable(trainable);\n    const bias = tf.scalar(1).variable();\n    const strayVariable = tf.scalar(-1).variable();\n\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const f = () => x.square().add(bias) as tf.Scalar;\n\n    let cost = optimizer.minimize(f, /* returnCost */ true);\n\n    // x should not have been updated.\n    expectArraysClose(await x.data(), [4]);\n    // de/db = 1\n    const expectedBias1 = -1 * learningRate + 1;\n    expectArraysClose(await bias.data(), [expectedBias1]);\n    expectArraysClose(await cost.data(), [Math.pow(4, 2) + 1]);\n    // The stray variable should remain unchanged.\n    expectArraysClose(await strayVariable.data(), [-1]);\n\n    cost = optimizer.minimize(f, /* returnCost */ false);\n\n    // x should not have been updated.\n    expectArraysClose(await x.data(), [4]);\n    const expectedBias2 = -learningRate + expectedBias1;\n    expectArraysClose(await bias.data(), [expectedBias2]);\n    expect(cost).toBe(null);\n    // The stray variable should remain unchanged.\n    expectArraysClose(await strayVariable.data(), [-1]);\n  });\n\n  it('only bias trainable, only x in varList throws error', () => {\n    const learningRate = .1;\n    const optimizer = new SGDOptimizer(learningRate);\n\n    const trainable = false;\n    const x = tf.scalar(4).variable(trainable);\n    const bias = tf.scalar(1).variable();\n    // stray variable.\n    tf.scalar(-1).variable();\n    const varList = [x];\n\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const f = () => x.square().add(bias) as tf.Scalar;\n\n    expect(() => optimizer.minimize(f, /* returnCost */ true, varList))\n        .toThrowError();\n  });\n\n  it('instanceof Optimizer', () => {\n    const learningRate = .1;\n    const optimizer = new SGDOptimizer(learningRate);\n\n    expect(optimizer instanceof Optimizer).toBe(true);\n  });\n\n  it('throws error when f returns a non-scalar', () => {\n    const learningRate = .1;\n    const optimizer = new SGDOptimizer(learningRate);\n\n    const x = tf.tensor1d([1, 2]).variable();\n    const f = () => x.square();\n\n    // tslint:disable-next-line:no-any\n    expect(() => optimizer.minimize(f as any)).toThrowError();\n  });\n});\n"]}
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