/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
describeWithFlags('AdamaxOptimizer', ALL_ENVS, () => {
    it('basic', async () => {
        const initialTensors = tf.memory().numTensors;
        const learningRate = 0.1;
        const beta1 = 0.8;
        const beta2 = 0.9;
        const decay = 0.1;
        const optimizer = tf.train.adamax(learningRate, beta1, beta2, undefined, decay);
        const x = tf.tensor1d([2, 4]).variable();
        const f = () => x.square().sum();
        let numTensors = tf.memory().numTensors;
        let cost = optimizer.minimize(f, /* returnCost */ true);
        // Cost & 2 accumulators should be the only additional arrays.
        expect(tf.memory().numTensors).toBe(numTensors + 3);
        // new_first_m = [
        //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
        //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2
        // ] = [.8, 1.6]
        //
        // ut_0 = beta2 * old_weighted_inf_norm = [0, 0]
        // u1_1 = [
        //    abs(grad_w1),
        //    abs(grad_w2)
        // ] = [4, 8]
        // new_weighted_inf_norm = max(ut_0, ut_1) = [4, 8]
        //
        // coefficient = alpha / (1-beta1) = 0.5
        // updates = coefficient * [
        //    new_first_m1 / new_weighted_inf_norm1,
        //    new_first_m2 / new_weighted_inf_norm2
        // ] = [0.1, 0.1]
        // w = [
        //    w1_old - updates_1,
        //    w2_old - updates_2
        // ] = [1.9, 3.9]
        //
        expectArraysClose(await x.data(), [1.9, 3.9]);
        cost.dispose();
        numTensors = tf.memory().numTensors;
        cost = optimizer.minimize(f, /* returnCost */ false);
        // gradient = [3.8, 7.8]
        // new_first_m = [
        //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
        //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2
        // ] = [
        //    0.8 * 0.8 + 0.2 * 3.8,
        //    0.8 * 1.6 + 0.2 * 7.8
        // ] = [1.4, 2.84]
        //
        // ut_0 = beta2 * old_weighted_inf_norm = [
        //    0.9 * 4,
        //    0.9 * 8
        // ] = [3.6, 7.2]
        // u1_1 = [
        //    abs(grad_w1),
        //    abs(grad_w2)
        // ] = [3.8, 7.8]
        // new_weighted_inf_norm = max(ut_0, ut_1) = [3.8, 7.8]
        //
        // alpha = 0.1 / (1 + 0.1 * 1) = 0.0909090909
        //
        // coefficient = alpha / (1 - beta1*beta1) = 0.25252525
        // updates = coefficient * [
        //    new_first_m1 / new_weighted_inf_norm1,
        //    new_first_m2 / new_weighted_inf_norm2
        // ] = [0.09303, 0.09194]
        // w = [
        //    w1_old - updates_1,
        //    w2_old - updates_2
        // ] = [1.80697, 3.8086]
        //
        expectArraysClose(await x.data(), [1.80697, 3.8086]);
        // There should be no new additional Tensors.
        expect(tf.memory().numTensors).toBe(numTensors);
        expect(cost).toBe(null);
        x.dispose();
        optimizer.dispose();
        // The only additional tensor remaining should be the argument to
        // variable().
        expect(tf.memory().numTensors).toBe(initialTensors + 1);
    });
    it('serialization round-trip', () => {
        const originalOpt = tf.train.adamax(0.1, 0.2, 0.3, 2e-8, 0.1);
        const reserialized = tf.AdamaxOptimizer.fromConfig(tf.AdamaxOptimizer, originalOpt.getConfig());
        expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
    });
});
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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('AdamaxOptimizer', ALL_ENVS, () => {\n  it('basic', async () => {\n    const initialTensors = tf.memory().numTensors;\n    const learningRate = 0.1;\n    const beta1 = 0.8;\n    const beta2 = 0.9;\n    const decay = 0.1;\n    const optimizer =\n        tf.train.adamax(learningRate, beta1, beta2, undefined, decay);\n\n    const x = tf.tensor1d([2, 4]).variable();\n\n    const f: () => tf.Scalar = () => x.square().sum();\n\n    let numTensors = tf.memory().numTensors;\n\n    let cost = optimizer.minimize(f, /* returnCost */ true);\n\n    // Cost & 2 accumulators should be the only additional arrays.\n    expect(tf.memory().numTensors).toBe(numTensors + 3);\n    // new_first_m = [\n    //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,\n    //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2\n    // ] = [.8, 1.6]\n    //\n    // ut_0 = beta2 * old_weighted_inf_norm = [0, 0]\n    // u1_1 = [\n    //    abs(grad_w1),\n    //    abs(grad_w2)\n    // ] = [4, 8]\n    // new_weighted_inf_norm = max(ut_0, ut_1) = [4, 8]\n    //\n    // coefficient = alpha / (1-beta1) = 0.5\n    // updates = coefficient * [\n    //    new_first_m1 / new_weighted_inf_norm1,\n    //    new_first_m2 / new_weighted_inf_norm2\n    // ] = [0.1, 0.1]\n    // w = [\n    //    w1_old - updates_1,\n    //    w2_old - updates_2\n    // ] = [1.9, 3.9]\n    //\n    expectArraysClose(await x.data(), [1.9, 3.9]);\n\n    cost.dispose();\n    numTensors = tf.memory().numTensors;\n\n    cost = optimizer.minimize(f, /* returnCost */ false);\n\n    // gradient = [3.8, 7.8]\n    // new_first_m = [\n    //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,\n    //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2\n    // ] = [\n    //    0.8 * 0.8 + 0.2 * 3.8,\n    //    0.8 * 1.6 + 0.2 * 7.8\n    // ] = [1.4, 2.84]\n    //\n    // ut_0 = beta2 * old_weighted_inf_norm = [\n    //    0.9 * 4,\n    //    0.9 * 8\n    // ] = [3.6, 7.2]\n    // u1_1 = [\n    //    abs(grad_w1),\n    //    abs(grad_w2)\n    // ] = [3.8, 7.8]\n    // new_weighted_inf_norm = max(ut_0, ut_1) = [3.8, 7.8]\n    //\n    // alpha = 0.1 / (1 + 0.1 * 1) = 0.0909090909\n    //\n    // coefficient = alpha / (1 - beta1*beta1) = 0.25252525\n    // updates = coefficient * [\n    //    new_first_m1 / new_weighted_inf_norm1,\n    //    new_first_m2 / new_weighted_inf_norm2\n    // ] = [0.09303, 0.09194]\n    // w = [\n    //    w1_old - updates_1,\n    //    w2_old - updates_2\n    // ] = [1.80697, 3.8086]\n    //\n    expectArraysClose(await x.data(), [1.80697, 3.8086]);\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\n    // The only additional tensor remaining should be the argument to\n    // variable().\n    expect(tf.memory().numTensors).toBe(initialTensors + 1);\n  });\n\n  it('serialization round-trip', () => {\n    const originalOpt = tf.train.adamax(0.1, 0.2, 0.3, 2e-8, 0.1);\n    const reserialized = tf.AdamaxOptimizer.fromConfig(\n        tf.AdamaxOptimizer, originalOpt.getConfig());\n    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());\n  });\n});\n"]}