keras-js/test/convolutional/data_Cropping3D.js

// TEST DATA
// Keyed by mocha test ID
// Python code for generating test data can be found in the matching jupyter notebook in folder `notebooks/`.

(function () {
  var DATA = {
    'convolutional.Cropping3D.0': {
      input: {
        data: [-0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858, 0.617251, 0.334305, -0.351137, -0.642574, 0.108974, -0.993964, 0.051085, -0.372012, 0.843766, 0.088025, -0.598662, 0.789035, -0.971233, -0.917609, 0.10035, -0.993908, 0.001303, 0.151006, -0.222302, -0.462642, 0.318882, 0.910461, -0.951606, -0.628088, 0.858834, 0.18648, 0.005839, -0.976863, -0.602595, -0.907466, 0.537085, 0.254335, -0.624879, -0.26462, -0.597274, 0.717515, 0.933281, -0.928165, -0.664793, 0.350419, 0.604354, 0.134613, -0.170544, 0.287505, -0.342583, -0.908204, -0.055996, 0.686364, -0.877763, 0.666511, -0.333493, -0.283194, 0.764997, 0.702478, -0.495511, 0.451364, 0.945924, -0.238126, 0.146568, -0.281422, -0.379007, 0.914172, 0.337272, 0.175728, -0.945958, -0.727577, -0.133378, -0.069779, -0.952209, 0.237581, -0.244335, 0.838199, -0.187442, 0.255861, 0.28158, -0.60797, 0.992258, 0.16382, 0.240562, 0.353587, 0.300174, 0.216722, -0.422345, -0.239941, 0.354771, 0.567014, -0.085289, 0.751301, -0.255041, -0.656808, -0.220736, -0.275667, 0.568007, 0.251274, 0.888831, 0.976139, 0.040987, 0.474905, 0.181267, 0.707542, -0.570195, 0.897656, -0.91601, -0.419443, -0.346422, -0.770603, 0.883902, -0.490007, 0.470248, -0.217687, 0.365399],
        shape: [3, 5, 3, 3]
      },
      expected: {
        data: [-0.26462, -0.597274, 0.717515, -0.170544, 0.287505, -0.342583, -0.283194, 0.764997, 0.702478],
        shape: [1, 3, 1, 3]
      }
    },
    'convolutional.Cropping3D.1': {
      input: {
        data: [-0.806777, -0.564841, -0.481331, 0.559626, 0.274958, -0.659222, -0.178541, 0.689453, -0.028873, 0.053859, -0.446394, -0.53406, 0.776897, -0.700858, -0.802179, -0.616515, 0.718677, 0.303042, -0.080606, -0.850593, -0.795971, 0.860487, -0.90685, 0.89858, 0.617251, 0.334305, -0.351137, -0.642574, 0.108974, -0.993964, 0.051085, -0.372012, 0.843766, 0.088025, -0.598662, 0.789035, -0.971233, -0.917609, 0.10035, -0.993908, 0.001303, 0.151006, -0.222302, -0.462642, 0.318882, 0.910461, -0.951606, -0.628088, 0.858834, 0.18648, 0.005839, -0.976863, -0.602595, -0.907466, 0.537085, 0.254335, -0.624879, -0.26462, -0.597274, 0.717515, 0.933281, -0.928165, -0.664793, 0.350419, 0.604354, 0.134613, -0.170544, 0.287505, -0.342583, -0.908204, -0.055996, 0.686364, -0.877763, 0.666511, -0.333493, -0.283194, 0.764997, 0.702478, -0.495511, 0.451364, 0.945924, -0.238126, 0.146568, -0.281422, -0.379007, 0.914172, 0.337272, 0.175728, -0.945958, -0.727577, -0.133378, -0.069779, -0.952209, 0.237581, -0.244335, 0.838199, -0.187442, 0.255861, 0.28158, -0.60797, 0.992258, 0.16382, 0.240562, 0.353587, 0.300174, 0.216722, -0.422345, -0.239941, 0.354771, 0.567014, -0.085289, 0.751301, -0.255041, -0.656808, -0.220736, -0.275667, 0.568007, 0.251274, 0.888831, 0.976139, 0.040987, 0.474905, 0.181267, 0.707542, -0.570195, 0.897656, -0.91601, -0.419443, -0.346422, -0.770603, 0.883902, -0.490007, 0.470248, -0.217687, 0.365399],
        shape: [3, 5, 3, 3]
      },
      expected: {
        data: [-0.700858, -0.90685, -0.372012, -0.597274, 0.287505, 0.764997, 0.353587, -0.255041, 0.474905],
        shape: [3, 3, 1, 1]
      }
    },
    'convolutional.Cropping3D.2': {
      input: {
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        shape: [7, 6, 6, 6]
      },
      expected: {
        data: [0.665183, -0.117032, 0.377938, 0.750921, -0.78475, -0.864729, 0.767556, 0.980922, -0.868826, 0.06667, -0.738848, 0.422474, -0.949383, 0.872373, -0.45984, 0.275255, 0.676277, -0.078256, -0.943866, 0.112569, -0.76884],
        shape: [7, 1, 3, 1]
      }
    }
  }

  window.TEST_DATA = Object.assign({}, window.TEST_DATA, DATA)
})()