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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('leakyrelu', ALL_ENVS, () => {\n it('basic', async () => {\n const a = tf.tensor1d([0, 1, -2]);\n const result = tf.leakyRelu(a);\n\n expect(result.shape).toEqual(a.shape);\n expectArraysClose(await result.data(), [0, 1, -0.4]);\n });\n\n it('propagates NaN', async () => {\n const a = tf.tensor1d([0, 1, NaN]);\n const result = tf.leakyRelu(a);\n\n expect(result.shape).toEqual(a.shape);\n expectArraysClose(await result.data(), [0, 1, NaN]);\n });\n\n it('gradients: Scalar', async () => {\n const a = tf.scalar(-4);\n const dy = tf.scalar(8);\n const alpha = 0.1;\n\n const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);\n\n expect(gradients.shape).toEqual(a.shape);\n expect(gradients.dtype).toEqual('float32');\n expectArraysClose(await gradients.data(), [8 * alpha]);\n });\n\n it('gradient with clones', async () => {\n const a = tf.scalar(-4);\n const dy = tf.scalar(8);\n const alpha = 0.1;\n\n const gradients =\n tf.grad((a) => tf.leakyRelu(a.clone(), alpha).clone())(a, dy);\n\n expect(gradients.shape).toEqual(a.shape);\n expect(gradients.dtype).toEqual('float32');\n expectArraysClose(await gradients.data(), [8 * alpha]);\n });\n\n it('gradients: Tensor1D', async () => {\n const aValues = [1, -1, 0.1];\n const dyValues = [1, 2, 3];\n const alpha = 0.1;\n\n const a = tf.tensor1d(aValues);\n const dy = tf.tensor1d(dyValues);\n\n const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);\n\n expect(gradients.shape).toEqual(a.shape);\n expect(gradients.dtype).toEqual('float32');\n\n expectArraysClose(await gradients.data(), [1, 2 * alpha, 3]);\n });\n\n it('gradients: Tensor2D', async () => {\n const aValues = [1, -1, 0.1, 0.5];\n const dyValues = [1, 2, 3, 4];\n const alpha = 0.1;\n\n const a = tf.tensor2d(aValues, [2, 2]);\n const dy = tf.tensor2d(dyValues, [2, 2]);\n\n const gradients = tf.grad((a) => tf.leakyRelu(a, alpha))(a, dy);\n\n expect(gradients.shape).toEqual(a.shape);\n expect(gradients.dtype).toEqual('float32');\n\n expectArraysClose(await gradients.data(), [1, 2 * alpha, 3, 4]);\n });\n\n it('throws when passed a non-tensor', () => {\n expect(() => tf.leakyRelu({} as tf.Tensor))\n .toThrowError(/Argument 'x' passed to 'leakyRelu' must be a Tensor/);\n });\n\n it('accepts a tensor-like object', async () => {\n const result = tf.leakyRelu([0, 1, -2]);\n\n expect(result.shape).toEqual([3]);\n expectArraysClose(await result.data(), [0, 1, -0.4]);\n });\n\n it('throws for string tensor', () => {\n expect(() => tf.leakyRelu('q'))\n .toThrowError(/Argument 'x' passed to 'leakyRelu' must be numeric/);\n });\n});\n"]} |