keras-js/test/convolutional/data_Convolution1D.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.Convolution1D.0': {
      input: {
        data: [0.528281, -0.994279, -0.285153, 0.81939, -0.087838, 0.963605, 0.734714, 0.972055, 0.846533, -0.392613],
        shape: [5, 2]
      },
      weights: [
        {
          data: [0.895265, -0.546905, 0.18884, -0.143383, 0.528281, -0.994279, -0.285153, 0.81939, -0.087838, 0.963605, 0.734714, 0.972055, 0.846533, -0.392613, 0.692207, -0.757556, 0.571153, -0.49899, -0.807941, 0.886982, 0.6521, 0.03665, 0.747001, 0.156751],
          shape: [4, 2, 3, 1]
        },
        {
          data: [0.895265, -0.546905, 0.18884, -0.143383],
          shape: [4]
        }
      ],
      expected: {
        data: [1.124918, -0.342879, 1.42759, -0.153716, 0.251835, 1.840331, -0.064904, 1.390416, 1.340388, 1.266877, 0.433117, 1.831188],
        shape: [3, 4]
      }
    },
    'convolutional.Convolution1D.1': {
      input: {
        data: [0.57107, 0.361384, -0.924121, -0.417132, -0.39254, 0.967698, -0.674584, 0.924125, 0.403362, 0.417301, 0.795356, -0.367641, -0.398474, 0.889135, -0.81216, 0.383587, 0.922044, 0.427167],
        shape: [6, 3]
      },
      weights: [
        {
          data: [-0.772191, 0.495762, 0.222119, 0.619384, 0.425715, -0.719926, -0.464976, -0.704791, -0.543864, -0.528877, 0.380048, -0.703304, -0.108788, 0.401685, -0.806723, 0.765265, 0.739665, 0.689188, -0.452596, 0.571359, 0.402272, -0.010539, 0.672675, -0.191632, -0.653554, -0.269196, 0.994178, -0.318691, 0.010759, 0.078695, -0.501326, 0.625487, -0.614715, -0.839499, -0.811676, -0.300069],
          shape: [4, 3, 3, 1]
        },
        {
          data: [0.895265, -0.546905, 0.18884, -0.143383],
          shape: [4]
        }
      ],
      expected: {
        data: [-1.877892, -0.642056, -0.468639, -1.365037, -0.876249, 0.22855, -0.661939, -0.585044, 1.423414, -0.225706, -0.233745, -0.120764, 0.283789, -1.702796, 1.034372, 0.323189],
        shape: [4, 4]
      }
    },
    'convolutional.Convolution1D.2': {
      input: {
        data: [0.18884, -0.143383, 0.528281, -0.994279, -0.285153, 0.81939, -0.087838, 0.963605, 0.734714, 0.972055, 0.846533, -0.392613, 0.692207, -0.757556, 0.571153, -0.49899, -0.807941, 0.886982, 0.6521, 0.03665, 0.747001, 0.156751, -0.099831, 0.360314],
        shape: [4, 6]
      },
      weights: [
        {
          data: [0.895265, -0.546905, 0.18884, -0.143383, 0.528281, -0.994279, -0.285153, 0.81939, -0.087838, 0.963605, 0.734714, 0.972055, 0.846533, -0.392613, 0.692207, -0.757556, 0.571153, -0.49899, -0.807941, 0.886982, 0.6521, 0.03665, 0.747001, 0.156751, -0.099831, 0.360314, -0.161149, 0.280787, 0.217313, -0.789132, 0.932089, 0.517401, 0.359284, -0.341304, -0.94709, 0.607321],
          shape: [2, 6, 3, 1]
        },
        {
          data: [0.895265, -0.546905],
          shape: [2]
        }
      ],
      expected: {
        data: [0.444624, 0.773535, 0.564385, 0.133453],
        shape: [2, 2]
      }
    },
    'convolutional.Convolution1D.3': {
      input: {
        data: [0.330694, 0.998309, 0.786447, 0.538158, -0.228315, 0.217332, -0.475436, -0.018066, -0.489741, -0.522387, 0.79989, 0.27058, -0.683115, -0.650208, 0.259853, -0.509243, 0.958185, 0.089546, 0.739799, 0.114385, -0.378872, -0.168716, 0.302124, 0.850416],
        shape: [8, 3]
      },
      weights: [
        {
          data: [0.861113, -0.237594, 0.330694, 0.998309, 0.786447, 0.538158, -0.228315, 0.217332, -0.475436, -0.018066, -0.489741, -0.522387, 0.79989, 0.27058, -0.683115, -0.650208, 0.259853, -0.509243, 0.958185, 0.089546, 0.739799, 0.114385, -0.378872, -0.168716, 0.302124, 0.850416, -0.984343, 0.839927, -0.895196, 0.303711, 0.128826, 0.058159, 0.254989, -0.759101, 0.793844, 0.647309, 0.252074, 0.075576, -0.859305, 0.952613, -0.053285, -0.677361],
          shape: [2, 3, 7, 1]
        },
        {
          data: [0.861113, -0.237594],
          shape: [2]
        }
      ],
      expected: {
        data: [0.854959, 0.355561, 0.888899, -0.9834, -0.825345, 0.941694, -0.490434, -0.699848, 0.872523, -0.887886, -0.408331, -0.018902, 0.959544, 0.66633, -0.779488, 0.038157],
        shape: [8, 2]
      }
    }
  }

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