@tensorflow/tfjs-core/dist/ops/multi_rnn_cell_test.js

/**
 * @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/);
    });
});
//# sourceMappingURL=multi_rnn_cell_test.js.map