Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 6528219162889228288
In this apply, we use a network to learn this target input, given it's pre-evaluated output:
TrainingTester.java:332 executed in 0.29 seconds (0.000 gc):
return RefArrays.stream(RefUtil.addRef(input_target)).flatMap(RefArrays::stream).map(x -> {
try {
return x.prettyPrint();
} finally {
x.freeRef();
}
}).reduce((a, b) -> a + "\n" + b).orElse("");
Returns
[
[ [ -0.464, -1.248, 1.668, -0.708, 1.86, 1.392, -0.672, -1.892, ... ], [ -1.308, 1.024, 1.316, 0.988, 1.816, 1.484, -0.744, -0.248, ... ], [ -1.96, 0.048, -1.544, -0.956, 1.8, 0.104, 0.448, -2.0, ... ], [ -0.42, 0.924, -1.028, 0.204, 0.62, -1.712, 0.336, -1.836, ... ], [ 1.448, -0.464, 0.728, 0.752, -1.42, -1.548, 0.104, 1.672, ... ], [ 0.284, -1.004, 1.648, -1.856, 1.808, 1.728, 0.268, -1.624, ... ], [ -1.952, 0.484, 0.696, 1.144, 1.524, -1.364, 0.536, -0.22, ... ], [ 1.64, 1.604, 0.516, 0.556, 0.128, -0.008, 1.216, -0.372, ... ], ... ],
[ [ -1.424, 0.584, 0.376, 0.64, -0.144, 0.888, -0.896, 1.316, ... ], [ 0.216, -0.708, -0.576, 0.472, 1.328, -1.4, -0.976, -0.572, ... ], [ 1.904, 0.08, 1.632, 1.668, -0.36, -1.564, 0.068, -1.664, ... ], [ -0.596, -0.604, -1.096, 1.172, 0.216, 1.536, -1.536, -1.996, ... ], [ 1.936, 1.52, -0.052, -0.84, -0.552, 0.88, 1.348, 0.484, ... ], [ -0.772, 1.2, -1.86, 1.404, 1.496, 1.376, 1.432, 1.52, ... ], [ -1.748, 1.328, 0.928, 1.2, 1.356, -1.932, -0.152, -0.96, ... ], [ 0.432, 1.916, -0.732, -1.876, -1.148, -1.292, 0.976, 1.852, ... ], ... ],
[ [ -0.416, 1.204, -1.66, 0.7, -0.616, 0.616, 1.028, -1.248, ... ], [ -0.136, -0.232, -1.064, -0.408, 1.956, 1.92, 1.924, -0.06, ... ], [ -1.348, -0.508, -0.176, 1.6, 1.352, 0.82, 0.56, -0.848, ... ], [ -1.672, 0.36, 0.992, 1.704, 0.02, 1.464, -0.868, 1.916, ... ], [ -0.892, -1.664, -0.948, -0.188, -0.312, -1.116, 1.412, 0.868, ... ], [ -0.568, 1.136, 1.408, 1.124, -1.496, -0.972, 0.34, -1.516, ... ], [ 1.612, 1.38, 1.528, 0.772, 1.5, -1.324, 1.572, -1.252, ... ], [ -0.612, 1.572, 1.968, -1.564, -0.384, 0.12, 1.964, -1.8, ... ], ... ],
[ [ -1.72, -1.848, 1.64, 0.492, 1.56, 0.516, 0.856, 0.512, ... ], [ -0.68, -0.104, 1.008, -1.096, 0.852, 0.392, -0.108, 1.296, ... ], [ -1.396, 1.78, 1.532, 1.612, 1.932, -0.868, -1.948, -1.404, ... ], [ 0.376, 1.608, -0.916, -0.708, 0.756, 0.728, 1.164, -1.744, ... ], [ 0.612, -1.196, -0.264, -0.956, 1.316, -0.488, -1.476, 0.132, ... ], [ -1.936, 0.708, -0.316, -0.556, 0.424, 0.616, 1.688, -1.248, ... ], [ -1.452, -1.856, -1.508, -0.044, 0.124, -0.744, -0.648, 0.312, ... ], [ -1.572, 0.384, -0.072, 0.104, -1.308, -0.76, 1.216, 1.512, ... ], ... ],
[ [ -1.156, 1.276, 0.732, -1.048, 0.612, -0.96, -0.896, -0.976, ... ], [ -1.328, 1.36, -1.22, 1.496, 0.284, -0.036, 1.972, 1.068, ... ], [ -1.288, 1.372, -1.612, 1.976, -0.772, 0.016, 0.852, 1.628, ... ], [ 0.472, 1.732, -0.128, -0.38, -0.072, 0.444, 0.176, -0.856, ... ], [ 0.432, -0.844, -1.4, -1.784, 0.144, -1.7, 0.136, 1.312, ... ], [ -1.744, 1.564, 0.6, 1.56, 1.28, 0.268, 0.712, -0.688, ... ], [ -0.712, 1.296, 0.972, 1.616, -1.832, -0.696, 0.792, -1.476, ... ], [ -1.056, 1.572, -0.472, 0.16, -0.812, 0.252, -1.144, -1.648, ... ], ... ],
[ [ 0.724, -1.084, 1.132, -0.544, 1.724, 0.88, -1.868, 1.632, ... ], [ 0.124, -0.136, 1.044, 1.832, 1.408, -0.528, -0.66, -0.204, ... ], [ -0.648, -1.568, 1.9, 1.052, 1.268, 0.996, -0.012, -0.712, ... ], [ -0.296, -0.8, 0.148, -1.228, -0.276, -1.544, -0.932, -0.228, ... ], [ 1.8, 1.584, 1.704, -1.4, 1.672, 0.152, -1.832, -0.252, ... ], [ 1.008, -0.588, -1.976, 1.572, -1.832, 0.18, 1.776, -0.868, ... ], [ -0.508, -1.96, 1.012, -0.12, 1.144, -1.364, -0.952, -1.812, ... ], [ 1.952, -1.448, 1.864, -1.316, 1.468, 1.36, 0.476, 1.3, ... ], ... ],
[ [ 0.704, -1.108, -1.732, 0.608, 0.596, -0.4, -1.604, -1.072, ... ], [ 1.376, -0.332, 1.144, 1.524, -1.992, -0.068, 1.444, 0.624, ... ], [ -1.556, 0.024, -0.816, -0.844, -1.26, -1.988, -0.468, 1.084, ... ], [ 0.352, -1.116, -0.832, 0.736, -0.032, -1.512, -1.4, 0.216, ... ], [ 0.516, -0.032, 1.516, 0.64, 1.284, -1.916, -0.104, 0.704, ... ], [ -1.244, 0.384, -1.484, -1.696, 1.236, -0.38, 1.692, -0.408, ... ], [ -0.82, 0.496, 1.888, 1.512, 1.708, -0.188, 1.168, -1.664, ... ], [ 0.532, -0.436, -0.692, 0.152, 0.724, 0.804, -0.82, -0.372, ... ], ... ],
[ [ 1.616, -1.768, -1.812, -1.82, -1.904, -1.544, -1.792, 0.772, ... ], [ -0.68, -0.268, -0.656, -0.672, -0.1, 1.908, 1.24, -1.072, ... ], [ -1.164, 1.28, 1.984, 0.368, 1.16, -0.1, -0.008, 0.876, ... ], [ 0.772, -0.464, -0.168, 1.228, 1.284, 1.152, 0.904, -0.64, ... ], [ -0.092, -0.512, 1.736, -0.752, -0.588, 0.7, -0.712, 1.784, ... ], [ 1.54, -1.444, 1.372, 1.988, -1.04, 0.668, 1.764, 1.3, ... ], [ 0.044, -1.572, 1.56, 1.632, -0.44, 0.992, 0.504, -1.276, ... ], [ 1.124, 0.936, -1.156, -1.116, 1.72, 0.332, -0.48, -1.544, ... ], ... ],
...
]
[
[ [ 0.876, -0.04, 0.628, -0.9, 0.396, -0.996, 0.972, 0.492, ... ], [ 0.176, 1.444, 1.604, 1.876, -1.796, 0.668, 0.86, 0.72, ... ], [ 1.388, 1.332, 1.256, -1.756, 1.508, -0.68, -1.62, 0.136, ... ], [ 1.128, -0.78, 0.192, 0.952, 1.456, -0.184, 0.612, 1.108, ... ], [ -1.984, -1.912, -1.204, -1.648, 1.532, -1.188, -1.064, -1.484, ... ], [ 1.968, -1.236, -0.496, -0.076, -1.672, -0.204, 1.148, -0.928, ... ], [ -0.212, 1.716, -1.176, -0.268, -1.812, 0.344, -1.784, 0.128, ... ], [ 0.984, 0.668, 0.044, -1.844, 1.964, 0.416, 1.28, -0.384, ... ], ... ],
[ [ -0.284, -0.74, 0.484, 1.38, 0.008, -0.4, -1.28, -0.5, ... ], [ 1.948, -0.804, -1.552, -0.78, -1.304, -0.092, -1.016, 1.292, ... ], [ 1.46, -0.212, 0.888, -1.544, -0.632, 0.496, 1.452, 1.556, ... ], [ 1.256, 0.888, -1.152, -0.388, 1.676, -1.4, 1.664, 0.012, ... ], [ 0.072, 0.96, -0.32, 1.296, -1.956, -0.7, -0.572, -0.516, ... ], [ 0.468, 1.376, -0.252, -1.68, 0.508, 0.012, -1.928, -1.772, ... ], [ -0.908, -1.924, 1.988, -0.832, -1.148, 1.428, -1.076, 1.456, ... ], [ 1.128, -1.756, -1.472, -0.812, 1.86, -1.86, 1.032, 0.108, ... ], ... ],
[ [ -1.52, -0.172, 0.78, -0.332, 0.264, 1.408, -1.948, -0.348, ... ], [ -0.068, -1.372, -0.68, -1.492, 1.056, 1.84, -1.216, -1.68, ... ], [ -1.224, -1.908, 1.02, 0.648, -0.512, -0.4, -0.804, -0.904, ... ], [ 0.212, 1.704, 1.04, 1.488, -0.024, 1.0, 0.976, -0.976, ... ], [ -1.66, -0.192, 1.996, -0.1, -1.36, -1.416, -1.32, 1.884, ... ], [ -0.32, 0.308, -1.724, 0.372, -0.8, 1.092, -1.124, -0.416, ... ], [ -1.316, 1.7, -0.3, -1.252, -0.476, -1.688, 1.496, -0.016, ... ], [ 0.676, 1.856, -1.812, 1.156, -0.188, 1.32, 1.66, 1.136, ... ], ... ],
[ [ 0.068, 1.68, 1.516, -0.604, -1.16, -1.988, 1.62, -1.46, ... ], [ -1.412, -0.728, -0.52, -0.956, 0.068, 1.024, -1.272, -0.164, ... ], [ 1.708, 0.16, -1.248, 1.364, -0.616, 0.74, 0.308, 1.556, ... ], [ 0.048, -0.852, 0.656, 0.084, 1.672, 0.82, -1.588, 0.304, ... ], [ 1.336, -1.144, -0.776, 0.532, -0.636, -1.456, 0.34, -0.48, ... ], [ 0.556, -0.328, -0.684, 1.304, -0.924, 1.384, -0.532, 0.284, ... ], [ 0.948, 0.3, -1.912, 1.624, -0.316, 0.832, -1.248, 0.76, ... ], [ 0.316, 0.24, -1.76, -1.784, 0.768, -1.48, 0.42, 0.844, ... ], ... ],
[ [ 1.712, -1.984, 0.028, 1.016, -0.404, 1.636, -0.64, -0.104, ... ], [ -0.576, 0.8, 0.772, -0.384, 0.656, -1.272, -1.048, -1.588, ... ], [ -0.348, 1.3, 1.052, -0.38, -1.444, -0.372, -1.624, -0.896, ... ], [ 1.64, -0.232, 1.576, 0.972, -1.952, -0.772, 1.776, -1.748, ... ], [ -1.684, 0.776, 0.844, -1.272, -0.648, -1.972, -0.868, 0.516, ... ], [ -1.96, 1.4, 1.62, 0.304, -0.128, 0.852, 1.68, -1.592, ... ], [ 1.384, -1.404, 1.604, -1.6, 0.484, -1.604, -1.072, 0.336, ... ], [ 0.516, 0.812, -0.896, -0.748, -0.552, 1.816, 0.068, 1.192, ... ], ... ],
[ [ -1.228, -0.84, 0.3, -1.664, -0.44, 1.996, -0.32, -0.344, ... ], [ -1.896, -0.668, 0.688, -1.472, 0.552, 0.288, -0.94, 0.084, ... ], [ -1.436, 0.828, 1.12, 0.836, -0.612, -1.844, -1.88, -1.968, ... ], [ -1.552, -1.04, 0.06, 0.16, 1.828, 1.664, 1.912, 1.824, ... ], [ -1.544, 0.412, -0.664, 0.076, -1.136, -1.956, -1.496, 1.48, ... ], [ 0.48, -1.872, -1.512, 0.368, 0.136, -1.1, -0.728, -1.796, ... ], [ 1.3, 0.028, 1.548, -0.936, 0.176, -0.788, 0.308, 0.844, ... ], [ 0.428, 1.036, 1.564, 1.12, 0.06, -0.98, 0.352, 1.86, ... ], ... ],
[ [ -1.828, 1.748, -1.904, -0.896, 1.984, 0.272, 1.36, -0.964, ... ], [ 0.84, 0.804, 1.024, -1.884, -1.784, -0.912, 1.568, 0.112, ... ], [ 1.936, -1.94, 0.94, -1.236, 0.992, -0.864, -1.74, 1.748, ... ], [ 1.552, -1.316, -1.768, -1.2, 1.312, -0.704, -1.552, 0.584, ... ], [ -1.124, -1.132, 1.912, 0.804, 0.556, -0.46, -1.876, -0.288, ... ], [ 1.676, -1.352, 1.796, -0.912, 0.28, -0.084, -0.4, -1.528, ... ], [ -1.696, -0.636, -0.088, 0.868, -1.484, -1.716, 0.956, -1.668, ... ], [ 1.972, -0.508, 0.52, -1.048, -1.156, -0.84, 1.836
...skipping 115070 bytes...
1.752, 1.864, -0.052, -1.608, ... ], [ -0.212, -1.12, 0.208, 0.568, 1.392, 0.88, -0.768, -0.04, ... ], [ 0.404, -1.72, -0.384, -1.884, -1.268, 1.512, 1.68, 0.26, ... ], [ 1.672, -0.396, 1.396, -1.512, -0.888, 1.784, -1.528, -0.728, ... ], [ -1.764, -0.384, -1.212, -0.976, 1.728, -0.016, 1.944, 1.624, ... ], [ 1.82, 1.2, 1.948, -1.784, 0.852, -1.64, -0.804, -0.744, ... ], [ -1.156, -1.728, -0.368, 1.008, 1.3, 0.02, 1.668, -0.412, ... ], [ 0.36, -0.856, 0.384, -0.62, -1.104, 0.908, 1.652, 1.168, ... ], ... ],
[ [ -0.848, -1.956, 0.552, 1.46, 1.08, -1.776, -1.34, 0.196, ... ], [ -0.02, 0.752, 1.196, -1.036, 0.096, -0.168, 0.304, 1.804, ... ], [ -1.948, -0.464, 1.88, 0.056, 1.776, -0.208, 1.9, 1.076, ... ], [ 1.056, 1.556, 0.62, -1.684, -0.416, 1.568, 0.296, 0.04, ... ], [ -0.872, -1.268, 0.56, 0.876, 0.148, 1.232, 0.556, -1.18, ... ], [ 0.112, -0.912, -0.896, -1.424, 0.864, -0.992, 0.104, -0.212, ... ], [ -0.876, -1.196, 1.168, -0.088, -0.244, -0.7, -0.456, -1.796, ... ], [ -1.936, 1.304, 0.34, -0.276, 0.88, -1.048, -1.896, 1.352, ... ], ... ],
[ [ 1.872, 0.052, 1.28, -1.188, -0.948, -1.448, -1.82, -1.88, ... ], [ -0.816, -1.776, -1.516, -1.044, -0.192, 0.284, 0.776, -0.024, ... ], [ 1.904, -1.424, 0.112, 0.284, -0.12, -1.128, 0.164, -1.632, ... ], [ 1.016, -1.372, -1.396, -0.232, 1.424, -1.628, 0.332, 1.196, ... ], [ 0.996, 1.788, -1.34, 1.252, -1.388, -0.876, 1.252, -1.372, ... ], [ 0.064, 0.012, -0.692, 1.548, -0.216, -1.348, 0.12, -0.46, ... ], [ -0.168, 0.728, 1.988, -1.852, -0.044, 1.74, -1.988, 1.528, ... ], [ 0.26, 0.488, 1.52, -0.088, -1.38, 0.408, 1.496, -0.916, ... ], ... ],
[ [ -1.792, -0.26, 0.772, -1.836, 0.768, -1.928, 0.944, -1.104, ... ], [ -0.308, 1.308, -1.988, 1.28, -0.648, -1.58, 0.772, 0.728, ... ], [ 1.724, 1.408, 1.792, -1.412, 1.332, 0.584, 1.608, 1.82, ... ], [ 1.352, -1.604, -1.776, 0.484, 1.312, -1.96, -1.312, -1.752, ... ], [ -1.62, 1.648, 1.776, -0.22, -0.628, 1.804, -1.388, 1.544, ... ], [ -1.276, -0.852, -1.884, -0.604, 0.016, -0.536, 1.908, -0.916, ... ], [ -1.98, 1.76, 0.452, -1.908, -1.276, -0.104, 0.064, 1.276, ... ], [ 0.476, 1.408, -1.776, -0.348, 0.12, -0.608, 0.252, -0.272, ... ], ... ],
[ [ -0.52, -0.052, 0.94, -0.868, 0.244, -0.788, 1.364, -1.852, ... ], [ 1.02, -0.528, -0.88, -0.56, -1.8, 0.012, -1.76, -0.96, ... ], [ -0.588, 1.664, -1.644, -0.568, -0.528, -0.764, 1.068, -1.16, ... ], [ -0.604, -1.08, 0.096, 0.1, -0.208, 0.248, 1.592, 0.412, ... ], [ 0.596, -1.908, -0.268, -0.76, 1.1, 0.376, 1.188, -0.944, ... ], [ -0.172, -0.876, 0.672, 0.98, -0.752, 1.732, -0.584, 0.1, ... ], [ 0.192, 0.168, 1.708, 0.776, 1.48, -0.268, 0.764, 0.616, ... ], [ 1.64, -0.276, 0.552, -1.888, -1.756, -0.832, 0.92, 0.676, ... ], ... ],
[ [ 0.772, -0.636, -0.476, 0.024, 0.36, -0.472, 0.164, -0.236, ... ], [ 0.804, 1.072, 1.564, 1.66, -1.392, -1.444, -0.404, -0.756, ... ], [ -0.292, -0.924, -0.108, 0.58, -1.172, 0.884, -1.376, 1.212, ... ], [ -1.456, 0.944, 0.356, -1.324, -0.096, 0.384, 0.56, 0.664, ... ], [ 1.94, 1.28, -0.672, -1.828, -0.4, -1.804, -1.868, 0.76, ... ], [ 1.068, -0.616, 0.444, 0.668, -0.176, -0.744, -1.832, 1.556, ... ], [ 1.128, -1.968, 0.596, 0.372, 0.74, 0.664, -1.648, -0.256, ... ], [ 1.368, 1.584, 0.72, 0.176, 1.056, 1.132, -1.648, -0.472, ... ], ... ],
[ [ 1.972, -0.62, -0.844, -1.764, 0.6, -1.744, 1.892, 0.468, ... ], [ 0.824, -0.756, -1.628, 1.124, -0.72, 0.72, -1.576, -1.764, ... ], [ -1.404, -1.1, -1.972, -0.2, -1.324, 0.128, 1.632, 1.68, ... ], [ -0.848, -1.648, 0.372, 1.712, -1.52, -0.944, 1.252, 1.464, ... ], [ -0.448, 0.396, -0.952, 1.448, -0.112, 1.252, -0.276, 1.72, ... ], [ 1.764, 0.904, -0.84, 0.56, 1.532, -0.848, 0.296, 0.8, ... ], [ -1.076, -0.876, 1.06, 0.936, 1.432, -1.792, 1.284, 0.26, ... ], [ -1.9, 0.912, -1.028, 0.472, 0.152, -1.932, 1.292, 1.172, ... ], ... ],
...
]
[
[ [ 1.644, 1.96, -1.624, 1.972, -0.844, 1.736, 1.112, -0.48, ... ], [ 0.616, -0.372, 1.752, 1.012, -1.984, 1.112, 1.484, -1.136, ... ], [ 1.156, -0.44, 0.876, 1.4, 0.104, 1.048, 0.4, 0.72, ... ], [ -1.272, -1.128, 0.088, 0.872, 1.024, 0.704, -1.828, 1.904, ... ], [ 1.892, -0.304, 1.992, -0.676, 1.812, 1.5, -0.64, -0.128, ... ], [ 0.764, -1.596, -1.308, -0.068, -0.912, 1.684, -0.908, -0.48, ... ], [ -0.212, -0.012, 0.72, -0.696, -1.132, 1.644, 0.036, -0.752, ... ], [ -1.036, 0.528, 1.232, -1.472, -0.94, 0.356, 1.644, 1.004, ... ], ... ],
[ [ 0.832, -0.492, 0.94, -1.04, 0.628, -0.876, -0.704, -1.684, ... ], [ 1.212, 1.944, 0.868, 1.124, 1.408, -1.176, -1.188, -1.616, ... ], [ 0.384, 0.876, 1.972, -0.872, -0.104, 1.032, -0.356, 1.1, ... ], [ 1.344, 0.508, 1.156, -1.564, -1.86, -0.016, 1.868, 0.056, ... ], [ 1.64, 0.124, 0.328, -0.856, 1.828, -1.176, -1.932, -0.804, ... ], [ 1.76, -0.368, 0.248, 0.38, -1.304, -0.956, 0.4, 0.596, ... ], [ -0.32, -0.332, -1.452, -0.444, -1.332, -0.444, -0.376, 1.668, ... ], [ -1.668, 0.144, 1.116, -1.044, 1.164, -0.164, -0.552, -0.884, ... ], ... ],
[ [ 1.916, 1.956, -0.296, -0.728, -1.032, 1.808, -1.056, -0.692, ... ], [ 1.172, -0.756, -0.936, -1.232, 1.232, 1.636, -0.792, 0.084, ... ], [ 0.528, -0.28, -0.276, -1.692, -1.128, 0.984, 0.968, -1.74, ... ], [ -1.492, -1.392, -0.36, 1.624, -1.32, 1.508, -0.748, 0.412, ... ], [ -1.252, 0.16, -0.48, 1.688, 1.704, 1.156, -0.524, 1.084, ... ], [ 0.244, -0.848, 0.164, 0.908, -0.588, -1.52, -1.552, 0.704, ... ], [ -0.912, -0.656, 1.976, -1.104, -1.672, -1.452, -1.284, -1.508, ... ], [ 1.968, -1.152, 0.724, 1.416, -0.128, 0.472, -0.888, 0.7, ... ], ... ],
[ [ 1.632, 1.96, 1.432, 0.104, 0.856, -0.964, -1.928, 0.0, ... ], [ -0.776, 0.056, -1.896, 1.452, 1.096, -1.224, 1.84, -0.564, ... ], [ 0.216, -0.052, -0.184, -1.468, -1.084, -1.356, -0.424, -0.708, ... ], [ -1.468, -1.908, 1.676, -0.056, -0.54, -0.452, 0.152, 1.896, ... ], [ 1.488, 1.644, 1.268, -1.676, -0.724, -0.3, -0.192, 0.108, ... ], [ 0.944, 0.832, -0.176, 0.1, 1.22, 0.896, -1.584, 0.22, ... ], [ 0.836, 0.656, 0.268, -1.56, 0.012, -0.788, -1.584, -1.86, ... ], [ -0.644, -1.764, -0.516, 0.464, 1.344, 1.5, -0.664, 0.756, ... ], ... ],
[ [ -1.216, -1.216, 1.112, 0.712, -1.044, -0.468, 1.796, 1.26, ... ], [ 0.012, -0.18, 0.872, -0.78, -0.156, -1.288, 1.908, 0.772, ... ], [ 0.14, -1.9, 1.86, -0.5, 0.152, 0.84, -1.736, -0.32, ... ], [ 1.624, -1.996, -0.912, -0.66, -1.832, 0.608, 1.992, -1.58, ... ], [ -0.22, 0.216, -0.84, 0.604, -0.904, 0.868, 0.144, -1.2, ... ], [ -1.932, -1.804, -1.328, -1.528, 1.344, 0.74, -0.628, 0.364, ... ], [ 0.304, 0.356, 1.268, 1.704, -0.296, 0.696, -1.1, 1.84, ... ], [ -0.664, 1.364, -0.36, -1.936, -0.948, 1.06, -1.596, -1.108, ... ], ... ],
[ [ -0.136, -0.212, 1.216, -1.756, 1.264, -1.452, -0.784, 1.82, ... ], [ -0.692, -1.1, 1.764, -0.884, -1.364, 1.628, -0.532, 1.72, ... ], [ 0.188, 1.092, 0.54, 0.832, -1.696, -1.292, -0.932, 0.568, ... ], [ -1.968, 1.656, -0.592, 0.912, 1.636, 0.348, -1.188, -1.704, ... ], [ 0.496, -0.188, -1.924, -0.252, -0.808, -1.856, -0.432, -1.296, ... ], [ -0.012, -0.844, 0.292, -1.74, 1.076, 1.264, 0.372, 1.884, ... ], [ 1.088, 1.656, -0.808, -1.668, -1.044, -0.572, -0.728, -0.448, ... ], [ 1.58, -0.244, -0.132, -1.788, -0.212, 1.172, -1.216, -0.016, ... ], ... ],
[ [ 0.204, 0.068, 1.324, -1.788, 0.364, -0.588, 0.416, -0.08, ... ], [ -1.012, -0.38, -1.844, -0.688, 1.176, -1.032, 0.768, -0.3, ... ], [ 1.12, -1.512, 1.344, -1.268, 1.612, -1.336, 1.8, 1.316, ... ], [ 1.3, 1.332, 1.32, -0.744, 1.312, 1.92, -1.072, -1.304, ... ], [ 1.68, -0.068, -1.1, -0.66, 0.744, -0.98, 1.596, 1.0, ... ], [ 0.708, 0.568, -1.416, 1.12, 1.52, -1.772, 1.612, 0.324, ... ], [ 1.092, -0.756, 0.932, 0.396, 0.516, -1.352, -1.876, 0.272, ... ], [ 1.124, -1.764, -1.632, 0.336, 0.764, -0.196, -0.384, -1.764, ... ], ... ],
[ [ -0.64, 0.1, -0.616, 1.552, 1.556, 0.004, -0.524, 0.356, ... ], [ 1.576, 0.96, 1.6, -1.736, -0.664, 1.552, -1.164, 1.1, ... ], [ -1.888, -1.888, 0.6, 0.276, -0.412, 0.056, -1.432, -1.748, ... ], [ -1.652, 0.604, 1.912, 1.888, -1.664, -1.916, -0.736, 1.252, ... ], [ -1.092, -1.5, 1.948, 1.388, 0.904, -0.104, 1.792, -1.26, ... ], [ -1.608, 1.0, 1.652, 1.308, 0.656, 0.228, -1.46, 1.868, ... ], [ 1.608, -0.5, 1.484, 0.868, 0.184, 1.288, -1.848, -0.852, ... ], [ -1.192, -1.512, 1.904, -0.092, 1.076, 0.824, 0.436, -1.984, ... ], ... ],
...
]
First, we train using basic gradient descent method apply weak line search conditions.
TrainingTester.java:480 executed in 30.11 seconds (0.473 gc):
IterativeTrainer iterativeTrainer = new IterativeTrainer(trainable.addRef());
try {
iterativeTrainer.setLineSearchFactory(label -> new ArmijoWolfeSearch());
iterativeTrainer.setOrientation(new GradientDescent());
iterativeTrainer.setMonitor(TrainingTester.getMonitor(history));
iterativeTrainer.setTimeout(30, TimeUnit.SECONDS);
iterativeTrainer.setMaxIterations(250);
iterativeTrainer.setTerminateThreshold(0);
return iterativeTrainer.run();
} finally {
iterativeTrainer.freeRef();
}
Reset training subject: 10912477386382
Reset training subject: 10912868196004
Constructing line search parameters: GD
th(0)=2.670427437656747;dx=-9.115058987201698E-6
New Minimum: 2.670427437656747 > 2.670407799893566
WOLFE (weak): th(2.154434690031884)=2.670407799893566; dx=-9.115025472024251E-6 evalInputDelta=1.963776318092414E-5
New Minimum: 2.670407799893566 > 2.670388162202592
WOLFE (weak): th(4.308869380063768)=2.670388162202592; dx=-9.114991956846809E-6 evalInputDelta=3.92754541551632E-5
New Minimum: 2.670388162202592 > 2.6703096121607572
WOLFE (weak): th(12.926608140191302)=2.6703096121607572; dx=-9.114857896137032E-6 evalInputDelta=1.178254959897096E-4
New Minimum: 2.6703096121607572 > 2.669956151269341
WOLFE (weak): th(51.70643256076521)=2.669956151269341; dx=-9.114254622943035E-6 evalInputDelta=4.7128638740590034E-4
New Minimum: 2.669956151269341 > 2.6680714216277797
WOLFE (weak): th(258.53216280382605)=2.6680714216277797; dx=-9.111037165908392E-6 evalInputDelta=0.0023560160289672893
New Minimum: 2.6680714216277797 > 2.6563069380353053
WOLFE (weak): th(1551.1929768229563)=2.6563069380353053; dx=-9.090928059441868E-6 evalInputDelta=0.014120499621441684
New Minimum: 2.6563069380353053 > 2.5723700065456265
WOLFE (weak): th(10858.350837760694)=2.5723700065456265; dx=-8.9461424928829E-6 evalInputDelta=0.09805743111112042
New Minimum: 2.5723700065456265 > 1.9373243163805285
END: th(86866.80670208555)=1.9373243163805285; dx=-7.763727032651327E-6 evalInputDelta=0.7331031212762185
Fitness changed from 2.670427437656747 to 1.9373243163805285
Iteration 1 complete. Error: 1.9373243163805285 Total: 2.9669; Orientation: 0.0610; Line Search: 2.3421
th(0)=1.9373243163805285;dx=-6.612733666578871E-6
New Minimum: 1.9373243163805285 > 0.8973984084877697
END: th(187148.86177126726)=0.8973984084877697; dx=-4.50062174526171E-6 evalInputDelta=1.0399259078927587
Fitness changed from 1.9373243163805285 to 0.8973984084877697
Iteration 2 complete. Error: 0.8973984084877697 Total: 0.5004; Orientation: 0.0567; Line Search: 0.3712
th(0)=0.8973984084877697;dx=-3.0631199009715876E-6
New Minimum: 0.8973984084877697 > 0.08728468837312615
END: th(403200.00000000006)=0.08728468837312615; dx=-9.553013297558105E-7 evalInputDelta=0.8101137201146436
Fitness changed from 0.8973984084877697 to 0.08728468837312615
Iteration 3 complete. Error: 0.08728468837312615 Total: 0.9460; Orientation: 0.0580; Line Search: 0.8229
th(0)=0.08728468837312615;dx=-2.97931736313604E-7
New Minimum: 0.08728468837312615 > 0.020322681298037448
WOLF (strong): th(868668.0670208557)=0.020322681298037448; dx=1.437600575854137E-7 evalInputDelta=0.0669620070750887
New Minimum: 0.020322681298037448 > 0.005843240232427567
END: th(434334.03351042786)=0.005843240232427567; dx=-7.708583936409513E-8 evalInputDelta=0.08144144814069859
Fitness changed from 0.08728468837312615 to 0.005843240232427567
Iteration 4 complete. Error: 0.005843240232427567 Total: 1.6428; Orientation: 0.0957; Line Search: 1.3436
th(0)=0.005843240232427567;dx=-1.9944926660019432E-8
New Minimum: 0.005843240232427567 > 0.00208260866732291
WOLF (strong): th(935744.3088563365)=0.00208260866732291; dx=1.1907193425604384E-8 evalInputDelta=0.003760631565104657
New Minimum: 0.00208260866732291 > 2.3724443798385226E-4
END: th(467872.15442816826)=2.3724443798385226E-4; dx=-4.018866617207524E-9 evalInputDelta=0.005605995794443715
Fitness changed from 0.005843240232427567 to 2.3724443798385226E-4
Iteration 5 complete. Error: 2.3724443798385226E-4 Total: 1.1321; Orientation: 0.1482; Line Search: 0.8777
th(0)=2.3724443798385226E-4;dx=-8.097943483182158E-10
New Minimum: 2.3724443798385226E-4 > 1.2309686318168265E-4
WOLF (strong): th(1008000.0000000003)=1.2309686318168265E-4; dx=5.833110649805777E-10 evalInputDelta=1.1414757480216961E-4
New Minimum: 1.2309686318168265E-4 > 4.639368507119415E-6
END: th(504000.0000000002)=4.639368507119415E-6; dx=-1.1324164166881898E-10 evalInputDelta=2.3260506947673284E-4
Fitness changed from 2.3724443798385226E-4 to 4.639368507119415E-6
Iteration 6 complete. Error: 4.639368507119415E-6 Total: 0.8718; Orientation: 0.1432; Line Search: 0.6661
Low gradient: 3.979410907529858E-6
th(0)=4.639368507119415E-6;dx=-1.5835711170967605E-11
New Minimum: 4.639368507119415E-6 > 3.376901226754229E-6
WOLF (strong): th(1085835.0837760698)=3.376901226754229E-6; dx=1.3510372269640409E-11 evalInputDelta=1.262467280365186E-6
New Minimum: 3.376901226754229E-6 > 2.5008995532804768E-8
END: th(542917.5418880349)=2.5008995532804768E-8; dx=-1.1626694506636008E-12 evalInputDelta=4.6143595115866106E-6
Fitness changed from 4.639368507119415E-6 to 2.5008995532804768E-8
Iteration 7 complete. Error: 2.5008995532804768E-8 Total: 0.9117; Orientation: 0.0553; Line Search: 0.7959
Low gradient: 2.921712478758082E-7
th(0)=2.5008995532804768E-8;dx=-8.536403808530696E-14
New Minimum: 2.5008995532804768E-8 > 2.4822005266401716E-8
WOLF (strong): th(1169680.3860704207)=2.4822005266401716E-8; dx=8.504430926257915E-14 evalInputDelta=1.8699026640305242E-10
New Minimum: 2.4822005266401716E-8 > 8.77101660058084E-14
END: th(584840.1930352104)=8.77101660058084E-14; dx=-1.598644113639404E-16 evalInputDelta=2.500890782263876E-8
Fitness changed from 2.5008995532804768E-8 to 8.77101660058084E-14
Iteration 8 complete. Error: 8.77101660058084E-14 Total: 0.9229; Orientation: 0.0853; Line Search: 0.7731
Low gradient: 5.471599704837937E-10
th(0)=8.77101660058084E-14;dx=-2.9938403329982605E-19
Armijo: th(1260000.0000000005)=1.1607740192943665E-13; dx=3.44411391908718E-19 evalInputDelta=-2.8367235923628257E-14
New Minimum: 8.77101660058084E-14 > 4.960044971594768E-16
WOLF (strong): th(630000.0000000002)=4.960044971594768E-16; dx=2.2513679303919743E-20 evalInputDelta=8.721416150864892E-14
END: th(210000.0000000001)=3.610593895409713E-14; dx=-1.9208479576546933E-19 evalInputDelta=5.1604227051711263E-14
Fitness changed from 8.77101660058084E-14 to 4.960044971594768E-16
Iteration 9 complete. Error: 4.960044971594768E-16 Total: 1.3412; Orientation: 0.1271; Line Search: 1.1537
Zero gradient: 4.11464297799661E-11
th(0)=4.960044971594768E-16;dx=-1.6930286836376813E-21
New Minimum: 4.960044971594768E-16 > 2.575051945454523E-17
END: th(452431.2849066958)=2.575051945454523E-17; dx=-3.857576133983531E-22 evalInputDelta=4.702539777049315E-16
Fitness changed from 4.960044971594768E-16 to 2.575051945454523E-17
Iteration 10 complete. Error: 2.575051945454523E-17 Total: 0.5950; Orientation: 0.1501; Line Search: 0.3824
Zero gradient: 9.37523900521196E-12
th(0)=2.575051945454523E-17;dx=-8.789510640484773E-23
New Minimum: 2.575051945454523E-17 > 1.133776182731892E-17
WOLF (strong): th(974733.6550586841)=1.133776182731892E-17; dx=5.832239686941138E-23 evalInputDelta=1.4412757627226312E-17
New Minimum: 1.133776182731892E-17 > 7.287504670142387E-19
END: th(487366.82752934203)=7.287504670142387E-19; dx=-1.4786354772444533E-23 evalInputDelta=2.5021768987530992E-17
Fitness changed from 2.575051945454523E-17 to 7.287504670142387E-19
Iteration 11 complete. Error: 7.287504670142387E-19 Total: 1.1048; Orientation: 0.0596; Line Search: 0.9777
Zero gradient: 1.577170967505405E-12
th(0)=7.287504670142387E-19;dx=-2.487468260741935E-24
New Minimum: 7.287504670142387E-19 > 4.57118933399038E-19
WOLF (strong): th(1050000.0000000005)=4.57118933399038E-19; dx=1.9700748634941526E-24 evalInputDelta=2.716315336152007E-19
New Minimum: 4.57118933399038E-19 > 7.882165093163631E-21
END: th(525000.0000000002)=7.882165093163631E-21; dx=-2.586966998053294E-25 evalInputDelta=7.20868301921075E-19
Fitness changed from 7.287504670142387E-19 to 7.882165093163631E-21
Iteration 12 complete. Error: 7.882165093163631E-21 Total: 1.1079; Orientation: 0.1310; Line Search: 0.9116
Zero gradient: 1.640257810569176E-13
th(0)=7.882165093163631E-21;dx=-2.6904456851331866E-26
New Minimum: 7.882165093163631E-21 > 6.82276114267994E-21
WOLF (strong): th(1131078.2122667395)=6.82276114267994E-21; dx=2.5031193017020176E-26 evalInputDelta=1.0594039504836904E-21
New Minimum: 6.82276114267994E-21 > 9.552891000389661E-24
END: th(565539.1061333697)=9.552891000389661E-24; dx=-9.366317524506457E-28 evalInputDelta=7.872612202163241E-21
Fitness changed from
...skipping 744 bytes...
complete. Error: 1.506831538198693E-26 Total: 1.1675; Orientation: 0.0568; Line Search: 1.0459
Zero gradient: 2.267888515128453E-16
th(0)=1.506831538198693E-26;dx=-5.143318317051538E-32
New Minimum: 1.506831538198693E-26 > 9.670480997540293E-28
END: th(437500.00000000023)=9.670480997540293E-28; dx=-1.3029704451500194E-32 evalInputDelta=1.41012672822329E-26
Fitness changed from 1.506831538198693E-26 to 9.670480997540293E-28
Iteration 15 complete. Error: 9.670480997540293E-28 Total: 0.9261; Orientation: 0.1825; Line Search: 0.5664
Zero gradient: 5.745308968042612E-17
th(0)=9.670480997540293E-28;dx=-3.300857513827087E-33
New Minimum: 9.670480997540293E-28 > 3.582947649305908E-28
WOLF (strong): th(942565.1768889497)=3.582947649305908E-28; dx=2.0091924793411175E-33 evalInputDelta=6.087533348234385E-28
New Minimum: 3.582947649305908E-28 > 3.704207815412285E-29
END: th(471282.58844447485)=3.704207815412285E-29; dx=-6.460062011994376E-34 evalInputDelta=9.300060215999064E-28
Fitness changed from 9.670480997540293E-28 to 3.704207815412285E-29
Iteration 16 complete. Error: 3.704207815412285E-29 Total: 1.0105; Orientation: 0.0542; Line Search: 0.8965
Zero gradient: 1.1244419064558472E-17
th(0)=3.704207815412285E-29;dx=-1.26436960099406E-34
New Minimum: 3.704207815412285E-29 > 1.991589848689726E-29
WOLF (strong): th(1015347.5573527961)=1.991589848689726E-29; dx=9.270442149156593E-35 evalInputDelta=1.712617966722559E-29
New Minimum: 1.991589848689726E-29 > 6.614734298674045E-31
END: th(507673.77867639804)=6.614734298674045E-31; dx=-1.68660733629287E-35 evalInputDelta=3.6380604724255445E-29
Fitness changed from 3.704207815412285E-29 to 6.614734298674045E-31
Iteration 17 complete. Error: 6.614734298674045E-31 Total: 1.2362; Orientation: 0.1211; Line Search: 1.0522
Zero gradient: 1.5026075027367395E-18
th(0)=6.614734298674045E-31;dx=-2.25782930728074E-36
New Minimum: 6.614734298674045E-31 > 4.8673963018335895E-31
WOLF (strong): th(1093750.0000000007)=4.8673963018335895E-31; dx=1.932741544104075E-36 evalInputDelta=1.7473379968404554E-31
New Minimum: 4.8673963018335895E-31 > 2.4708660734088875E-33
END: th(546875.0000000003)=2.4708660734088875E-33; dx=-8.195163330530571E-38 evalInputDelta=6.590025637939956E-31
Fitness changed from 6.614734298674045E-31 to 2.4708660734088875E-33
Iteration 18 complete. Error: 2.4708660734088875E-33 Total: 1.5237; Orientation: 0.1877; Line Search: 1.0848
Zero gradient: 9.183621034520645E-20
th(0)=2.4708660734088875E-33;dx=-8.433889530569002E-39
Armijo: th(1178206.4711111872)=2.5282555428770155E-33; dx=8.499247364877913E-39 evalInputDelta=-5.738946946812804E-35
New Minimum: 2.4708660734088875E-33 > 4.5600878823647325E-41
WOLF (strong): th(589103.2355555936)=4.5600878823647325E-41; dx=2.880902591062813E-44 evalInputDelta=2.4708660278080087E-33
WOLFE (weak): th(196367.74518519788)=2.3649046621423717E-33; dx=-8.204972361754106E-39 evalInputDelta=1.0596141126651578E-34
END: th(392735.49037039577)=2.8155105223686347E-35; dx=-2.2884176221357084E-40 evalInputDelta=2.442710968185201E-33
Fitness changed from 2.4708660734088875E-33 to 4.5600878823647325E-41
Iteration 19 complete. Error: 4.5600878823647325E-41 Total: 1.3384; Orientation: 0.0605; Line Search: 1.2131
Zero gradient: 1.2476016981314571E-23
th(0)=4.5600878823647325E-41;dx=-1.5565099971804955E-46
New Minimum: 4.5600878823647325E-41 > 8.991593944584021E-42
WOLF (strong): th(846122.9644606635)=8.991593944584021E-42; dx=6.911680453189518E-47 evalInputDelta=3.6609284879063303E-41
New Minimum: 8.991593944584021E-42 > 3.5235862782060035E-42
END: th(423061.48223033175)=3.5235862782060035E-42; dx=-4.326709759307718E-47 evalInputDelta=4.207729254544132E-41
Fitness changed from 4.5600878823647325E-41 to 3.5235862782060035E-42
Iteration 20 complete. Error: 3.5235862782060035E-42 Total: 1.1480; Orientation: 0.0541; Line Search: 1.0314
Zero gradient: 3.468021697780522E-24
th(0)=3.5235862782060035E-42;dx=-1.2027174496276494E-47
New Minimum: 3.5235862782060035E-42 > 1.0875266290759323E-42
WOLF (strong): th(911458.3333333341)=1.0875266290759323E-42; dx=6.681763609042513E-48 evalInputDelta=2.436059649130071E-42
New Minimum: 1.0875266290759323E-42 > 1.7400426065214724E-43
END: th(455729.16666666704)=1.7400426065214724E-43; dx=-2.6727054436169905E-48 evalInputDelta=3.3495820175538565E-42
Fitness changed from 3.5235862782060035E-42 to 1.7400426065214724E-43
Iteration 21 complete. Error: 1.7400426065214724E-43 Total: 1.1557; Orientation: 0.1377; Line Search: 0.9544
Zero gradient: 7.706714883956692E-25
th(0)=1.7400426065214724E-43;dx=-5.93934543025996E-49
New Minimum: 1.7400426065214724E-43 > 7.943849143368247E-44
WOLF (strong): th(981838.7259259896)=7.943849143368247E-44; dx=4.013045993877918E-49 evalInputDelta=9.456576921846476E-44
New Minimum: 7.943849143368247E-44 > 4.575834595511367E-45
END: th(490919.3629629948)=4.575834595511367E-45; dx=-9.63149718191021E-50 evalInputDelta=1.6942842605663588E-43
Fitness changed from 1.7400426065214724E-43 to 4.575834595511367E-45
Iteration 22 complete. Error: 4.575834595511367E-45 Total: 0.8777; Orientation: 0.0587; Line Search: 0.7567
Zero gradient: 1.2497539258861644E-25
th(0)=4.575834595511367E-45;dx=-1.5618848752678803E-50
New Minimum: 4.575834595511367E-45 > 2.965714379624199E-45
WOLF (strong): th(1057653.7055758296)=2.965714379624199E-45; dx=1.2574146677890431E-50 evalInputDelta=1.610120215887168E-45
New Minimum: 2.965714379624199E-45 > 4.347122651478558E-47
END: th(528826.8527879148)=4.347122651478558E-47; dx=-1.522351037394185E-51 evalInputDelta=4.5323633689965815E-45
Fitness changed from 4.575834595511367E-45 to 4.347122651478558E-47
Iteration 23 complete. Error: 4.347122651478558E-47 Total: 0.8311; Orientation: 0.1328; Line Search: 0.6325
Zero gradient: 1.2181206282786671E-26
th(0)=4.347122651478558E-47;dx=-1.4838178650380146E-52
New Minimum: 4.347122651478558E-47 > 3.877526068757127E-47
WOLF (strong): th(1139322.916666668)=3.877526068757127E-47; dx=1.4013835392025728E-52 evalInputDelta=4.6959658272143086E-48
New Minimum: 3.877526068757127E-47 > 3.354261305152937E-50
END: th(569661.458333334)=3.354261305152937E-50; dx=-4.121716291772096E-54 evalInputDelta=4.343768390173405E-47
Fitness changed from 4.347122651478558E-47 to 3.354261305152937E-50
Iteration 24 complete. Error: 3.354261305152937E-50 Total: 1.1627; Orientation: 0.0574; Line Search: 1.0449
Zero gradient: 3.3836684118850497E-28
th(0)=3.354261305152937E-50;dx=-1.1449211921588694E-55
Armijo: th(1227298.4074074873)=4.0188266615251434E-50; dx=1.2532184656436473E-55 evalInputDelta=-6.645653563722064E-51
New Minimum: 3.354261305152937E-50 > 7.502754886833665E-53
WOLF (strong): th(613649.2037037436)=7.502754886833665E-53; dx=5.414863674238902E-57 evalInputDelta=3.3467585502661035E-50
END: th(204549.73456791454)=1.421110404297079E-50; dx=-7.452312491917832E-56 evalInputDelta=1.933150900855858E-50
Fitness changed from 3.354261305152937E-50 to 7.502754886833665E-53
Iteration 25 complete. Error: 7.502754886833665E-53 Total: 1.1284; Orientation: 0.0714; Line Search: 0.9925
Zero gradient: 1.600293827616007E-29
th(0)=7.502754886833665E-53;dx=-2.5609403347058908E-58
New Minimum: 7.502754886833665E-53 > 4.610427743584899E-54
END: th(440689.0439899291)=4.610427743584899E-54; dx=-6.348332877652392E-59 evalInputDelta=7.041712112475176E-53
Fitness changed from 7.502754886833665E-53 to 4.610427743584899E-54
Iteration 26 complete. Error: 4.610427743584899E-54 Total: 0.5160; Orientation: 0.0554; Line Search: 0.3999
Zero gradient: 3.966979543443995E-30
th(0)=4.610427743584899E-54;dx=-1.5736926698103127E-59
New Minimum: 4.610427743584899E-54 > 1.774366438696224E-54
WOLF (strong): th(949435.76388889)=1.774366438696224E-54; dx=9.762723044193639E-60 evalInputDelta=2.8360613048886756E-54
New Minimum: 1.774366438696224E-54 > 1.661121621434785E-55
END: th(474717.881944445)=1.661121621434785E-55; dx=-2.987101826954744E-60 evalInputDelta=4.4443155814414205E-54
Fitness changed from 4.610427743584899E-54 to 1.661121621434785E-55
Iteration 27 complete. Error: 1.661121621434785E-55 Total: 1.1931; Orientation: 0.0575; Line Search: 1.0685
Final threshold in iteration 27: 1.661121621434785E-55 (> 0.0) after 30.103s (< 30.000s)
Returns
1.661121621434785E-55
First, we use a conjugate gradient descent method, which converges the fastest for purely linear functions.
TrainingTester.java:452 executed in 9.25 seconds (0.051 gc):
IterativeTrainer iterativeTrainer = new IterativeTrainer(trainable.addRef());
try {
iterativeTrainer.setLineSearchFactory(label -> new QuadraticSearch());
iterativeTrainer.setOrientation(new GradientDescent());
iterativeTrainer.setMonitor(TrainingTester.getMonitor(history));
iterativeTrainer.setTimeout(30, TimeUnit.SECONDS);
iterativeTrainer.setMaxIterations(250);
iterativeTrainer.setTerminateThreshold(0);
return iterativeTrainer.run();
} finally {
iterativeTrainer.freeRef();
}
Reset training subject: 10942597852524
Reset training subject: 10942662659650
Constructing line search parameters: GD
F(0.0) = LineSearchPoint{point=PointSample{avg=2.670427437656747}, derivative=-9.115058987201698E-6}
New Minimum: 2.670427437656747 > 2.670427437656746
F(1.0E-10) = LineSearchPoint{point=PointSample{avg=2.670427437656746}, derivative=-9.115058987201696E-6}, evalInputDelta = -8.881784197001252E-16
New Minimum: 2.670427437656746 > 2.6704274376567403
F(7.000000000000001E-10) = LineSearchPoint{point=PointSample{avg=2.6704274376567403}, derivative=-9.115058987201686E-6}, evalInputDelta = -6.661338147750939E-15
New Minimum: 2.6704274376567403 > 2.670427437656702
F(4.900000000000001E-9) = LineSearchPoint{point=PointSample{avg=2.670427437656702}, derivative=-9.11505898720162E-6}, evalInputDelta = -4.4853010194856324E-14
New Minimum: 2.670427437656702 > 2.6704274376564343
F(3.430000000000001E-8) = LineSearchPoint{point=PointSample{avg=2.6704274376564343}, derivative=-9.115058987201162E-6}, evalInputDelta = -3.126388037344441E-13
New Minimum: 2.6704274376564343 > 2.6704274376545585
F(2.4010000000000004E-7) = LineSearchPoint{point=PointSample{avg=2.6704274376545585}, derivative=-9.11505898719796E-6}, evalInputDelta = -2.1884716261411086E-12
New Minimum: 2.6704274376545585 > 2.6704274376414268
F(1.6807000000000003E-6) = LineSearchPoint{point=PointSample{avg=2.6704274376414268}, derivative=-9.115058987175552E-6}, evalInputDelta = -1.532018956140746E-11
New Minimum: 2.6704274376414268 > 2.6704274375495087
F(1.1764900000000001E-5) = LineSearchPoint{point=PointSample{avg=2.6704274375495087}, derivative=-9.115058987018676E-6}, evalInputDelta = -1.0723821830538327E-10
New Minimum: 2.6704274375495087 > 2.6704274369060825
F(8.235430000000001E-5) = LineSearchPoint{point=PointSample{avg=2.6704274369060825}, derivative=-9.115058985920562E-6}, evalInputDelta = -7.506644195132139E-10
New Minimum: 2.6704274369060825 > 2.6704274324020965
F(5.764801000000001E-4) = LineSearchPoint{point=PointSample{avg=2.6704274324020965}, derivative=-9.11505897823376E-6}, evalInputDelta = -5.254650492503288E-9
New Minimum: 2.6704274324020965 > 2.670427400874196
F(0.004035360700000001) = LineSearchPoint{point=PointSample{avg=2.670427400874196}, derivative=-9.115058924426143E-6}, evalInputDelta = -3.6782550782987755E-8
New Minimum: 2.670427400874196 > 2.6704271801788972
F(0.028247524900000005) = LineSearchPoint{point=PointSample{avg=2.6704271801788972}, derivative=-9.115058547772823E-6}, evalInputDelta = -2.5747784970775456E-7
New Minimum: 2.6704271801788972 > 2.670425635312061
F(0.19773267430000002) = LineSearchPoint{point=PointSample{avg=2.670425635312061}, derivative=-9.115055911199581E-6}, evalInputDelta = -1.802344685941648E-6
New Minimum: 2.670425635312061 > 2.6704148212567187
F(1.3841287201) = LineSearchPoint{point=PointSample{avg=2.6704148212567187}, derivative=-9.115037455186884E-6}, evalInputDelta = -1.2616400028253594E-5
New Minimum: 2.6704148212567187 > 2.6703391234824148
F(9.688901040700001) = LineSearchPoint{point=PointSample{avg=2.6703391234824148}, derivative=-9.114908263098005E-6}, evalInputDelta = -8.831417433219357E-5
New Minimum: 2.6703391234824148 > 2.6698092691037942
F(67.8223072849) = LineSearchPoint{point=PointSample{avg=2.6698092691037942}, derivative=-9.11400391847585E-6}, evalInputDelta = -6.181685529527137E-4
New Minimum: 2.6698092691037942 > 2.6661017604871824
F(474.7561509943) = LineSearchPoint{point=PointSample{avg=2.6661017604871824}, derivative=-9.107673506120779E-6}, evalInputDelta = -0.004325677169564557
New Minimum: 2.6661017604871824 > 2.6402213298237927
F(3323.2930569601003) = LineSearchPoint{point=PointSample{avg=2.6402213298237927}, derivative=-9.063360619635276E-6}, evalInputDelta = -0.0302061078329543
New Minimum: 2.6402213298237927 > 2.462592668171849
F(23263.0513987207) = LineSearchPoint{point=PointSample{avg=2.462592668171849}, derivative=-8.753170414236758E-6}, evalInputDelta = -0.2078347694848981
New Minimum: 2.462592668171849 > 1.3923753332058182
F(162841.3597910449) = LineSearchPoint{point=PointSample{avg=1.3923753332058182}, derivative=-6.581838976447122E-6}, evalInputDelta = -1.2780521044509288
F(1139889.5185373144) = LineSearchPoint{point=PointSample{avg=2.386835521724616}, derivative=8.61748108808033E-6}, evalInputDelta = -0.28359191593213096
2.386835521724616 <= 2.670427437656747
New Minimum: 1.3923753332058182 > 4.180471369939823E-32
F(585937.5) = LineSearchPoint{point=PointSample{avg=4.180471369939823E-32}, derivative=7.064276352038078E-22}, evalInputDelta = -2.670427437656747
Right bracket at 585937.5
Converged to right
Fitness changed from 2.670427437656747 to 4.180471369939823E-32
Iteration 1 complete. Error: 4.180471369939823E-32 Total: 8.1323; Orientation: 0.0552; Line Search: 7.8911
Zero gradient: 3.7774782959086958E-19
F(0.0) = LineSearchPoint{point=PointSample{avg=4.180471369939823E-32}, derivative=-1.426934227606126E-37}
New Minimum: 4.180471369939823E-32 > 0.0
F(585937.5) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=0.0}, evalInputDelta = -4.180471369939823E-32
0.0 <= 4.180471369939823E-32
Converged to right
Fitness changed from 4.180471369939823E-32 to 0.0
Iteration 2 complete. Error: 0.0 Total: 0.7389; Orientation: 0.1160; Line Search: 0.5548
Zero gradient: 0.0
F(0.0) = LineSearchPoint{point=PointSample{avg=0.0}, derivative=0.0}
Fitness changed from 0.0 to 0.0
Static Iteration Total: 0.3749; Orientation: 0.1958; Line Search: 0.1159
Iteration 3 failed. Error: 0.0
Previous Error: 0.0 -> 0.0
Optimization terminated 3
Final threshold in iteration 3: 0.0 (> 0.0) after 9.247s (< 30.000s)
Returns
0.0
Next, we apply the same optimization using L-BFGS, which is nearly ideal for purely second-order or quadratic functions.
TrainingTester.java:509 executed in 60.87 seconds (0.459 gc):
IterativeTrainer iterativeTrainer = new IterativeTrainer(trainable.addRef());
try {
iterativeTrainer.setLineSearchFactory(label -> new ArmijoWolfeSearch());
iterativeTrainer.setOrientation(new LBFGS());
iterativeTrainer.setMonitor(TrainingTester.getMonitor(history));
iterativeTrainer.setTimeout(30, TimeUnit.SECONDS);
iterativeTrainer.setIterationsPerSample(100);
iterativeTrainer.setMaxIterations(250);
iterativeTrainer.setTerminateThreshold(0);
return iterativeTrainer.run();
} finally {
iterativeTrainer.freeRef();
}
Reset training subject: 10951859163516
Reset training subject: 10951924304758
Adding measurement 63aef238 to history. Total: 0
LBFGS Accumulation History: 1 points
Constructing line search parameters: GD
Non-optimal measurement 2.670427437656747 < 2.670427437656747. Total: 1
th(0)=2.670427437656747;dx=-9.115058987201698E-6
Adding measurement 76a56f30 to history. Total: 1
New Minimum: 2.670427437656747 > 2.670407799893566
WOLFE (weak): th(2.154434690031884)=2.670407799893566; dx=-9.115025472024251E-6 evalInputDelta=1.963776318092414E-5
Adding measurement 4b7da803 to history. Total: 2
New Minimum: 2.670407799893566 > 2.670388162202592
WOLFE (weak): th(4.308869380063768)=2.670388162202592; dx=-9.114991956846809E-6 evalInputDelta=3.92754541551632E-5
Adding measurement 14712d1e to history. Total: 3
New Minimum: 2.670388162202592 > 2.6703096121607572
WOLFE (weak): th(12.926608140191302)=2.6703096121607572; dx=-9.114857896137032E-6 evalInputDelta=1.178254959897096E-4
Adding measurement 6a395f27 to history. Total: 4
New Minimum: 2.6703096121607572 > 2.669956151269341
WOLFE (weak): th(51.70643256076521)=2.669956151269341; dx=-9.114254622943035E-6 evalInputDelta=4.7128638740590034E-4
Adding measurement 380f21d to history. Total: 5
New Minimum: 2.669956151269341 > 2.6680714216277797
WOLFE (weak): th(258.53216280382605)=2.6680714216277797; dx=-9.111037165908392E-6 evalInputDelta=0.0023560160289672893
Adding measurement 510060b0 to history. Total: 6
New Minimum: 2.6680714216277797 > 2.6563069380353053
WOLFE (weak): th(1551.1929768229563)=2.6563069380353053; dx=-9.090928059441868E-6 evalInputDelta=0.014120499621441684
Adding measurement 4133478c to history. Total: 7
New Minimum: 2.6563069380353053 > 2.5723700065456265
WOLFE (weak): th(10858.350837760694)=2.5723700065456265; dx=-8.9461424928829E-6 evalInputDelta=0.09805743111112042
Adding measurement 2d519d0a to history. Total: 8
New Minimum: 2.5723700065456265 > 1.9373243163805285
END: th(86866.80670208555)=1.9373243163805285; dx=-7.763727032651327E-6 evalInputDelta=0.7331031212762185
Fitness changed from 2.670427437656747 to 1.9373243163805285
Iteration 1 complete. Error: 1.9373243163805285 Total: 2.8099; Orientation: 0.3572; Line Search: 2.2697
Non-optimal measurement 1.9373243163805285 < 1.9373243163805285. Total: 9
Rejected: LBFGS Orientation magnitude: 1.507e+03, gradient 2.572e-03, dot -1.000; [19ee58d1-2353-47a6-8503-3d085e81361c = 1.000/1.000e+00, c2aeda4f-02ab-470e-9a6b-95d981750525 = 1.000/1.000e+00, 63ae4c8b-9f5a-484f-a8ea-aa5034d878e1 = 1.000/1.000e+00, 5dd5b10a-a04a-48a4-b01c-4072c31aa5be = 1.000/1.000e+00, 4c86ccd9-1e1e-4810-95c7-cf21deb045cb = 1.000/1.000e+00, 7758dc9a-4ca9-41b9-8a30-78ac37c50654 = 1.000/1.000e+00, 251f9a4a-d720-420d-b10d-20ec575d607e = 1.000/1.000e+00, e2f87d99-52b8-4e59-b97e-b4a3a928207a = 1.000/1.000e+00, ccb12977-0dae-431f-80ea-a08b9e79a88c = 1.000/1.000e+00, 31c96004-928a-49af-9e2d-59350efa9ce7 = 1.000/1.000e+00, 10e7279c-8efe-4a59-8139-ddbc9b493f06 = 1.000/1.000e+00, 038776c2-8d73-4877-bc0a-2ca105440e19 = 1.000/1.000e+00, a8b1acb8-48e0-4e5a-957b-f293eea5a489 = 1.000/1.000e+00, 9b45371a-3c72-4cff-8697-d3a1011de0e4 = 1.000/1.000e+00, c34f1f3d-00d5-433b-a1db-89cdb06b9550 = 1.000/1.000e+00, 4d5ac43f-5ee6-47d0-ae63-0fd702fd3667 = 1.000/1.000e+00, 068471ef-a7ec-478b-a336-a1f116a9a007 = 1.000/1.000e+00, 7351b4f6-6258-4d71-a379-691c417b5740 = 1.000/1.000e+00, 1cda8404-3329-4aac-9b7d-58410f3e1d3e = 1.000/1.000e+00, 1f3a1e67-a88d-4202-8f42-5f1826ff74a5 = 1.000/1.000e+00, 5a837891-5517-42b9-a5f8-53a17729677d = 1.000/1.000e+00, 639871d4-7019-4136-a5eb-3ed9aaef5f07 = 1.000/1.000e+00, a4bde5d0-6aed-4626-b0cb-0074a8699fb1 = 1.000/1.000e+00, fa5c9a9d-5e41-4ba0-ad9c-32be3f6b8718 = 1.000/1.000e+00, 493c1fb5-a866-4f54-b88a-b15d43313e31 = 1.000/1.000e+00, c119915c-fe7b-4ddc-b70a-168c6fb30151 = 1.000/1.000e+00, 6aab1a37-40ad-448f-9f61-4911131cdd97 = 1.000/1.000e+00, 17d0a4b5-3a97-4336-b73e-a04e34cf9a2f = 1.000/1.000e+00, c493b133-1396-4a6b-ade3-b569d708c984 = 1.000/1.000e+00, bfe0b9be-2b64-4f57-808f-71ed698ed796 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.9373243163805285, 2.5723700065456265, 2.6563069380353053, 2.6680714216277797, 2.669956151269341, 2.6703096121607572, 2.670388162202592, 2.670407799893566, 2.670427437656747
Rejected: LBFGS Orientation magnitude: 1.507e+03, gradient 2.572e-03, dot -1.000; [1cda8404-3329-4aac-9b7d-58410f3e1d3e = 1.000/1.000e+00, 251f9a4a-d720-420d-b10d-20ec575d607e = 1.000/1.000e+00, 10e7279c-8efe-4a59-8139-ddbc9b493f06 = 1.000/1.000e+00, 19ee58d1-2353-47a6-8503-3d085e81361c = 1.000/1.000e+00, 63ae4c8b-9f5a-484f-a8ea-aa5034d878e1 = 1.000/1.000e+00, 038776c2-8d73-4877-bc0a-2ca105440e19 = 1.000/1.000e+00, 639871d4-7019-4136-a5eb-3ed9aaef5f07 = 1.000/1.000e+00, a4bde5d0-6aed-4626-b0cb-0074a8699fb1 = 1.000/1.000e+00, 068471ef-a7ec-478b-a336-a1f116a9a007 = 1.000/1.000e+00, 6aab1a37-40ad-448f-9f61-4911131cdd97 = 1.000/1.000e+00, 7351b4f6-6258-4d71-a379-691c417b5740 = 1.000/1.000e+00, e2f87d99-52b8-4e59-b97e-b4a3a928207a = 1.000/1.000e+00, c493b133-1396-4a6b-ade3-b569d708c984 = 1.000/1.000e+00, bfe0b9be-2b64-4f57-808f-71ed698ed796 = 1.000/1.000e+00, ccb12977-0dae-431f-80ea-a08b9e79a88c = 1.000/1.000e+00, c34f1f3d-00d5-433b-a1db-89cdb06b9550 = 1.000/1.000e+00, 1f3a1e67-a88d-4202-8f42-5f1826ff74a5 = 1.000/1.000e+00, fa5c9a9d-5e41-4ba0-ad9c-32be3f6b8718 = 1.000/1.000e+00, c2aeda4f-02ab-470e-9a6b-95d981750525 = 1.000/1.000e+00, 5a837891-5517-42b9-a5f8-53a17729677d = 1.000/1.000e+00, c119915c-fe7b-4ddc-b70a-168c6fb30151 = 1.000/1.000e+00, 493c1fb5-a866-4f54-b88a-b15d43313e31 = 1.000/1.000e+00, 4d5ac43f-5ee6-47d0-ae63-0fd702fd3667 = 1.000/1.000e+00, 31c96004-928a-49af-9e2d-59350efa9ce7 = 1.000/1.000e+00, 5dd5b10a-a04a-48a4-b01c-4072c31aa5be = 1.000/1.000e+00, 4c86ccd9-1e1e-4810-95c7-cf21deb045cb = 1.000/1.000e+00, 9b45371a-3c72-4cff-8697-d3a1011de0e4 = 1.000/1.000e+00, 17d0a4b5-3a97-4336-b73e-a04e34cf9a2f = 1.000/1.000e+00, a8b1acb8-48e0-4e5a-957b-f293eea5a489 = 1.000/1.000e+00, 7758dc9a-4ca9-41b9-8a30-78ac37c50654 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.9373243163805285, 2.5723700065456265, 2.6563069380353053, 2.6680714216277797, 2.669956151269341, 2.6703096121607572, 2.670388162202592, 2.670407799893566
Rejected: LBFGS Orientation magnitude: 1.507e+03, gradient 2.572e-03, dot -1.000; [5dd5b10a-a04a-48a4-b01c-4072c31aa5be = 1.000/1.000e+00, 4d5ac43f-5ee6-47d0-ae63-0fd702fd3667 = 1.000/1.000e+00, 6aab1a37-40ad-448f-9f61-4911131cdd97 = 1.000/1.000e+00, c493b133-1396-4a6b-ade3-b569d708c984 = 1.000/1.000e+00, 9b45371a-3c72-4cff-8697-d3a1011de0e4 = 1.000/1.000e+00, e2f87d99-52b8-4e59-b97e-b4a3a928207a = 1.000/1.000e+00, 5a837891-5517-42b9-a5f8-53a17729677d = 1.000/1.000e+00, 10e7279c-8efe-4a59-8139-ddbc9b493f06 = 1.000/1.000e+00, a8b1acb8-48e0-4e5a-957b-f293eea5a489 = 1.000/1.000e+00, 639871d4-7019-4136-a5eb-3ed9aaef5f07 = 1.000/1.000e+00, c2aeda4f-02ab-470e-9a6b-95d981750525 = 1.000/1.000e+00, fa5c9a9d-5e41-4ba0-ad9c-32be3f6b8718 = 1.000/1.000e+00, 068471ef-a7ec-478b-a336-a1f116a9a007 = 1.000/1.000e+00, a4bde5d0-6aed-4626-b0cb-0074a8699fb1 = 1.000/1.000e+00, 19ee58d1-2353-47a6-8503-3d085e81361c = 1.000/1.000e+00, 17d0a4b5-3a97-4336-b73e-a04e34cf9a2f = 1.000/1.000e+00, c34f1f3d-00d5-433b-a1db-89cdb06b9550 = 1.000/1.000e+00, 038776c2-8d73-4877-bc0a-2ca105440e19 = 1.000/1.000e+00, 7351b4f6-6258-4d71-a379-691c417b5740 = 1.000/1.000e+00, c119915c-fe7b-4ddc-b70a-168c6fb30151 = 1.000/1.000e+00, bfe0b9be-2b64-4f57-808f-71ed698ed796 = 1.000/1.000e+00, 1cda8404-3329-4aac-9b7d-58410f3e1d3e = 1.000/1.000e+00, 1f3a1e67-a88d-4202-8f42-5f1826ff74a5 = 1.000/1.000e+00, ccb12977-0dae-431f-80ea-a08b9e79a88c = 1.000/1.000e+00, 31c96004-928a-49af-9e2d-59350efa9ce7 = 1.000/1.000e+00, 63ae4c8b-9f5a-484f-a8ea-aa5034d878e1 = 1.000/1.000e+00, 4c86ccd9-1e1e-4810-95c7-cf21deb045cb = 1.000/1.000e+00, 251f9a4a-d720-420d-b10d-20ec575d607e = 1.000/1.000e+00, 493c1fb5-a866-4f54-b88a-b15d43313e31 = 1.000/1.000e+00, 7758dc9a-4ca9-41b9-8a30-78ac37c50654 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.9373243163805285, 2.5723700065456265, 2.6563069380353053, 2.6680714216277797, 2.669956151269341, 2.6703096121607572, 2.670388162202592
Rejected: LBFGS Orientation magnitude: 1.507e+03, gradient 2.572e-03, dot -1.000; [251f9a4a-d720-420d-b10d-20ec575d607e = 1.000/1.000e+00, 5dd5b10a-a04a-48a4-b01c-4072c31aa5be = 1.000/1.000e+00, 068471ef-a7ec-478b-a336-a1f116a9a007 = 1.000/1.000e+00, 6aab1a37-40ad-448f-9f61-4911131cdd97 = 1.000/1.000e+00, 31c96004-928a-49af-9e2d-59350efa9ce7 = 1.000/1.000e+00, bfe0b9be-2b64-4f57-808f-71ed698ed796 = 1.000/1.000e+00, c34f1f3d-00d5-433b-a1db-89cdb06b9550 = 1.000/1.000e+00, 5a837891-5517-42b9-a5f8-53a17729677d = 1.000/1.000e+00, 493c1fb5-a866-4f54-b88a-b15d43313e31 = 1.000/1.000e+00, 1f3a1e67-a88d-4202-8f42-5f1826ff74a5 = 1.000/1.000e+00, 639871d4-7019-4136-a5eb-3ed9aaef5f07 = 1.000/1.000e+00, c2aeda4f-02ab-470e-9a6b-95d981750525 = 1.000/1.000e+00, 19ee58d1-2353-47a6-8503-3d085e81361c = 1.000/1.000e+00, a4bde5d0-6aed-4626-b0cb-0074a8699fb1 = 1.000/1.000e+00, 7351b4f6-6258-4d71-a379-691c417b5740 = 1.000/1.000e+00, 038776c2-8d73-4877-bc0a-2ca105440e19 = 1.000/1.000e+00, a8b1acb8-48e0-4e5a-957b-f293eea5a489 = 1.000/1.000e+00, 63ae4c8b-9f5a-484f-a8ea-aa5034d878e1 = 1.000/1.000e+00, 4c86ccd9-1e1e-4810-95c7-cf21deb045cb = 1.000/1.000e+00, e2f87d99-52b8-4e59-b97e-b4a3a928207a = 1.000/1.000e+00, 10e7279c-8efe-4a59-8139-ddbc9b493f06 = 1.000/1.000e+00, c119915c-fe7b-4ddc-b70a-168c6fb30151 = 1.000/1.000e+00, 9b45371a-3c72-4cff-8697-d3a1011de0e4 = 1.000/1.000e+00, 17d0a4b5-3a97-4336-b73e-a04e34cf9a2f = 1.000/1.000e+00, ccb12977-0dae-431f-80ea-a08b9e79a88c = 1.000/1.000e+00, 4d5ac43f-5ee6-47d0-ae63-0fd702fd3667 = 1.000/1.000e+00, fa5c9a9d-5e41-4ba0-ad9c-32be3f6b8718 = 1.000/1.000e+00, c493b133-1396-4a6b-ade3-b569d708c984 = 1.000/1.000e+00, 1cda8404-3329-4aac-9b7d-58410f3e1d3e = 1.000/1.000e+00, 7758dc9a-4ca9-41b9-8a30-78ac37c50654 = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.9373243163805285, 2.5723700065456265, 2.6563069380353053, 2.6680714216277797, 2.669956151269341, 2.6703096121607572
Rejected: LBFGS Orientation magnitude: 1.507e+03, gradient 2.572e-03, dot -1.000; [7351b4f6-6258-4d71-a379-691c417b5740 = 1.000/1.000e+00, c2aeda4f-02ab-470e-9a6b-95d981750525 = 1.000/1.000e+00, 5a837891-5517-42b9-a5f8-53a17729677d = 1.000/1.000e+00, 251f9a4a-d720-420d-b10d-20ec575d607e = 1.000/1.000e+00, 4d5ac43f-5ee6-47d0-ae63-0fd702fd3667 = 1.000/1.000e+00, e2f87d99-52b8-4e59-b97e-b4a3a928207a = 1.000/1.000e+00, 639871d4-7019-4136-a5eb-3ed9aaef5f07 = 1.000/1.000e+00, 5dd5b10a-a04a-48a4-b01c-4072c31aa5be = 1.000/1.000e+00, 17d0a4b5-3a97-4336-b73e-a04e34cf9a2f = 1.000/1.000e+00, 10e7279c-8efe-4a59-8139-ddbc9b493f06 = 1.000/1.000e+00, a8b1acb8-48e0-4e5a-957b-f293eea5a489 = 1.000/1.000e+00, 6aab1a37-40ad-448f-9f61-4911131cdd97 = 1.000/1.000e+00, c493b133-1396-4a6b-ade3-b569d708c984 = 1.000/1.000e+00, 19ee58d1-2353-47a6-8503-3d085e81361c = 1.000/1.000e+00, fa5c9a9d-5e41-4ba0-ad9c-32be3f6b8718 = 1.000/1.000e+00, ccb12977-0dae-431f-80ea-a08b9e79a88c = 1.000/1.000e+00, 4c86ccd9-1e1e-4810-95c7-cf21deb045cb = 1.000/1.000e+00, 038776c2-8d73-4877-bc0a-2ca105440e19 = 1.000/1.000e+00, 7758dc9a-4ca9-41b9-8a30-78ac37c50654 = 1.000/1.000e+00, c119915c-fe7b-4ddc-b70a-168c6fb30151 = 1.000/1.000e+00, 1f3a1e67-a88d-4202-8f42-5f1826ff74a5 = 1.000/1.000e+00, 068471ef-a7ec-478b-a336-a1f116a9a007 = 1.000/1.000e+00, 31c96004-928a-49af-9e2d-59350efa9ce7 = 1.000/1.000e+00, a4bde5d0-6aed-4626-b0cb-0074a8699fb1 = 1.000/1.000e+00, bfe0b9be-2b64-4f57-808f-71ed698ed796 = 1.000/1.000e+00, 9b45371a-3c72-4cff-8697-d3a1011de0e4 = 1.000/1.000e+00, c34f1f3d-00d5-433b-a1db-89cdb06b9550 = 1.000/1.000e+00, 493c1fb5-a866-4f54-b88a-b15d43313e31 = 1.000/1.000e+00, 63ae4c8b-9f5a-484f-a8ea-aa5034d878e1 = 1.000/1.000e+00, 1cda8404-3329-4aac-9b7d-58410f3e1d3e = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.9373243163805285, 2.5723700065456265, 2.6563069380353053, 2.6680714216277797, 2.669956151269341
Rejected: LBFGS Orientation magnitude: 1.507e+03, gradient 2.572e-03, dot -1.000; [c493b133-1396-4a6b-ade3-b569d708c984 = 1.000/1.000e+00, 5dd5b10a-a04a-48a4-b01c-4072c31aa5be = 1.000/1.000e+00, a4bde5d0-6aed-4626-b0cb-0074a8699fb1 = 1.000/1.000e+00, 7758dc9a-4ca9-41b9-8a30-78ac37c50654 = 1.000/1.000e+00, 1f3a1e67-a88d-4202-8f42-5f1826ff74a5 = 1.000/1.000e+00, 4d5ac43f-5ee6-47d0-ae63-0fd702fd3667 = 1.000/1.000e+00, 4c86ccd9-1e1e-4810-95c7-cf21deb045cb = 1.000/1.000e+00, 7351b4f6-6258-4d71-a379-691c417b5740 = 1.000/1.000e+00, 63ae4c8b-9f5a-484f-a8ea-aa5034d878e1 = 1.000/1.000e+00, 19ee58d1-2353-47a6-8503-3d085e81361c = 1.000/1.000e+00, 9b45371a-3c72-4cff-8697-d3a1011de0e4 = 1.000/1.000e+00, c119915c-fe7b-4ddc-b70a-168c6fb30151 = 1.000/1.000e+00, 5a837891-5517-42b9-a5f8-53a17729677d = 1.000/1.000e+00, 038776c2-8d73-4877-bc0a-2ca105440e19 = 1.000/1.000e+00, 251f9a4a-d720-420d-b10d-20ec575d607e = 1.000/1.000e+00, a8b1acb8-48e0-4e5a-957b-f293eea5a489 = 1.000/1.000e+00, 17d0a4b5-3a97-4336-b73e-a04e34cf9a2f = 1.000/1.000e+00, 493c1fb5-a866-4f54-b88a-b15d43313e31 = 1.000/1.000e+00, e2f87d99-52b8-4e59-b97e-b4a3a928207a = 1.000/1.000e+00, ccb12977-0dae-431f-80ea-a08b9e79a88c = 1.000/1.000e+00, fa5c9a9d-5e41-4ba0-ad9c-32be3f6b8718 = 1.000/1.000e+00, 639871d4-7019-4136-a5eb-3ed9aaef5f07 = 1.000/1.000e+00, 6aab1a37-40ad-448f-9f61-4911131cdd97 = 1.000/1.000e+00, bfe0b9be-2b64-4f57-808f-71ed698ed796 = 1.000/1.000e+00, c34f1f3d-00d5-433b-a1db-89cdb06b9550 = 1.000/1.000e+00, 10e7279c-8efe-4a59-8139-ddbc9b493f06 = 1.000/1.000e+00, 068471ef-a7ec-478b-a336-a1f116a9a007 = 1.000/1.000e+00, c2aeda4f-02ab-470e-9a6b-95d981750525 = 1.000/1.000e+00, 31c96004-928a-49af-9e2d-59350efa9ce7 = 1.000/1.000e+00, 1cda8404-3329-4aac-9b7d-58410f3e1d3e = 1.000/1.000e+00]
Orientation rejected. Popping history element from 1.9373243163805285, 2.5723700065456265, 2.6563069380353053, 2.6680714216277797
LBFGS Accumulation History: 3 points
Removed measurement 2d519d0a to history. Total: 8
Removed measurement 4133478c to history. Total: 7
Removed measurement 510060b0 to history. Total: 6
Removed measurement 380f21d to history. Total: 5
Removed measurement 6a395f27 to history. Total: 4
Removed measurement 14712d1e to history. Total: 3
Adding measurement 3a557810 to history. Total: 3
th(0)=1.9373243163805285;dx=-6.612733666578871E-6
Adding measurement 41b23d2e to history. Total: 4
New Minimum: 1.9373243163805285 > 0.8973984084877697
END: th(187148.86177126726)=0.8973984084877697; dx=-4.50062174526171E-6 evalInputDelta=1.0399259078927587
Fitness changed from 1.9373243163805285 to 0.8973984084877697
Iteration 2 complete. Error: 0.8973984084877697 Total: 58.0589; Orientation: 57.5072; Line Search: 0.4894
Final threshold in iteration 2: 0.8973984084877697 (> 0.0) after 60.869s (< 30.000s)
Returns
0.8973984084877697
This training apply resulted in the following configuration:
TrainingTester.java:610 executed in 0.00 seconds (0.000 gc):
RefList<double[]> state = network.state();
assert state != null;
String description = state.stream().map(RefArrays::toString).reduce((a, b) -> a + "\n" + b)
.orElse("");
state.freeRef();
return description;
Returns
TrainingTester.java:622 executed in 0.01 seconds (0.000 gc):
return RefArrays.stream(RefUtil.addRef(data)).flatMap(x -> {
return RefArrays.stream(x);
}).limit(1).map(x -> {
String temp_18_0015 = x.prettyPrint();
x.freeRef();
return temp_18_0015;
}).reduce((a, b) -> a + "\n" + b).orElse("");
Returns
[
[ [ -0.802543986435758, 0.6093543365413845, 1.6471308501512203, -0.20714040362928948, 0.45017298799355565, 1.1624393516634244, -1.19372874621949, -1.4583854420309126, ... ], [ -0.3897574066536975, 0.281985783154503, 1.589617742461777, 1.354369519567464, 1.410210975162619, -0.23654546531049603, -1.3260174013381867, -1.0433464886812671, ... ], [ -0.2997431898082005, -1.0279206144259707, -0.015914472183804707, -0.9490436167170735, -0.18952561891698871, -0.49888655118696634, 0.3413354563284598, -1.2394354277333655, ... ], [ -1.2060713109706984, -0.42090076803246335, -0.5874290587479862, 0.048640773347974064, -0.7434511234536005, -1.2204155813398583, 0.29658049473008297, 0.18830753533162148, ... ], [ 1.5314765993951185, -0.3874797838878081, -0.1925613877739446, 1.1902521468243716, -1.3342046061772392, -1.3439460903674882, -0.7539539382276057, 1.3496875745577372, ... ], [ -0.2887422236276179, -1.1222585158097511, 0.23585419356591364, 0.12425044120642015, 1.7917684390065047, 1.860171282375604, 0.6042251920081159, -1.088358487214657, ... ], [ -0.35435063935453937, -0.9351021897170131, 0.292529769590261, 1.0303790730455333, 1.2782077906699292, -0.816764515076446, -0.3520982657869542, 0.9486723915316578, ... ], [ 0.9930563546878323, 0.2660556152504634, 0.8197620700211253, -0.26949081624061544, 0.9488532273853312, -0.7871149276877719, 1.3528088712308883, -0.3743187944276422, ... ], ... ],
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[ [ -0.7568627808634002, 0.1906868351203681, 0.3040188802129249, 0.31508012501139837, -0.5116542507561019, 1.047295763541445, -0.34472630116416925, -0.8143854420309127, ... ], [ -0.040929428466670706, 0.4358127951609473, 0.12554154138043738, 0.3201014502796439, -0.07758271304219022, 0.08583360773503712, 0.7831531416000483, 0.12782234863901643, ... ], [ 0.10588410613164545, -0.016415581339858237, 1.0112227469527952, 1.3727581460910665, 1.013456013564242, -0.09824259334630253, 0.7872418539089334, 0.48298800146661036, ... ], [ -0.08362581706510785, -0.2312925790487554, 0.9456241114471564, 0.15272652790738273, 0.15449007680324633, 0.6848850723122282, -0.035552800476458035, 1.489341825313839, ... ], [ 0.21174614755767673, -0.36779391494802255, -0.35438862652360237, 0.5053195338650112, -0.7085138471268124, 0.6625153260015506, 1.0920063689853794, -0.24734011969588776, ... ], [ 0.6006723915316577, 0.7464425361561141, 1.7303124254422628, 0.015616263587038826, -0.14646164311225246, -1.3731514359820967, -0.5480982657869542, -0.08762263257241831, ... ], [ 0.3644885979285083, 1.6095606483365756, -0.5125390963251168, 1.0803996588764098, 0.7603045775821452, -1.4306645436715402, 0.49839818000167163, -0.8021538810374175, ... ], [ -1.038658174686161, 1.0038953652276663, 0.6277368208228211, -1.0098081317935195, 0.4855479103658169, 0.5397017914032342, 0.2805552455317788, -0.744948535422809, ... ], ... ],
[ [ 0.15822348639016415, -0.4822300821187571, 1.4776843900650476, -0.4378365654845135, 0.6000191069561382, -0.3836922379251651, 0.14181131628620916, 0.2685265850975713, ... ], [ -0.17218402034636304, 0.3041078192650233, -0.724139437448707, -0.42123082155612623, 1.505900028595094, 0.939235484923554, -1.019286210063376, 0.18065988030411234, ... ], [ -0.8673148704975834, 0.411911287691115, 0.8386804661349887, -0.21289121455439408, 1.7140333238016354, -0.7520602786178912, -0.8117907304553327, -0.5135829397854037, ... ], [ -0.8344106912292168, 0.2445488765463994, -0.5449928915772515, 0.24502450976093526, 0.874258515809751, -0.6238571513153899, 0.3060460617723941, -1.1016939435431168, ... ], [ -0.2900110323528073, 0.24860892842107696, 1.0043805519202715, -0.8354226897626067, -0.311793688204809, -0.9517588855284356, -1.3878858117495974, 1.006185499221101, ... ], [ -1.936, 0.5248152402162679, 0.31239328989103016, -0.9803393802585185, 0.9967422236276179, 0.6322315609934952, 1.74133227183577, -1.1506106340390285, ... ], [ 0.5491195910551998, -1.4594861528731875, -1.1880063689853795, 0.7397525165430561, 0.8289135060032222, 0.389890475117025, 0.44415217541946583, 0.9612624397398098, ... ], [ 0.14390787645521175, 1.1468833666942766, 0.3013259028503906, 1.131225931445485, -0.9578620414260312, -1.4672323004308643, -0.6367167476861003, -0.18999510988935864, ... ], ... ],
[ [ -1.051654250756102, 1.3223758885528436, 0.5047581460910666, -0.4567074209512446, -0.5798603358080794, -1.0550705715333293, -1.246137958573969, -0.05080102337077097, ... ], [ -1.3210436167170736, 1.4713021325268245, 0.2199713395657925, 0.2577637756390769, 0.7060205858308763, -0.5670039239300587, -0.24476747282592215, 0.40714358812197926, ... ], [ -0.43236485620003634, 0.015505259829325935, -1.366207790669929, 0.02589388635292822, -1.0154734149024287, 0.7209135060032221, 0.9122886551186966, 1.3474258742552963, ... ], [ -0.8358000571901884, 0.44043150380330676, -0.27176525451381506, 0.672732670149549, 0.802185499221101, -0.8336557296308401, -0.44775570103574586, 0.16890713701784266, ... ], [ -0.8572497017690511, -0.5819762296764339, -1.1194258742552965, -1.5034258742552964, -0.27106420254794983, -1.1550833095040882, -0.7799237989186603, 0.2592673298504511, ... ], [ -1.0715496159837683, 0.23069320410574767, -0.8399713395657924, 0.5559620128309369, 0.1461095248829749, -0.7685011091560535, 1.0180808644487676, -0.42829502410407605, ... ], [ 0.4358032416828782, 1.2078858117495972, 0.4618652259187208, 0.49834108587647014, -1.0807106054439344, -0.9974432755934831, -0.14943053762272424, -0.9287645150764461, ... ], [ -0.6594861528731876, 0.5981063403902852, 0.22595712272029567, -1.0341791302357217, -0.5870769405187087, 0.00388899624228696, 0.3469848169739205, -1.402207790669929, ... ], ... ],
[ [ 1.2619603072129852, -0.2793783336081642, -0.498112482632451, -0.5973322718357701, 0.5367772530472048, -0.6527231166714798, -0.10571623499194482, 1.8314163207772274, ... ], [ 0.9796351437999639, 0.43674222362761794, -0.5165486498031857, 0.14623645110413652, 0.5454084729171098, -0.08047267546505965, -1.2281046347723337, 0.837138698011338, ... ], [ -0.8520539096325117, -1.4404663064796803, 0.7035020753366361, 0.26129110017401735, 0.24773045183744158, 0.20065351131873285, -0.7586518057007814, -0.06273756026019014, ... ], [ 0.9862933184861244, -0.6353655956374054, 0.10626170030244078, -1.2627819164146328, -0.9739571227202957, -0.9016939435431168, -0.5586740971496094, 0.4931450669967174, ... ], [ 1.5727581460910665, 0.2993878870862335, 1.467482968380498, -0.9060967869122161, 1.2778049473008297, 0.8801014502796438, -1.1711435881219794, 0.8146454367154019, ... ], [ -0.40182701200644433, -0.9103124254422628, -0.15806516872853238, 0.9853450098065291, -0.6702839917512688, -0.7683869209056507, 0.291971566309006, -0.15844890514149348, ... ], [ 0.2108262725690751, -1.5797177138666827, 0.3859255045366119, 0.6730276942536249, 1.287765254513815, -0.4318446400878444, -1.4621347740812793, 0.1612940579234936, ... ], [ 0.4007265279073829, -1.2717716234991945, 0.42866624928949176, -1.2348421950325237, -0.20153198790236804, -0.35358908202756956, 1.1971450669967174, -0.3672131934747259, ... ], ... ],
[ [ -0.6385819736048212, -1.4024868923105567, -1.289110264320344, 1.169148251489407, -0.05558123416745209, -0.5878223486390164, -0.08982723874965792, -0.5015765708000242, ... ], [ 0.3070357688569558, -0.6032989480341349, 0.5898081317935194, 0.4086598803041123, 0.029988740903979294, -0.03785567244065169, -0.12582382751375443, 1.4077525165430562, ... ], [ -1.1200666476032706, 0.6315241400422507, -0.41021097516261884, -0.3454591980569317, 0.09185715131538985, -1.4639524593528679, -0.25235211822927744, 0.511257776372382, ... ], [ -0.8166723915316577, -0.6220967869122161, -0.3798350866097753, -0.319051464577191, 0.7517525165430561, -0.08362263257241831, -0.3681364796992307, -0.1666010805609594, ... ], [ 1.2139571227202957, -0.9780681264780087, 0.6673212394829628, -0.5959174299332809, 1.5297922093300709, -1.1136971280358063, 0.20903724773169405, 0.7758826272569075, ... ], [ 0.5832100089820362, 0.708631219869905, -0.7767676995691357, -1.709912766565853, -0.3500553885072497, 0.030426613692665555, 0.5488343471724062, -0.3848120557235782, ... ], [ -0.6368152402162679, 0.12731168600489365, 1.1158414555951546, 0.01174000531551056, 0.5439651973236267, 0.28039647438371995, 0.8062680692878201, -1.3556003411235902, ... ], [ 0.8519936310146207, -1.0643932898910302, 0.5462362243609231, 0.1195368780130095, 1.3663060564568832, -0.39945430794629044, 0.39272948565685917, -0.05432516341302164, ... ], ... ],
[ [ -0.4778713681608866, -1.232358487214657, 0.09636781394951255, -1.1637811769772637, -1.161985783154503, -0.5886566958114227, -1.400123741728472, -0.2946454367154019, ... ], [ 0.055057833562570435, -0.012932612959360407, -0.994543986435758, 0.4039206144259706, -1.1388199035836957, 1.7851038953349645, 1.0521776513609837, -0.6522982085967658, ... ], [ -0.013877963889479705, 1.5582553313170613, 1.7521205572357823, 0.1639460903674883, -0.3472163779674158, 0.8437493320503665, -0.8752291159381745, 0.3983283479057113, ... ], [ 1.3516986069105446, 0.2896081889837078, -1.0305915270828903, 0.022226897626067443, 1.295593972138211, 0.6024457206488038, 0.18285493300328262, 0.16925925524712004, ... ], [ -0.08736241114471564, -0.8459063975804737, 0.773700312528496, 0.1407358546422386, -0.08714040362928954, 1.103470230409739, 0.6955082175788021, -0.2634954796080432, ... ], [ -0.4750323576210528, -0.10141802639517883, -0.19086744423082802, -0.027032357621052827, -0.4556638042341712, 0.0743886265236024, 0.8759017342130457, 0.6785630933918962, ... ], [ 0.7094940007333052, -1.495479783887808, 1.2052244525707467, 0.4494148419024892, 0.9049007680324633, 0.043613079094349194, 1.0605106626341227, 0.41903872660643193, ... ], [ -0.5246628380535885, 0.7968723343414693, 0.6225153260015506, 0.11991742993328092, 0.02959886224885222, -0.8714543079462904, -1.2243330112731392, 0.01886744423082798, ... ], ... ],
...
]
To produce the following output:
TrainingTester.java:633 executed in 0.02 seconds (0.000 gc):
Result[] array = ConstantResult.batchResultArray(pop(RefUtil.addRef(data)));
@Nullable
Result eval = layer.eval(array);
assert eval != null;
TensorList tensorList = Result.getData(eval);
String temp_18_0016 = tensorList.stream().limit(1).map(x -> {
String temp_18_0017 = x.prettyPrint();
x.freeRef();
return temp_18_0017;
}).reduce((a, b) -> a + "\n" + b).orElse("");
tensorList.freeRef();
return temp_18_0016;
Returns
[
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...
]
TrainingTester.java:432 executed in 0.16 seconds (0.000 gc):
return TestUtil.compare(title + " vs Iteration", runs);
Plotting range=[1.0, -54.77959856888254], [27.0, 0.2872023295598177]; valueStats=DoubleSummaryStatistics{count=30, sum=5.762815, min=0.000000, average=0.192094, max=1.937324}
Plotting 27 points for GD
Plotting 2 points for CjGD
Plotting 2 points for LBFGS
Returns
TrainingTester.java:435 executed in 0.01 seconds (0.000 gc):
return TestUtil.compareTime(title + " vs Time", runs);
Plotting range=[0.0, -54.77959856888254], [58.059, 0.2872023295598177]; valueStats=DoubleSummaryStatistics{count=30, sum=5.762815, min=0.000000, average=0.192094, max=1.937324}
Plotting 27 points for GD
Plotting 2 points for CjGD
Plotting 2 points for LBFGS
Returns
TrainingTester.java:255 executed in 0.00 seconds (0.000 gc):
return grid(inputLearning, modelLearning, completeLearning);
Returns
TrainingTester.java:258 executed in 0.00 seconds (0.000 gc):
return new ComponentResult(null == inputLearning ? null : inputLearning.value,
null == modelLearning ? null : modelLearning.value, null == completeLearning ? null : completeLearning.value);
Returns
{"input":{ "LBFGS": { "type": "NonConverged", "value": 0.8973984084877697 }, "CjGD": { "type": "Converged", "value": 0.0 }, "GD": { "type": "Converged", "value": 1.661121621434785E-55 } }, "model":null, "complete":null}
LayerTests.java:425 executed in 0.00 seconds (0.000 gc):
throwException(exceptions.addRef());
| details | result |
|---|---|
| {"input":{ "LBFGS": { "type": "NonConverged", "value": 0.8973984084877697 }, "CjGD": { "type": "Converged", "value": 0.0 }, "GD": { "type": "Converged", "value": 1.661121621434785E-55 } }, "model":null, "complete":null} | OK |
{
"result": "OK",
"performance": {
"execution_time": "101.560",
"gc_time": "1.246"
},
"created_on": 1586745543708,
"file_name": "trainingTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgTileAssemblyLayerTest.Basic",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/ImgTileAssemblyLayerTest.java",
"javaDoc": ""
},
"training_analysis": {
"input": {
"LBFGS": {
"type": "NonConverged",
"value": 0.8973984084877697
},
"CjGD": {
"type": "Converged",
"value": 0.0
},
"GD": {
"type": "Converged",
"value": 1.661121621434785E-55
}
}
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/ImgTileAssemblyLayer/Basic/trainingTest/202004133903",
"id": "1bd09e58-67e7-4475-bfa1-8d4937247ea2",
"report_type": "Components",
"display_name": "Comparative Training",
"target": {
"simpleName": "ImgTileAssemblyLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgTileAssemblyLayer",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/ImgTileAssemblyLayer.java",
"javaDoc": ""
}
}