@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 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('lstm', ALL_ENVS, () => {
it('MultiRNNCell with 2 BasicLSTMCells', async () => {
const lstmKernel1 = tf.tensor2d([
0.26242125034332275, -0.8787832260131836, 0.781475305557251,
1.337337851524353, 0.6180247068405151, -0.2760246992111206,
-0.11299663782119751, -0.46332040429115295, -0.1765323281288147,
0.6807947158813477, -0.8326982855796814, 0.6732975244522095
], [3, 4]);
const lstmBias1 = tf.tensor1d([1.090713620185852, -0.8282332420349121, 0, 1.0889357328414917]);
const lstmKernel2 = tf.tensor2d([
-1.893059492111206, -1.0185645818710327, -0.6270437240600586,
-2.1829540729522705, -0.4583775997161865, -0.5454602241516113,
-0.3114445209503174, 0.8450229167938232
], [2, 4]);
const lstmBias2 = tf.tensor1d([0.9906240105628967, 0.6248329877853394, 0, 1.0224634408950806]);
const forgetBias = tf.scalar(1.0);
const lstm1 = (data, c, h) => tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
const lstm2 = (data, c, h) => tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
const c = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
const h = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
const onehot = tf.buffer([1, 2], 'float32');
onehot.set(1.0, 0, 0);
const output = tf.multiRNNCell([lstm1, lstm2], onehot.toTensor(), c, h);
expectArraysClose(await output[0][0].data(), [-0.7440074682235718]);
expectArraysClose(await output[0][1].data(), [0.7460772395133972]);
expectArraysClose(await output[1][0].data(), [-0.5802832245826721]);
expectArraysClose(await output[1][1].data(), [0.5745711922645569]);
});
});
describeWithFlags('multiRNN throws when passed non-tensor', ALL_ENVS, () => {
it('input: data', () => {
const lstmKernel1 = tf.zeros([3, 4]);
const lstmBias1 = tf.zeros([4]);
const lstmKernel2 = tf.zeros([2, 4]);
const lstmBias2 = tf.zeros([4]);
const forgetBias = tf.scalar(1.0);
const lstm1 = (data, c, h) => tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
const lstm2 = (data, c, h) => tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
const c = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
const h = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
expect(() => tf.multiRNNCell([lstm1, lstm2], {}, c, h))
.toThrowError(/Argument 'data' passed to 'multiRNNCell' must be a Tensor/);
});
it('input: c', () => {
const lstmKernel1 = tf.zeros([3, 4]);
const lstmBias1 = tf.zeros([4]);
const lstmKernel2 = tf.zeros([2, 4]);
const lstmBias2 = tf.zeros([4]);
const forgetBias = tf.scalar(1.0);
const lstm1 = (data, c, h) => tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
const lstm2 = (data, c, h) => tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
const h = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
const data = tf.zeros([1, 2]);
expect(() => tf.multiRNNCell([lstm1, lstm2], data, [{}], h))
.toThrowError(/Argument 'c\[0\]' passed to 'multiRNNCell' must be a Tensor/);
});
it('input: h', () => {
const lstmKernel1 = tf.zeros([3, 4]);
const lstmBias1 = tf.zeros([4]);
const lstmKernel2 = tf.zeros([2, 4]);
const lstmBias2 = tf.zeros([4]);
const forgetBias = tf.scalar(1.0);
const lstm1 = (data, c, h) => tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
const lstm2 = (data, c, h) => tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
const c = [
tf.zeros([1, lstmBias1.shape[0] / 4]),
tf.zeros([1, lstmBias2.shape[0] / 4])
];
const data = tf.zeros([1, 2]);
expect(() => tf.multiRNNCell([lstm1, lstm2], data, c, [{}]))
.toThrowError(/Argument 'h\[0\]' passed to 'multiRNNCell' must be a Tensor/);
});
});
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