mirror of
https://github.com/wassname/keras-js.git
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119 lines
6.0 KiB
JavaScript
119 lines
6.0 KiB
JavaScript
// TEST DATA
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// Keyed by mocha test ID
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// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.
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(function () {
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var DATA = {
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'normalization.BatchNormalization.0': {
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input: {
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data: [0.193375, 0.789956, 0.069255, -0.988089, 0.804359, 0.509039, -0.655792, 0.460058, -0.25375, -0.635374, -0.109318, -0.426266],
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shape: [4, 3]
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},
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weights: [
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{ data: [0.307179, -0.769986, 0.900566], shape: [3] },
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{ data: [-0.387536, -0.469873, -0.60788], shape: [3] },
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{ data: [-0.742023, -0.077688, -0.167692], shape: [3] },
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{ data: [0.597806, 0.435934, 0.01687], shape: [3] }
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],
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expected: {
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data: [-0.015911, -1.481707, 1.034528, -0.485295, -1.498503, 4.082906, -0.353277, -1.096984, -1.204392, -0.345165, -0.432986, -2.400193],
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shape: [4, 3]
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}
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},
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'normalization.BatchNormalization.1': {
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input: {
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data: [0.718808, -0.159628, -0.955364, -0.945802, -0.433707, -0.559921, 0.418526, 0.035668, 0.099925, 0.537048, 0.914591, -0.504129],
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shape: [4, 3]
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},
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weights: [
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{ data: [-0.211487, -0.648815, -0.854588], shape: [3] },
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{ data: [0.311362, -0.228519, 0.253024], shape: [3] },
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{ data: [-0.946541, 0.585593, -0.49527], shape: [3] },
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{ data: [0.557039, 0.055171, 0.263987], shape: [3] }
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],
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expected: {
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data: [-0.156355, 1.665477, 1.004194, 0.311154, 2.362055, 0.358576, -0.07202, 1.169128, -0.718718, -0.105307, -1.064677, 0.267487],
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shape: [4, 3]
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}
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},
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'normalization.BatchNormalization.2': {
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input: {
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data: [-0.920023, 0.466664, 0.332702, 0.693696, -0.705391, 0.385741, 0.296606, 0.805754, -0.876679, -0.140908, -0.817375, 0.222433, -0.406426, 0.144013, 0.566715, 0.215483, -0.18429, 0.901878, 0.31492, -0.560544, 0.989602, 0.265541, 0.755267, 0.664617],
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shape: [4, 3, 2]
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},
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weights: [
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{ data: [0.517906, -0.666537, 0.378665, -0.380062], shape: [4] },
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{ data: [0.400557, 0.743871, 0.437134, -0.5548], shape: [4] },
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{ data: [-0.726393, 0.961405, -0.352651, -0.616831], shape: [4] },
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{ data: [0.91407, 0.630071, 0.508933, 0.052897], shape: [4] }
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],
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expected: {
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data: [0.295668, 1.046836, 0.974269, 1.16982, 0.411934, 1.003001, 1.302105, 0.874571, 2.287317, 1.669487, 2.237519, 1.364389, 0.408591, 0.700756, 0.925121, 0.738691, 0.526497, 1.103021, -2.094362, -0.647805, -3.209161, -2.012771, -2.821961, -2.672177],
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shape: [4, 3, 2]
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}
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},
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'normalization.BatchNormalization.3': {
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input: {
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data: [0.177239, -0.818004, -0.955256, -0.625241, 0.936162, 0.471789, -0.251405, 0.671525, -0.819759, 0.468665, 0.492218, 0.014732, -0.415985, 0.354683, -0.445545, -0.401559, 0.68942, 0.302571, -0.247906, -0.871673, -0.153346, -0.940188, 0.924139, 0.554809],
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shape: [4, 3, 2]
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},
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weights: [
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{ data: [-0.089873, -0.210305, -0.554444], shape: [3] },
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{ data: [-0.997964, -0.487343, -0.350362], shape: [3] },
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{ data: [-0.145437, 0.11872, -0.368563], shape: [3] },
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{ data: [0.486933, 0.587355, 0.517041], shape: [3] }
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],
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expected: {
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data: [-1.039522, -0.911343, -0.192635, -0.283194, -1.35639, -0.998328, -0.984316, -1.103182, -0.229817, -0.58337, -1.01408, -0.645908, -0.96312, -1.062375, -0.332504, -0.344574, -1.166136, -0.86785, -0.984767, -0.904431, -0.412686, -0.19677, -1.347119, -1.062342],
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shape: [4, 3, 2]
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}
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},
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'normalization.BatchNormalization.4': {
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input: {
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data: [-0.929182, 0.875771, -0.883762, 0.72433, -0.585509, -0.152172, -0.775304, 0.788189, -0.508879, 0.009276, 0.928936, -0.407103, 0.022937, -0.299328, -0.616092, -0.951088, -0.779181, 0.39148, -0.023818, 0.37291, -0.119784, -0.941465, -0.9535, -0.687951],
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shape: [4, 3, 2]
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},
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weights: [
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{ data: [0.660544, -0.047716], shape: [2] },
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{ data: [-0.956656, 0.127814], shape: [2] },
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{ data: [-0.359373, -0.71013], shape: [2] },
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{ data: [0.551285, 0.957558], shape: [2] }
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],
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expected: {
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data: [-1.463575, 0.050482, -1.423168, 0.057867, -1.157833, 0.100607, -1.326681, 0.054753, -1.089661, 0.092734, 0.189464, 0.113038, -0.616541, 0.107782, -1.18504, 0.139563, -1.33013, 0.074097, -0.658135, 0.075003, -0.74351, 0.139094, -1.485209, 0.126732],
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shape: [4, 3, 2]
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}
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},
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'normalization.BatchNormalization.5': {
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input: {
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data: [0.295997, -0.197309, 0.752622, -0.502156, -0.322833, 0.698127, 0.740562, -0.668644, -0.229012, -0.967928, -0.411317, 0.458195, 0.94757, 0.256302, -0.571923, 0.259773, -0.737641, -0.119598, -0.999641, 0.772751, -0.900172, 0.100827, 0.17941, 0.288567],
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shape: [4, 3, 2]
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},
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weights: [
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{ data: [0.833294, -0.732828], shape: [2] },
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{ data: [0.013718, -0.842599], shape: [2] }
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],
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expected: {
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data: [0.84691, -0.10986, 0.846988, -0.109791, -0.819544, -1.575399, 0.846992, -0.109788, 0.846959, -0.109817, -0.819535, -1.575391, 0.846953, -0.109822, -0.819532, -1.575388, -0.819506, -1.575365, -0.819562, -1.575414, -0.819543, -1.575398, -0.818029, -1.574066],
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shape: [4, 3, 2]
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}
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},
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'normalization.BatchNormalization.6': {
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input: {
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data: [-0.435983, -0.714349, -0.557696, -0.838492, 0.885101, -0.155057, 0.36079, 0.909788, 0.061332, -0.616654, 0.853256, -0.279329, -0.308602, 0.003971, -0.539738, 0.186558, 0.261057, 0.11644, -0.980123, -0.455651, 0.528218, 0.999738, -0.665862, 0.374054],
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shape: [4, 3, 2]
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},
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weights: [
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{ data: [0.128705, -0.956067], shape: [2] },
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{ data: [-0.168081, -0.9895], shape: [2] }
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],
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expected: {
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data: [-0.252569, 0.142873, -0.27886, 0.351052, 0.032798, -0.795018, -0.080458, -2.580686, -0.145144, -0.020954, 0.025919, -0.586624, -0.225054, -1.061697, -0.274981, -1.367882, -0.102002, -1.250299, -0.370108, -0.290944, -0.044292, -2.731525, -0.302225, -1.682298],
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shape: [4, 3, 2]
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}
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}
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}
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window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
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})()
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