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* @license\n * Copyright 2020 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 {Rank} from '../types';\n\ndescribeWithFlags('basicLSTMCell', ALL_ENVS, () => {\n it('basicLSTMCell with batch=2', async () => {\n const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n const lstmBias = tf.randomNormal<Rank.R1>([4]);\n const forgetBias = tf.scalar(1.0);\n\n const data = tf.randomNormal<Rank.R2>([1, 2]);\n const batchedData = tf.concat2d([data, data], 0); // 2x2\n const c = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedC = tf.concat2d([c, c], 0); // 2x1\n const h = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedH = tf.concat2d([h, h], 0); // 2x1\n const [newC, newH] = tf.basicLSTMCell(\n forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH);\n const newCVals = await newC.array();\n const newHVals = await newH.array();\n expect(newCVals[0][0]).toEqual(newCVals[1][0]);\n expect(newHVals[0][0]).toEqual(newHVals[1][0]);\n });\n\n it('basicLSTMCell accepts a tensor-like object', async () => {\n const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n const lstmBias = [0, 0, 0, 0];\n const forgetBias = 1;\n\n const data = [[0, 0]]; // 1x2\n const batchedData = tf.concat2d([data, data], 0); // 2x2\n const c = [[0]]; // 1x1\n const batchedC = tf.concat2d([c, c], 0); // 2x1\n const h = [[0]]; // 1x1\n const batchedH = tf.concat2d([h, h], 0); // 2x1\n const [newC, newH] = tf.basicLSTMCell(\n forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH);\n const newCVals = await newC.array();\n const newHVals = await newH.array();\n expect(newCVals[0][0]).toEqual(newCVals[1][0]);\n expect(newHVals[0][0]).toEqual(newHVals[1][0]);\n });\n});\ndescribeWithFlags('basicLSTMCell throws with non-tensor', ALL_ENVS, () => {\n it('input: forgetBias', () => {\n const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n const lstmBias = tf.randomNormal<Rank.R1>([4]);\n\n const data = tf.randomNormal<Rank.R2>([1, 2]);\n const batchedData = tf.concat2d([data, data], 0); // 2x2\n const c = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedC = tf.concat2d([c, c], 0); // 2x1\n const h = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedH = tf.concat2d([h, h], 0); // 2x1\n expect(\n () => tf.basicLSTMCell(\n {} as tf.Scalar, lstmKernel, lstmBias, batchedData, batchedC,\n batchedH))\n .toThrowError(\n /Argument 'forgetBias' passed to 'basicLSTMCell' must be a Tensor/);\n });\n it('input: lstmKernel', () => {\n const lstmBias = tf.randomNormal<Rank.R1>([4]);\n const forgetBias = tf.scalar(1.0);\n\n const data = tf.randomNormal<Rank.R2>([1, 2]);\n const batchedData = tf.concat2d([data, data], 0); // 2x2\n const c = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedC = tf.concat2d([c, c], 0); // 2x1\n const h = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedH = tf.concat2d([h, h], 0); // 2x1\n expect(\n () => tf.basicLSTMCell(\n forgetBias, {} as tf.Tensor2D, lstmBias, batchedData, batchedC,\n batchedH))\n .toThrowError(\n /Argument 'lstmKernel' passed to 'basicLSTMCell' must be a Tensor/);\n });\n it('input: lstmBias', () => {\n const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n const forgetBias = tf.scalar(1.0);\n\n const data = tf.randomNormal<Rank.R2>([1, 2]);\n const batchedData = tf.concat2d([data, data], 0); // 2x2\n const c = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedC = tf.concat2d([c, c], 0); // 2x1\n const h = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedH = tf.concat2d([h, h], 0); // 2x1\n expect(\n () => tf.basicLSTMCell(\n forgetBias, lstmKernel, {} as tf.Tensor1D, batchedData, batchedC,\n batchedH))\n .toThrowError(\n /Argument 'lstmBias' passed to 'basicLSTMCell' must be a Tensor/);\n });\n it('input: data', () => {\n const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n const lstmBias = tf.randomNormal<Rank.R1>([4]);\n const forgetBias = tf.scalar(1.0);\n\n const c = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedC = tf.concat2d([c, c], 0); // 2x1\n const h = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedH = tf.concat2d([h, h], 0); // 2x1\n expect(\n () => tf.basicLSTMCell(\n forgetBias, lstmKernel, lstmBias, {} as tf.Tensor2D, batchedC,\n batchedH))\n .toThrowError(\n /Argument 'data' passed to 'basicLSTMCell' must be a Tensor/);\n });\n it('input: c', () => {\n const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n const lstmBias = tf.randomNormal<Rank.R1>([4]);\n const forgetBias = tf.scalar(1.0);\n\n const data = tf.randomNormal<Rank.R2>([1, 2]);\n const batchedData = tf.concat2d([data, data], 0); // 2x2\n const h = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedH = tf.concat2d([h, h], 0); // 2x1\n expect(\n () => tf.basicLSTMCell(\n forgetBias, lstmKernel, lstmBias, batchedData, {} as tf.Tensor2D,\n batchedH))\n .toThrowError(\n /Argument 'c' passed to 'basicLSTMCell' must be a Tensor/);\n });\n it('input: h', () => {\n const lstmKernel = tf.randomNormal<Rank.R2>([3, 4]);\n const lstmBias = tf.randomNormal<Rank.R1>([4]);\n const forgetBias = tf.scalar(1.0);\n\n const data = tf.randomNormal<Rank.R2>([1, 2]);\n const batchedData = tf.concat2d([data, data], 0); // 2x2\n const c = tf.randomNormal<Rank.R2>([1, 1]);\n const batchedC = tf.concat2d([c, c], 0); // 2x1\n expect(\n () => tf.basicLSTMCell(\n forgetBias, lstmKernel, lstmBias, batchedData, batchedC,\n {} as tf.Tensor2D))\n .toThrowError(\n /Argument 'h' passed to 'basicLSTMCell' must be a Tensor/);\n });\n});\n"]} |