a9a_1e-03.py

import logistic
model = logistic.LogiBinaryLibSVM('a9a', 0.001)
model.train(3, 0.001, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 1, 4096, 1, 1, 512)
model.train(3, 1, 1, 1, 4096, 1, 1, 512)
model.train(3, 2, 1, 1, 4096, 1, 1, 512)
model.train(3, 5, 1, 1, 4096, 1, 1, 512)
model.train(3, 10, 1, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 1, 4096, 1, 1, 512)
model.train(3, 1, 2, 1, 4096, 1, 1, 512)
model.train(3, 2, 2, 1, 4096, 1, 1, 512)
model.train(3, 5, 2, 1, 4096, 1, 1, 512)
model.train(3, 10, 2, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 1, 4096, 1, 1, 512)
model.train(3, 1, 4, 1, 4096, 1, 1, 512)
model.train(3, 2, 4, 1, 4096, 1, 1, 512)
model.train(3, 5, 4, 1, 4096, 1, 1, 512)
model.train(3, 10, 4, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 1, 4096, 1, 1, 512)
model.train(3, 1, 8, 1, 4096, 1, 1, 512)
model.train(3, 2, 8, 1, 4096, 1, 1, 512)
model.train(3, 5, 8, 1, 4096, 1, 1, 512)
model.train(3, 10, 8, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 1, 4096, 1, 1, 512)
model.train(3, 1, 16, 1, 4096, 1, 1, 512)
model.train(3, 2, 16, 1, 4096, 1, 1, 512)
model.train(3, 5, 16, 1, 4096, 1, 1, 512)
model.train(3, 10, 16, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 1, 4096, 1, 1, 512)
model.train(3, 1, 32, 1, 4096, 1, 1, 512)
model.train(3, 2, 32, 1, 4096, 1, 1, 512)
model.train(3, 5, 32, 1, 4096, 1, 1, 512)
model.train(3, 10, 32, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 1, 4096, 1, 1, 512)
model.train(3, 1, 64, 1, 4096, 1, 1, 512)
model.train(3, 2, 64, 1, 4096, 1, 1, 512)
model.train(3, 5, 64, 1, 4096, 1, 1, 512)
model.train(3, 10, 64, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 1, 4096, 1, 1, 512)
model.train(3, 1, 128, 1, 4096, 1, 1, 512)
model.train(3, 2, 128, 1, 4096, 1, 1, 512)
model.train(3, 5, 128, 1, 4096, 1, 1, 512)
model.train(3, 10, 128, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 1, 4096, 1, 1, 512)
model.train(3, 1, 256, 1, 4096, 1, 1, 512)
model.train(3, 2, 256, 1, 4096, 1, 1, 512)
model.train(3, 5, 256, 1, 4096, 1, 1, 512)
model.train(3, 10, 256, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 1, 4096, 1, 1, 512)
model.train(3, 1, 512, 1, 4096, 1, 1, 512)
model.train(3, 2, 512, 1, 4096, 1, 1, 512)
model.train(3, 5, 512, 1, 4096, 1, 1, 512)
model.train(3, 10, 512, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 1, 4096, 1, 1, 512)
model.train(3, 1, 1024, 1, 4096, 1, 1, 512)
model.train(3, 2, 1024, 1, 4096, 1, 1, 512)
model.train(3, 5, 1024, 1, 4096, 1, 1, 512)
model.train(3, 10, 1024, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 1, 4096, 1, 1, 512)
model.train(3, 1, 2048, 1, 4096, 1, 1, 512)
model.train(3, 2, 2048, 1, 4096, 1, 1, 512)
model.train(3, 5, 2048, 1, 4096, 1, 1, 512)
model.train(3, 10, 2048, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 1, 4096, 1, 1, 512)
model.train(3, 1, 4096, 1, 4096, 1, 1, 512)
model.train(3, 2, 4096, 1, 4096, 1, 1, 512)
model.train(3, 5, 4096, 1, 4096, 1, 1, 512)
model.train(3, 10, 4096, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 1, 4096, 1, 1, 512)
model.train(3, 1, 8192, 1, 4096, 1, 1, 512)
model.train(3, 2, 8192, 1, 4096, 1, 1, 512)
model.train(3, 5, 8192, 1, 4096, 1, 1, 512)
model.train(3, 10, 8192, 1, 4096, 1, 1, 512)
model.train(3, 0.001, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 2, 4096, 1, 1, 512)
model.train(3, 1, 1, 2, 4096, 1, 1, 512)
model.train(3, 2, 1, 2, 4096, 1, 1, 512)
model.train(3, 5, 1, 2, 4096, 1, 1, 512)
model.train(3, 10, 1, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 2, 4096, 1, 1, 512)
model.train(3, 1, 2, 2, 4096, 1, 1, 512)
model.train(3, 2, 2, 2, 4096, 1, 1, 512)
model.train(3, 5, 2, 2, 4096, 1, 1, 512)
model.train(3, 10, 2, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 2, 4096, 1, 1, 512)
model.train(3, 1, 4, 2, 4096, 1, 1, 512)
model.train(3, 2, 4, 2, 4096, 1, 1, 512)
model.train(3, 5, 4, 2, 4096, 1, 1, 512)
model.train(3, 10, 4, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 2, 4096, 1, 1, 512)
model.train(3, 1, 8, 2, 4096, 1, 1, 512)
model.train(3, 2, 8, 2, 4096, 1, 1, 512)
model.train(3, 5, 8, 2, 4096, 1, 1, 512)
model.train(3, 10, 8, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 2, 4096, 1, 1, 512)
model.train(3, 1, 16, 2, 4096, 1, 1, 512)
model.train(3, 2, 16, 2, 4096, 1, 1, 512)
model.train(3, 5, 16, 2, 4096, 1, 1, 512)
model.train(3, 10, 16, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 2, 4096, 1, 1, 512)
model.train(3, 1, 32, 2, 4096, 1, 1, 512)
model.train(3, 2, 32, 2, 4096, 1, 1, 512)
model.train(3, 5, 32, 2, 4096, 1, 1, 512)
model.train(3, 10, 32, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 2, 4096, 1, 1, 512)
model.train(3, 1, 64, 2, 4096, 1, 1, 512)
model.train(3, 2, 64, 2, 4096, 1, 1, 512)
model.train(3, 5, 64, 2, 4096, 1, 1, 512)
model.train(3, 10, 64, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 2, 4096, 1, 1, 512)
model.train(3, 1, 128, 2, 4096, 1, 1, 512)
model.train(3, 2, 128, 2, 4096, 1, 1, 512)
model.train(3, 5, 128, 2, 4096, 1, 1, 512)
model.train(3, 10, 128, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 2, 4096, 1, 1, 512)
model.train(3, 1, 256, 2, 4096, 1, 1, 512)
model.train(3, 2, 256, 2, 4096, 1, 1, 512)
model.train(3, 5, 256, 2, 4096, 1, 1, 512)
model.train(3, 10, 256, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 2, 4096, 1, 1, 512)
model.train(3, 1, 512, 2, 4096, 1, 1, 512)
model.train(3, 2, 512, 2, 4096, 1, 1, 512)
model.train(3, 5, 512, 2, 4096, 1, 1, 512)
model.train(3, 10, 512, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 2, 4096, 1, 1, 512)
model.train(3, 1, 1024, 2, 4096, 1, 1, 512)
model.train(3, 2, 1024, 2, 4096, 1, 1, 512)
model.train(3, 5, 1024, 2, 4096, 1, 1, 512)
model.train(3, 10, 1024, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 2, 4096, 1, 1, 512)
model.train(3, 1, 2048, 2, 4096, 1, 1, 512)
model.train(3, 2, 2048, 2, 4096, 1, 1, 512)
model.train(3, 5, 2048, 2, 4096, 1, 1, 512)
model.train(3, 10, 2048, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 2, 4096, 1, 1, 512)
model.train(3, 1, 4096, 2, 4096, 1, 1, 512)
model.train(3, 2, 4096, 2, 4096, 1, 1, 512)
model.train(3, 5, 4096, 2, 4096, 1, 1, 512)
model.train(3, 10, 4096, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 2, 4096, 1, 1, 512)
model.train(3, 1, 8192, 2, 4096, 1, 1, 512)
model.train(3, 2, 8192, 2, 4096, 1, 1, 512)
model.train(3, 5, 8192, 2, 4096, 1, 1, 512)
model.train(3, 10, 8192, 2, 4096, 1, 1, 512)
model.train(3, 0.001, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 4, 4096, 1, 1, 512)
model.train(3, 1, 1, 4, 4096, 1, 1, 512)
model.train(3, 2, 1, 4, 4096, 1, 1, 512)
model.train(3, 5, 1, 4, 4096, 1, 1, 512)
model.train(3, 10, 1, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 4, 4096, 1, 1, 512)
model.train(3, 1, 2, 4, 4096, 1, 1, 512)
model.train(3, 2, 2, 4, 4096, 1, 1, 512)
model.train(3, 5, 2, 4, 4096, 1, 1, 512)
model.train(3, 10, 2, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 4, 4096, 1, 1, 512)
model.train(3, 1, 4, 4, 4096, 1, 1, 512)
model.train(3, 2, 4, 4, 4096, 1, 1, 512)
model.train(3, 5, 4, 4, 4096, 1, 1, 512)
model.train(3, 10, 4, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 4, 4096, 1, 1, 512)
model.train(3, 1, 8, 4, 4096, 1, 1, 512)
model.train(3, 2, 8, 4, 4096, 1, 1, 512)
model.train(3, 5, 8, 4, 4096, 1, 1, 512)
model.train(3, 10, 8, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 4, 4096, 1, 1, 512)
model.train(3, 1, 16, 4, 4096, 1, 1, 512)
model.train(3, 2, 16, 4, 4096, 1, 1, 512)
model.train(3, 5, 16, 4, 4096, 1, 1, 512)
model.train(3, 10, 16, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 4, 4096, 1, 1, 512)
model.train(3, 1, 32, 4, 4096, 1, 1, 512)
model.train(3, 2, 32, 4, 4096, 1, 1, 512)
model.train(3, 5, 32, 4, 4096, 1, 1, 512)
model.train(3, 10, 32, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 4, 4096, 1, 1, 512)
model.train(3, 1, 64, 4, 4096, 1, 1, 512)
model.train(3, 2, 64, 4, 4096, 1, 1, 512)
model.train(3, 5, 64, 4, 4096, 1, 1, 512)
model.train(3, 10, 64, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 4, 4096, 1, 1, 512)
model.train(3, 1, 128, 4, 4096, 1, 1, 512)
model.train(3, 2, 128, 4, 4096, 1, 1, 512)
model.train(3, 5, 128, 4, 4096, 1, 1, 512)
model.train(3, 10, 128, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 4, 4096, 1, 1, 512)
model.train(3, 1, 256, 4, 4096, 1, 1, 512)
model.train(3, 2, 256, 4, 4096, 1, 1, 512)
model.train(3, 5, 256, 4, 4096, 1, 1, 512)
model.train(3, 10, 256, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 4, 4096, 1, 1, 512)
model.train(3, 1, 512, 4, 4096, 1, 1, 512)
model.train(3, 2, 512, 4, 4096, 1, 1, 512)
model.train(3, 5, 512, 4, 4096, 1, 1, 512)
model.train(3, 10, 512, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 4, 4096, 1, 1, 512)
model.train(3, 1, 1024, 4, 4096, 1, 1, 512)
model.train(3, 2, 1024, 4, 4096, 1, 1, 512)
model.train(3, 5, 1024, 4, 4096, 1, 1, 512)
model.train(3, 10, 1024, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 4, 4096, 1, 1, 512)
model.train(3, 1, 2048, 4, 4096, 1, 1, 512)
model.train(3, 2, 2048, 4, 4096, 1, 1, 512)
model.train(3, 5, 2048, 4, 4096, 1, 1, 512)
model.train(3, 10, 2048, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 4, 4096, 1, 1, 512)
model.train(3, 1, 4096, 4, 4096, 1, 1, 512)
model.train(3, 2, 4096, 4, 4096, 1, 1, 512)
model.train(3, 5, 4096, 4, 4096, 1, 1, 512)
model.train(3, 10, 4096, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 4, 4096, 1, 1, 512)
model.train(3, 1, 8192, 4, 4096, 1, 1, 512)
model.train(3, 2, 8192, 4, 4096, 1, 1, 512)
model.train(3, 5, 8192, 4, 4096, 1, 1, 512)
model.train(3, 10, 8192, 4, 4096, 1, 1, 512)
model.train(3, 0.001, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 8, 4096, 1, 1, 512)
model.train(3, 1, 1, 8, 4096, 1, 1, 512)
model.train(3, 2, 1, 8, 4096, 1, 1, 512)
model.train(3, 5, 1, 8, 4096, 1, 1, 512)
model.train(3, 10, 1, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 8, 4096, 1, 1, 512)
model.train(3, 1, 2, 8, 4096, 1, 1, 512)
model.train(3, 2, 2, 8, 4096, 1, 1, 512)
model.train(3, 5, 2, 8, 4096, 1, 1, 512)
model.train(3, 10, 2, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 8, 4096, 1, 1, 512)
model.train(3, 1, 4, 8, 4096, 1, 1, 512)
model.train(3, 2, 4, 8, 4096, 1, 1, 512)
model.train(3, 5, 4, 8, 4096, 1, 1, 512)
model.train(3, 10, 4, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 8, 4096, 1, 1, 512)
model.train(3, 1, 8, 8, 4096, 1, 1, 512)
model.train(3, 2, 8, 8, 4096, 1, 1, 512)
model.train(3, 5, 8, 8, 4096, 1, 1, 512)
model.train(3, 10, 8, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 8, 4096, 1, 1, 512)
model.train(3, 1, 16, 8, 4096, 1, 1, 512)
model.train(3, 2, 16, 8, 4096, 1, 1, 512)
model.train(3, 5, 16, 8, 4096, 1, 1, 512)
model.train(3, 10, 16, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 8, 4096, 1, 1, 512)
model.train(3, 1, 32, 8, 4096, 1, 1, 512)
model.train(3, 2, 32, 8, 4096, 1, 1, 512)
model.train(3, 5, 32, 8, 4096, 1, 1, 512)
model.train(3, 10, 32, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 8, 4096, 1, 1, 512)
model.train(3, 1, 64, 8, 4096, 1, 1, 512)
model.train(3, 2, 64, 8, 4096, 1, 1, 512)
model.train(3, 5, 64, 8, 4096, 1, 1, 512)
model.train(3, 10, 64, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 8, 4096, 1, 1, 512)
model.train(3, 1, 128, 8, 4096, 1, 1, 512)
model.train(3, 2, 128, 8, 4096, 1, 1, 512)
model.train(3, 5, 128, 8, 4096, 1, 1, 512)
model.train(3, 10, 128, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 8, 4096, 1, 1, 512)
model.train(3, 1, 256, 8, 4096, 1, 1, 512)
model.train(3, 2, 256, 8, 4096, 1, 1, 512)
model.train(3, 5, 256, 8, 4096, 1, 1, 512)
model.train(3, 10, 256, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 8, 4096, 1, 1, 512)
model.train(3, 1, 512, 8, 4096, 1, 1, 512)
model.train(3, 2, 512, 8, 4096, 1, 1, 512)
model.train(3, 5, 512, 8, 4096, 1, 1, 512)
model.train(3, 10, 512, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 8, 4096, 1, 1, 512)
model.train(3, 1, 1024, 8, 4096, 1, 1, 512)
model.train(3, 2, 1024, 8, 4096, 1, 1, 512)
model.train(3, 5, 1024, 8, 4096, 1, 1, 512)
model.train(3, 10, 1024, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 8, 4096, 1, 1, 512)
model.train(3, 1, 2048, 8, 4096, 1, 1, 512)
model.train(3, 2, 2048, 8, 4096, 1, 1, 512)
model.train(3, 5, 2048, 8, 4096, 1, 1, 512)
model.train(3, 10, 2048, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 8, 4096, 1, 1, 512)
model.train(3, 1, 4096, 8, 4096, 1, 1, 512)
model.train(3, 2, 4096, 8, 4096, 1, 1, 512)
model.train(3, 5, 4096, 8, 4096, 1, 1, 512)
model.train(3, 10, 4096, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 8, 4096, 1, 1, 512)
model.train(3, 1, 8192, 8, 4096, 1, 1, 512)
model.train(3, 2, 8192, 8, 4096, 1, 1, 512)
model.train(3, 5, 8192, 8, 4096, 1, 1, 512)
model.train(3, 10, 8192, 8, 4096, 1, 1, 512)
model.train(3, 0.001, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 16, 4096, 1, 1, 512)
model.train(3, 1, 1, 16, 4096, 1, 1, 512)
model.train(3, 2, 1, 16, 4096, 1, 1, 512)
model.train(3, 5, 1, 16, 4096, 1, 1, 512)
model.train(3, 10, 1, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 16, 4096, 1, 1, 512)
model.train(3, 1, 2, 16, 4096, 1, 1, 512)
model.train(3, 2, 2, 16, 4096, 1, 1, 512)
model.train(3, 5, 2, 16, 4096, 1, 1, 512)
model.train(3, 10, 2, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 16, 4096, 1, 1, 512)
model.train(3, 1, 4, 16, 4096, 1, 1, 512)
model.train(3, 2, 4, 16, 4096, 1, 1, 512)
model.train(3, 5, 4, 16, 4096, 1, 1, 512)
model.train(3, 10, 4, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 16, 4096, 1, 1, 512)
model.train(3, 1, 8, 16, 4096, 1, 1, 512)
model.train(3, 2, 8, 16, 4096, 1, 1, 512)
model.train(3, 5, 8, 16, 4096, 1, 1, 512)
model.train(3, 10, 8, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 16, 4096, 1, 1, 512)
model.train(3, 1, 16, 16, 4096, 1, 1, 512)
model.train(3, 2, 16, 16, 4096, 1, 1, 512)
model.train(3, 5, 16, 16, 4096, 1, 1, 512)
model.train(3, 10, 16, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 16, 4096, 1, 1, 512)
model.train(3, 1, 32, 16, 4096, 1, 1, 512)
model.train(3, 2, 32, 16, 4096, 1, 1, 512)
model.train(3, 5, 32, 16, 4096, 1, 1, 512)
model.train(3, 10, 32, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 16, 4096, 1, 1, 512)
model.train(3, 1, 64, 16, 4096, 1, 1, 512)
model.train(3, 2, 64, 16, 4096, 1, 1, 512)
model.train(3, 5, 64, 16, 4096, 1, 1, 512)
model.train(3, 10, 64, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 16, 4096, 1, 1, 512)
model.train(3, 1, 128, 16, 4096, 1, 1, 512)
model.train(3, 2, 128, 16, 4096, 1, 1, 512)
model.train(3, 5, 128, 16, 4096, 1, 1, 512)
model.train(3, 10, 128, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 16, 4096, 1, 1, 512)
model.train(3, 1, 256, 16, 4096, 1, 1, 512)
model.train(3, 2, 256, 16, 4096, 1, 1, 512)
model.train(3, 5, 256, 16, 4096, 1, 1, 512)
model.train(3, 10, 256, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 16, 4096, 1, 1, 512)
model.train(3, 1, 512, 16, 4096, 1, 1, 512)
model.train(3, 2, 512, 16, 4096, 1, 1, 512)
model.train(3, 5, 512, 16, 4096, 1, 1, 512)
model.train(3, 10, 512, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 16, 4096, 1, 1, 512)
model.train(3, 1, 1024, 16, 4096, 1, 1, 512)
model.train(3, 2, 1024, 16, 4096, 1, 1, 512)
model.train(3, 5, 1024, 16, 4096, 1, 1, 512)
model.train(3, 10, 1024, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 16, 4096, 1, 1, 512)
model.train(3, 1, 2048, 16, 4096, 1, 1, 512)
model.train(3, 2, 2048, 16, 4096, 1, 1, 512)
model.train(3, 5, 2048, 16, 4096, 1, 1, 512)
model.train(3, 10, 2048, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 16, 4096, 1, 1, 512)
model.train(3, 1, 4096, 16, 4096, 1, 1, 512)
model.train(3, 2, 4096, 16, 4096, 1, 1, 512)
model.train(3, 5, 4096, 16, 4096, 1, 1, 512)
model.train(3, 10, 4096, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 16, 4096, 1, 1, 512)
model.train(3, 1, 8192, 16, 4096, 1, 1, 512)
model.train(3, 2, 8192, 16, 4096, 1, 1, 512)
model.train(3, 5, 8192, 16, 4096, 1, 1, 512)
model.train(3, 10, 8192, 16, 4096, 1, 1, 512)
model.train(3, 0.001, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 32, 4096, 1, 1, 512)
model.train(3, 1, 1, 32, 4096, 1, 1, 512)
model.train(3, 2, 1, 32, 4096, 1, 1, 512)
model.train(3, 5, 1, 32, 4096, 1, 1, 512)
model.train(3, 10, 1, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 32, 4096, 1, 1, 512)
model.train(3, 1, 2, 32, 4096, 1, 1, 512)
model.train(3, 2, 2, 32, 4096, 1, 1, 512)
model.train(3, 5, 2, 32, 4096, 1, 1, 512)
model.train(3, 10, 2, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 32, 4096, 1, 1, 512)
model.train(3, 1, 4, 32, 4096, 1, 1, 512)
model.train(3, 2, 4, 32, 4096, 1, 1, 512)
model.train(3, 5, 4, 32, 4096, 1, 1, 512)
model.train(3, 10, 4, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 32, 4096, 1, 1, 512)
model.train(3, 1, 8, 32, 4096, 1, 1, 512)
model.train(3, 2, 8, 32, 4096, 1, 1, 512)
model.train(3, 5, 8, 32, 4096, 1, 1, 512)
model.train(3, 10, 8, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 32, 4096, 1, 1, 512)
model.train(3, 1, 16, 32, 4096, 1, 1, 512)
model.train(3, 2, 16, 32, 4096, 1, 1, 512)
model.train(3, 5, 16, 32, 4096, 1, 1, 512)
model.train(3, 10, 16, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 32, 4096, 1, 1, 512)
model.train(3, 1, 32, 32, 4096, 1, 1, 512)
model.train(3, 2, 32, 32, 4096, 1, 1, 512)
model.train(3, 5, 32, 32, 4096, 1, 1, 512)
model.train(3, 10, 32, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 32, 4096, 1, 1, 512)
model.train(3, 1, 64, 32, 4096, 1, 1, 512)
model.train(3, 2, 64, 32, 4096, 1, 1, 512)
model.train(3, 5, 64, 32, 4096, 1, 1, 512)
model.train(3, 10, 64, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 32, 4096, 1, 1, 512)
model.train(3, 1, 128, 32, 4096, 1, 1, 512)
model.train(3, 2, 128, 32, 4096, 1, 1, 512)
model.train(3, 5, 128, 32, 4096, 1, 1, 512)
model.train(3, 10, 128, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 32, 4096, 1, 1, 512)
model.train(3, 1, 256, 32, 4096, 1, 1, 512)
model.train(3, 2, 256, 32, 4096, 1, 1, 512)
model.train(3, 5, 256, 32, 4096, 1, 1, 512)
model.train(3, 10, 256, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 32, 4096, 1, 1, 512)
model.train(3, 1, 512, 32, 4096, 1, 1, 512)
model.train(3, 2, 512, 32, 4096, 1, 1, 512)
model.train(3, 5, 512, 32, 4096, 1, 1, 512)
model.train(3, 10, 512, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 32, 4096, 1, 1, 512)
model.train(3, 1, 1024, 32, 4096, 1, 1, 512)
model.train(3, 2, 1024, 32, 4096, 1, 1, 512)
model.train(3, 5, 1024, 32, 4096, 1, 1, 512)
model.train(3, 10, 1024, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 32, 4096, 1, 1, 512)
model.train(3, 1, 2048, 32, 4096, 1, 1, 512)
model.train(3, 2, 2048, 32, 4096, 1, 1, 512)
model.train(3, 5, 2048, 32, 4096, 1, 1, 512)
model.train(3, 10, 2048, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 32, 4096, 1, 1, 512)
model.train(3, 1, 4096, 32, 4096, 1, 1, 512)
model.train(3, 2, 4096, 32, 4096, 1, 1, 512)
model.train(3, 5, 4096, 32, 4096, 1, 1, 512)
model.train(3, 10, 4096, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 32, 4096, 1, 1, 512)
model.train(3, 1, 8192, 32, 4096, 1, 1, 512)
model.train(3, 2, 8192, 32, 4096, 1, 1, 512)
model.train(3, 5, 8192, 32, 4096, 1, 1, 512)
model.train(3, 10, 8192, 32, 4096, 1, 1, 512)
model.train(3, 0.001, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 64, 4096, 1, 1, 512)
model.train(3, 1, 1, 64, 4096, 1, 1, 512)
model.train(3, 2, 1, 64, 4096, 1, 1, 512)
model.train(3, 5, 1, 64, 4096, 1, 1, 512)
model.train(3, 10, 1, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 64, 4096, 1, 1, 512)
model.train(3, 1, 2, 64, 4096, 1, 1, 512)
model.train(3, 2, 2, 64, 4096, 1, 1, 512)
model.train(3, 5, 2, 64, 4096, 1, 1, 512)
model.train(3, 10, 2, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 64, 4096, 1, 1, 512)
model.train(3, 1, 4, 64, 4096, 1, 1, 512)
model.train(3, 2, 4, 64, 4096, 1, 1, 512)
model.train(3, 5, 4, 64, 4096, 1, 1, 512)
model.train(3, 10, 4, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 64, 4096, 1, 1, 512)
model.train(3, 1, 8, 64, 4096, 1, 1, 512)
model.train(3, 2, 8, 64, 4096, 1, 1, 512)
model.train(3, 5, 8, 64, 4096, 1, 1, 512)
model.train(3, 10, 8, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 64, 4096, 1, 1, 512)
model.train(3, 1, 16, 64, 4096, 1, 1, 512)
model.train(3, 2, 16, 64, 4096, 1, 1, 512)
model.train(3, 5, 16, 64, 4096, 1, 1, 512)
model.train(3, 10, 16, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 64, 4096, 1, 1, 512)
model.train(3, 1, 32, 64, 4096, 1, 1, 512)
model.train(3, 2, 32, 64, 4096, 1, 1, 512)
model.train(3, 5, 32, 64, 4096, 1, 1, 512)
model.train(3, 10, 32, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 64, 4096, 1, 1, 512)
model.train(3, 1, 64, 64, 4096, 1, 1, 512)
model.train(3, 2, 64, 64, 4096, 1, 1, 512)
model.train(3, 5, 64, 64, 4096, 1, 1, 512)
model.train(3, 10, 64, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 64, 4096, 1, 1, 512)
model.train(3, 1, 128, 64, 4096, 1, 1, 512)
model.train(3, 2, 128, 64, 4096, 1, 1, 512)
model.train(3, 5, 128, 64, 4096, 1, 1, 512)
model.train(3, 10, 128, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 64, 4096, 1, 1, 512)
model.train(3, 1, 256, 64, 4096, 1, 1, 512)
model.train(3, 2, 256, 64, 4096, 1, 1, 512)
model.train(3, 5, 256, 64, 4096, 1, 1, 512)
model.train(3, 10, 256, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 64, 4096, 1, 1, 512)
model.train(3, 1, 512, 64, 4096, 1, 1, 512)
model.train(3, 2, 512, 64, 4096, 1, 1, 512)
model.train(3, 5, 512, 64, 4096, 1, 1, 512)
model.train(3, 10, 512, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 64, 4096, 1, 1, 512)
model.train(3, 1, 1024, 64, 4096, 1, 1, 512)
model.train(3, 2, 1024, 64, 4096, 1, 1, 512)
model.train(3, 5, 1024, 64, 4096, 1, 1, 512)
model.train(3, 10, 1024, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 64, 4096, 1, 1, 512)
model.train(3, 1, 2048, 64, 4096, 1, 1, 512)
model.train(3, 2, 2048, 64, 4096, 1, 1, 512)
model.train(3, 5, 2048, 64, 4096, 1, 1, 512)
model.train(3, 10, 2048, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 64, 4096, 1, 1, 512)
model.train(3, 1, 4096, 64, 4096, 1, 1, 512)
model.train(3, 2, 4096, 64, 4096, 1, 1, 512)
model.train(3, 5, 4096, 64, 4096, 1, 1, 512)
model.train(3, 10, 4096, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 64, 4096, 1, 1, 512)
model.train(3, 1, 8192, 64, 4096, 1, 1, 512)
model.train(3, 2, 8192, 64, 4096, 1, 1, 512)
model.train(3, 5, 8192, 64, 4096, 1, 1, 512)
model.train(3, 10, 8192, 64, 4096, 1, 1, 512)
model.train(3, 0.001, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 128, 4096, 1, 1, 512)
model.train(3, 1, 1, 128, 4096, 1, 1, 512)
model.train(3, 2, 1, 128, 4096, 1, 1, 512)
model.train(3, 5, 1, 128, 4096, 1, 1, 512)
model.train(3, 10, 1, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 128, 4096, 1, 1, 512)
model.train(3, 1, 2, 128, 4096, 1, 1, 512)
model.train(3, 2, 2, 128, 4096, 1, 1, 512)
model.train(3, 5, 2, 128, 4096, 1, 1, 512)
model.train(3, 10, 2, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 128, 4096, 1, 1, 512)
model.train(3, 1, 4, 128, 4096, 1, 1, 512)
model.train(3, 2, 4, 128, 4096, 1, 1, 512)
model.train(3, 5, 4, 128, 4096, 1, 1, 512)
model.train(3, 10, 4, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 128, 4096, 1, 1, 512)
model.train(3, 1, 8, 128, 4096, 1, 1, 512)
model.train(3, 2, 8, 128, 4096, 1, 1, 512)
model.train(3, 5, 8, 128, 4096, 1, 1, 512)
model.train(3, 10, 8, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 128, 4096, 1, 1, 512)
model.train(3, 1, 16, 128, 4096, 1, 1, 512)
model.train(3, 2, 16, 128, 4096, 1, 1, 512)
model.train(3, 5, 16, 128, 4096, 1, 1, 512)
model.train(3, 10, 16, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 128, 4096, 1, 1, 512)
model.train(3, 1, 32, 128, 4096, 1, 1, 512)
model.train(3, 2, 32, 128, 4096, 1, 1, 512)
model.train(3, 5, 32, 128, 4096, 1, 1, 512)
model.train(3, 10, 32, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 128, 4096, 1, 1, 512)
model.train(3, 1, 64, 128, 4096, 1, 1, 512)
model.train(3, 2, 64, 128, 4096, 1, 1, 512)
model.train(3, 5, 64, 128, 4096, 1, 1, 512)
model.train(3, 10, 64, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 128, 4096, 1, 1, 512)
model.train(3, 1, 128, 128, 4096, 1, 1, 512)
model.train(3, 2, 128, 128, 4096, 1, 1, 512)
model.train(3, 5, 128, 128, 4096, 1, 1, 512)
model.train(3, 10, 128, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 128, 4096, 1, 1, 512)
model.train(3, 1, 256, 128, 4096, 1, 1, 512)
model.train(3, 2, 256, 128, 4096, 1, 1, 512)
model.train(3, 5, 256, 128, 4096, 1, 1, 512)
model.train(3, 10, 256, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 128, 4096, 1, 1, 512)
model.train(3, 1, 512, 128, 4096, 1, 1, 512)
model.train(3, 2, 512, 128, 4096, 1, 1, 512)
model.train(3, 5, 512, 128, 4096, 1, 1, 512)
model.train(3, 10, 512, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 128, 4096, 1, 1, 512)
model.train(3, 1, 1024, 128, 4096, 1, 1, 512)
model.train(3, 2, 1024, 128, 4096, 1, 1, 512)
model.train(3, 5, 1024, 128, 4096, 1, 1, 512)
model.train(3, 10, 1024, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 128, 4096, 1, 1, 512)
model.train(3, 1, 2048, 128, 4096, 1, 1, 512)
model.train(3, 2, 2048, 128, 4096, 1, 1, 512)
model.train(3, 5, 2048, 128, 4096, 1, 1, 512)
model.train(3, 10, 2048, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 128, 4096, 1, 1, 512)
model.train(3, 1, 4096, 128, 4096, 1, 1, 512)
model.train(3, 2, 4096, 128, 4096, 1, 1, 512)
model.train(3, 5, 4096, 128, 4096, 1, 1, 512)
model.train(3, 10, 4096, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 128, 4096, 1, 1, 512)
model.train(3, 1, 8192, 128, 4096, 1, 1, 512)
model.train(3, 2, 8192, 128, 4096, 1, 1, 512)
model.train(3, 5, 8192, 128, 4096, 1, 1, 512)
model.train(3, 10, 8192, 128, 4096, 1, 1, 512)
model.train(3, 0.001, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 1, 256, 4096, 1, 1, 512)
model.train(3, 1, 1, 256, 4096, 1, 1, 512)
model.train(3, 2, 1, 256, 4096, 1, 1, 512)
model.train(3, 5, 1, 256, 4096, 1, 1, 512)
model.train(3, 10, 1, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 2, 256, 4096, 1, 1, 512)
model.train(3, 1, 2, 256, 4096, 1, 1, 512)
model.train(3, 2, 2, 256, 4096, 1, 1, 512)
model.train(3, 5, 2, 256, 4096, 1, 1, 512)
model.train(3, 10, 2, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 4, 256, 4096, 1, 1, 512)
model.train(3, 1, 4, 256, 4096, 1, 1, 512)
model.train(3, 2, 4, 256, 4096, 1, 1, 512)
model.train(3, 5, 4, 256, 4096, 1, 1, 512)
model.train(3, 10, 4, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 8, 256, 4096, 1, 1, 512)
model.train(3, 1, 8, 256, 4096, 1, 1, 512)
model.train(3, 2, 8, 256, 4096, 1, 1, 512)
model.train(3, 5, 8, 256, 4096, 1, 1, 512)
model.train(3, 10, 8, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 16, 256, 4096, 1, 1, 512)
model.train(3, 1, 16, 256, 4096, 1, 1, 512)
model.train(3, 2, 16, 256, 4096, 1, 1, 512)
model.train(3, 5, 16, 256, 4096, 1, 1, 512)
model.train(3, 10, 16, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 32, 256, 4096, 1, 1, 512)
model.train(3, 1, 32, 256, 4096, 1, 1, 512)
model.train(3, 2, 32, 256, 4096, 1, 1, 512)
model.train(3, 5, 32, 256, 4096, 1, 1, 512)
model.train(3, 10, 32, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 64, 256, 4096, 1, 1, 512)
model.train(3, 1, 64, 256, 4096, 1, 1, 512)
model.train(3, 2, 64, 256, 4096, 1, 1, 512)
model.train(3, 5, 64, 256, 4096, 1, 1, 512)
model.train(3, 10, 64, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 128, 256, 4096, 1, 1, 512)
model.train(3, 1, 128, 256, 4096, 1, 1, 512)
model.train(3, 2, 128, 256, 4096, 1, 1, 512)
model.train(3, 5, 128, 256, 4096, 1, 1, 512)
model.train(3, 10, 128, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 256, 256, 4096, 1, 1, 512)
model.train(3, 1, 256, 256, 4096, 1, 1, 512)
model.train(3, 2, 256, 256, 4096, 1, 1, 512)
model.train(3, 5, 256, 256, 4096, 1, 1, 512)
model.train(3, 10, 256, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 512, 256, 4096, 1, 1, 512)
model.train(3, 1, 512, 256, 4096, 1, 1, 512)
model.train(3, 2, 512, 256, 4096, 1, 1, 512)
model.train(3, 5, 512, 256, 4096, 1, 1, 512)
model.train(3, 10, 512, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 1024, 256, 4096, 1, 1, 512)
model.train(3, 1, 1024, 256, 4096, 1, 1, 512)
model.train(3, 2, 1024, 256, 4096, 1, 1, 512)
model.train(3, 5, 1024, 256, 4096, 1, 1, 512)
model.train(3, 10, 1024, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 2048, 256, 4096, 1, 1, 512)
model.train(3, 1, 2048, 256, 4096, 1, 1, 512)
model.train(3, 2, 2048, 256, 4096, 1, 1, 512)
model.train(3, 5, 2048, 256, 4096, 1, 1, 512)
model.train(3, 10, 2048, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 4096, 256, 4096, 1, 1, 512)
model.train(3, 1, 4096, 256, 4096, 1, 1, 512)
model.train(3, 2, 4096, 256, 4096, 1, 1, 512)
model.train(3, 5, 4096, 256, 4096, 1, 1, 512)
model.train(3, 10, 4096, 256, 4096, 1, 1, 512)
model.train(3, 0.001, 8192, 256, 4096, 1, 1, 512)
model.train(3, 0.002, 8192, 256, 4096, 1, 1, 512)
model.train(3, 0.005, 8192, 256, 4096, 1, 1, 512)
model.train(3, 0.01, 8192, 256, 4096, 1, 1, 512)
model.train(3, 0.02, 8192, 256, 4096, 1, 1, 512)
model.train(3, 0.05, 8192, 256, 4096, 1, 1, 512)
model.train(3, 0.1, 8192, 256, 4096, 1, 1, 512)
model.train(3, 0.2, 8192, 256, 4096, 1, 1, 512)
model.train(3, 0.5, 8192, 256, 4096, 1, 1, 512)
model.train(3, 1, 8192, 256, 4096, 1, 1, 512)
model.train(3, 2, 8192, 256, 4096, 1, 1, 512)
model.train(3, 5, 8192, 256, 4096, 1, 1, 512)
model.train(3, 10, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 1, 4096, 1, 1, 512)
model.train(4, 1, 1, 1, 4096, 1, 1, 512)
model.train(4, 2, 1, 1, 4096, 1, 1, 512)
model.train(4, 5, 1, 1, 4096, 1, 1, 512)
model.train(4, 10, 1, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 1, 4096, 1, 1, 512)
model.train(4, 1, 2, 1, 4096, 1, 1, 512)
model.train(4, 2, 2, 1, 4096, 1, 1, 512)
model.train(4, 5, 2, 1, 4096, 1, 1, 512)
model.train(4, 10, 2, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 1, 4096, 1, 1, 512)
model.train(4, 1, 4, 1, 4096, 1, 1, 512)
model.train(4, 2, 4, 1, 4096, 1, 1, 512)
model.train(4, 5, 4, 1, 4096, 1, 1, 512)
model.train(4, 10, 4, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 1, 4096, 1, 1, 512)
model.train(4, 1, 8, 1, 4096, 1, 1, 512)
model.train(4, 2, 8, 1, 4096, 1, 1, 512)
model.train(4, 5, 8, 1, 4096, 1, 1, 512)
model.train(4, 10, 8, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 1, 4096, 1, 1, 512)
model.train(4, 1, 16, 1, 4096, 1, 1, 512)
model.train(4, 2, 16, 1, 4096, 1, 1, 512)
model.train(4, 5, 16, 1, 4096, 1, 1, 512)
model.train(4, 10, 16, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 1, 4096, 1, 1, 512)
model.train(4, 1, 32, 1, 4096, 1, 1, 512)
model.train(4, 2, 32, 1, 4096, 1, 1, 512)
model.train(4, 5, 32, 1, 4096, 1, 1, 512)
model.train(4, 10, 32, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 1, 4096, 1, 1, 512)
model.train(4, 1, 64, 1, 4096, 1, 1, 512)
model.train(4, 2, 64, 1, 4096, 1, 1, 512)
model.train(4, 5, 64, 1, 4096, 1, 1, 512)
model.train(4, 10, 64, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 1, 4096, 1, 1, 512)
model.train(4, 1, 128, 1, 4096, 1, 1, 512)
model.train(4, 2, 128, 1, 4096, 1, 1, 512)
model.train(4, 5, 128, 1, 4096, 1, 1, 512)
model.train(4, 10, 128, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 1, 4096, 1, 1, 512)
model.train(4, 1, 256, 1, 4096, 1, 1, 512)
model.train(4, 2, 256, 1, 4096, 1, 1, 512)
model.train(4, 5, 256, 1, 4096, 1, 1, 512)
model.train(4, 10, 256, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 1, 4096, 1, 1, 512)
model.train(4, 1, 512, 1, 4096, 1, 1, 512)
model.train(4, 2, 512, 1, 4096, 1, 1, 512)
model.train(4, 5, 512, 1, 4096, 1, 1, 512)
model.train(4, 10, 512, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 1, 4096, 1, 1, 512)
model.train(4, 1, 1024, 1, 4096, 1, 1, 512)
model.train(4, 2, 1024, 1, 4096, 1, 1, 512)
model.train(4, 5, 1024, 1, 4096, 1, 1, 512)
model.train(4, 10, 1024, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 1, 4096, 1, 1, 512)
model.train(4, 1, 2048, 1, 4096, 1, 1, 512)
model.train(4, 2, 2048, 1, 4096, 1, 1, 512)
model.train(4, 5, 2048, 1, 4096, 1, 1, 512)
model.train(4, 10, 2048, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 1, 4096, 1, 1, 512)
model.train(4, 1, 4096, 1, 4096, 1, 1, 512)
model.train(4, 2, 4096, 1, 4096, 1, 1, 512)
model.train(4, 5, 4096, 1, 4096, 1, 1, 512)
model.train(4, 10, 4096, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 1, 4096, 1, 1, 512)
model.train(4, 1, 8192, 1, 4096, 1, 1, 512)
model.train(4, 2, 8192, 1, 4096, 1, 1, 512)
model.train(4, 5, 8192, 1, 4096, 1, 1, 512)
model.train(4, 10, 8192, 1, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 2, 4096, 1, 1, 512)
model.train(4, 1, 1, 2, 4096, 1, 1, 512)
model.train(4, 2, 1, 2, 4096, 1, 1, 512)
model.train(4, 5, 1, 2, 4096, 1, 1, 512)
model.train(4, 10, 1, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 2, 4096, 1, 1, 512)
model.train(4, 1, 2, 2, 4096, 1, 1, 512)
model.train(4, 2, 2, 2, 4096, 1, 1, 512)
model.train(4, 5, 2, 2, 4096, 1, 1, 512)
model.train(4, 10, 2, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 2, 4096, 1, 1, 512)
model.train(4, 1, 4, 2, 4096, 1, 1, 512)
model.train(4, 2, 4, 2, 4096, 1, 1, 512)
model.train(4, 5, 4, 2, 4096, 1, 1, 512)
model.train(4, 10, 4, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 2, 4096, 1, 1, 512)
model.train(4, 1, 8, 2, 4096, 1, 1, 512)
model.train(4, 2, 8, 2, 4096, 1, 1, 512)
model.train(4, 5, 8, 2, 4096, 1, 1, 512)
model.train(4, 10, 8, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 2, 4096, 1, 1, 512)
model.train(4, 1, 16, 2, 4096, 1, 1, 512)
model.train(4, 2, 16, 2, 4096, 1, 1, 512)
model.train(4, 5, 16, 2, 4096, 1, 1, 512)
model.train(4, 10, 16, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 2, 4096, 1, 1, 512)
model.train(4, 1, 32, 2, 4096, 1, 1, 512)
model.train(4, 2, 32, 2, 4096, 1, 1, 512)
model.train(4, 5, 32, 2, 4096, 1, 1, 512)
model.train(4, 10, 32, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 2, 4096, 1, 1, 512)
model.train(4, 1, 64, 2, 4096, 1, 1, 512)
model.train(4, 2, 64, 2, 4096, 1, 1, 512)
model.train(4, 5, 64, 2, 4096, 1, 1, 512)
model.train(4, 10, 64, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 2, 4096, 1, 1, 512)
model.train(4, 1, 128, 2, 4096, 1, 1, 512)
model.train(4, 2, 128, 2, 4096, 1, 1, 512)
model.train(4, 5, 128, 2, 4096, 1, 1, 512)
model.train(4, 10, 128, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 2, 4096, 1, 1, 512)
model.train(4, 1, 256, 2, 4096, 1, 1, 512)
model.train(4, 2, 256, 2, 4096, 1, 1, 512)
model.train(4, 5, 256, 2, 4096, 1, 1, 512)
model.train(4, 10, 256, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 2, 4096, 1, 1, 512)
model.train(4, 1, 512, 2, 4096, 1, 1, 512)
model.train(4, 2, 512, 2, 4096, 1, 1, 512)
model.train(4, 5, 512, 2, 4096, 1, 1, 512)
model.train(4, 10, 512, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 2, 4096, 1, 1, 512)
model.train(4, 1, 1024, 2, 4096, 1, 1, 512)
model.train(4, 2, 1024, 2, 4096, 1, 1, 512)
model.train(4, 5, 1024, 2, 4096, 1, 1, 512)
model.train(4, 10, 1024, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 2, 4096, 1, 1, 512)
model.train(4, 1, 2048, 2, 4096, 1, 1, 512)
model.train(4, 2, 2048, 2, 4096, 1, 1, 512)
model.train(4, 5, 2048, 2, 4096, 1, 1, 512)
model.train(4, 10, 2048, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 2, 4096, 1, 1, 512)
model.train(4, 1, 4096, 2, 4096, 1, 1, 512)
model.train(4, 2, 4096, 2, 4096, 1, 1, 512)
model.train(4, 5, 4096, 2, 4096, 1, 1, 512)
model.train(4, 10, 4096, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 2, 4096, 1, 1, 512)
model.train(4, 1, 8192, 2, 4096, 1, 1, 512)
model.train(4, 2, 8192, 2, 4096, 1, 1, 512)
model.train(4, 5, 8192, 2, 4096, 1, 1, 512)
model.train(4, 10, 8192, 2, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 4, 4096, 1, 1, 512)
model.train(4, 1, 1, 4, 4096, 1, 1, 512)
model.train(4, 2, 1, 4, 4096, 1, 1, 512)
model.train(4, 5, 1, 4, 4096, 1, 1, 512)
model.train(4, 10, 1, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 4, 4096, 1, 1, 512)
model.train(4, 1, 2, 4, 4096, 1, 1, 512)
model.train(4, 2, 2, 4, 4096, 1, 1, 512)
model.train(4, 5, 2, 4, 4096, 1, 1, 512)
model.train(4, 10, 2, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 4, 4096, 1, 1, 512)
model.train(4, 1, 4, 4, 4096, 1, 1, 512)
model.train(4, 2, 4, 4, 4096, 1, 1, 512)
model.train(4, 5, 4, 4, 4096, 1, 1, 512)
model.train(4, 10, 4, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 4, 4096, 1, 1, 512)
model.train(4, 1, 8, 4, 4096, 1, 1, 512)
model.train(4, 2, 8, 4, 4096, 1, 1, 512)
model.train(4, 5, 8, 4, 4096, 1, 1, 512)
model.train(4, 10, 8, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 4, 4096, 1, 1, 512)
model.train(4, 1, 16, 4, 4096, 1, 1, 512)
model.train(4, 2, 16, 4, 4096, 1, 1, 512)
model.train(4, 5, 16, 4, 4096, 1, 1, 512)
model.train(4, 10, 16, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 4, 4096, 1, 1, 512)
model.train(4, 1, 32, 4, 4096, 1, 1, 512)
model.train(4, 2, 32, 4, 4096, 1, 1, 512)
model.train(4, 5, 32, 4, 4096, 1, 1, 512)
model.train(4, 10, 32, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 4, 4096, 1, 1, 512)
model.train(4, 1, 64, 4, 4096, 1, 1, 512)
model.train(4, 2, 64, 4, 4096, 1, 1, 512)
model.train(4, 5, 64, 4, 4096, 1, 1, 512)
model.train(4, 10, 64, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 4, 4096, 1, 1, 512)
model.train(4, 1, 128, 4, 4096, 1, 1, 512)
model.train(4, 2, 128, 4, 4096, 1, 1, 512)
model.train(4, 5, 128, 4, 4096, 1, 1, 512)
model.train(4, 10, 128, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 4, 4096, 1, 1, 512)
model.train(4, 1, 256, 4, 4096, 1, 1, 512)
model.train(4, 2, 256, 4, 4096, 1, 1, 512)
model.train(4, 5, 256, 4, 4096, 1, 1, 512)
model.train(4, 10, 256, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 4, 4096, 1, 1, 512)
model.train(4, 1, 512, 4, 4096, 1, 1, 512)
model.train(4, 2, 512, 4, 4096, 1, 1, 512)
model.train(4, 5, 512, 4, 4096, 1, 1, 512)
model.train(4, 10, 512, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 4, 4096, 1, 1, 512)
model.train(4, 1, 1024, 4, 4096, 1, 1, 512)
model.train(4, 2, 1024, 4, 4096, 1, 1, 512)
model.train(4, 5, 1024, 4, 4096, 1, 1, 512)
model.train(4, 10, 1024, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 4, 4096, 1, 1, 512)
model.train(4, 1, 2048, 4, 4096, 1, 1, 512)
model.train(4, 2, 2048, 4, 4096, 1, 1, 512)
model.train(4, 5, 2048, 4, 4096, 1, 1, 512)
model.train(4, 10, 2048, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 4, 4096, 1, 1, 512)
model.train(4, 1, 4096, 4, 4096, 1, 1, 512)
model.train(4, 2, 4096, 4, 4096, 1, 1, 512)
model.train(4, 5, 4096, 4, 4096, 1, 1, 512)
model.train(4, 10, 4096, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 4, 4096, 1, 1, 512)
model.train(4, 1, 8192, 4, 4096, 1, 1, 512)
model.train(4, 2, 8192, 4, 4096, 1, 1, 512)
model.train(4, 5, 8192, 4, 4096, 1, 1, 512)
model.train(4, 10, 8192, 4, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 8, 4096, 1, 1, 512)
model.train(4, 1, 1, 8, 4096, 1, 1, 512)
model.train(4, 2, 1, 8, 4096, 1, 1, 512)
model.train(4, 5, 1, 8, 4096, 1, 1, 512)
model.train(4, 10, 1, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 8, 4096, 1, 1, 512)
model.train(4, 1, 2, 8, 4096, 1, 1, 512)
model.train(4, 2, 2, 8, 4096, 1, 1, 512)
model.train(4, 5, 2, 8, 4096, 1, 1, 512)
model.train(4, 10, 2, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 8, 4096, 1, 1, 512)
model.train(4, 1, 4, 8, 4096, 1, 1, 512)
model.train(4, 2, 4, 8, 4096, 1, 1, 512)
model.train(4, 5, 4, 8, 4096, 1, 1, 512)
model.train(4, 10, 4, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 8, 4096, 1, 1, 512)
model.train(4, 1, 8, 8, 4096, 1, 1, 512)
model.train(4, 2, 8, 8, 4096, 1, 1, 512)
model.train(4, 5, 8, 8, 4096, 1, 1, 512)
model.train(4, 10, 8, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 8, 4096, 1, 1, 512)
model.train(4, 1, 16, 8, 4096, 1, 1, 512)
model.train(4, 2, 16, 8, 4096, 1, 1, 512)
model.train(4, 5, 16, 8, 4096, 1, 1, 512)
model.train(4, 10, 16, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 8, 4096, 1, 1, 512)
model.train(4, 1, 32, 8, 4096, 1, 1, 512)
model.train(4, 2, 32, 8, 4096, 1, 1, 512)
model.train(4, 5, 32, 8, 4096, 1, 1, 512)
model.train(4, 10, 32, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 8, 4096, 1, 1, 512)
model.train(4, 1, 64, 8, 4096, 1, 1, 512)
model.train(4, 2, 64, 8, 4096, 1, 1, 512)
model.train(4, 5, 64, 8, 4096, 1, 1, 512)
model.train(4, 10, 64, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 8, 4096, 1, 1, 512)
model.train(4, 1, 128, 8, 4096, 1, 1, 512)
model.train(4, 2, 128, 8, 4096, 1, 1, 512)
model.train(4, 5, 128, 8, 4096, 1, 1, 512)
model.train(4, 10, 128, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 8, 4096, 1, 1, 512)
model.train(4, 1, 256, 8, 4096, 1, 1, 512)
model.train(4, 2, 256, 8, 4096, 1, 1, 512)
model.train(4, 5, 256, 8, 4096, 1, 1, 512)
model.train(4, 10, 256, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 8, 4096, 1, 1, 512)
model.train(4, 1, 512, 8, 4096, 1, 1, 512)
model.train(4, 2, 512, 8, 4096, 1, 1, 512)
model.train(4, 5, 512, 8, 4096, 1, 1, 512)
model.train(4, 10, 512, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 8, 4096, 1, 1, 512)
model.train(4, 1, 1024, 8, 4096, 1, 1, 512)
model.train(4, 2, 1024, 8, 4096, 1, 1, 512)
model.train(4, 5, 1024, 8, 4096, 1, 1, 512)
model.train(4, 10, 1024, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 8, 4096, 1, 1, 512)
model.train(4, 1, 2048, 8, 4096, 1, 1, 512)
model.train(4, 2, 2048, 8, 4096, 1, 1, 512)
model.train(4, 5, 2048, 8, 4096, 1, 1, 512)
model.train(4, 10, 2048, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 8, 4096, 1, 1, 512)
model.train(4, 1, 4096, 8, 4096, 1, 1, 512)
model.train(4, 2, 4096, 8, 4096, 1, 1, 512)
model.train(4, 5, 4096, 8, 4096, 1, 1, 512)
model.train(4, 10, 4096, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 8, 4096, 1, 1, 512)
model.train(4, 1, 8192, 8, 4096, 1, 1, 512)
model.train(4, 2, 8192, 8, 4096, 1, 1, 512)
model.train(4, 5, 8192, 8, 4096, 1, 1, 512)
model.train(4, 10, 8192, 8, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 16, 4096, 1, 1, 512)
model.train(4, 1, 1, 16, 4096, 1, 1, 512)
model.train(4, 2, 1, 16, 4096, 1, 1, 512)
model.train(4, 5, 1, 16, 4096, 1, 1, 512)
model.train(4, 10, 1, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 16, 4096, 1, 1, 512)
model.train(4, 1, 2, 16, 4096, 1, 1, 512)
model.train(4, 2, 2, 16, 4096, 1, 1, 512)
model.train(4, 5, 2, 16, 4096, 1, 1, 512)
model.train(4, 10, 2, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 16, 4096, 1, 1, 512)
model.train(4, 1, 4, 16, 4096, 1, 1, 512)
model.train(4, 2, 4, 16, 4096, 1, 1, 512)
model.train(4, 5, 4, 16, 4096, 1, 1, 512)
model.train(4, 10, 4, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 16, 4096, 1, 1, 512)
model.train(4, 1, 8, 16, 4096, 1, 1, 512)
model.train(4, 2, 8, 16, 4096, 1, 1, 512)
model.train(4, 5, 8, 16, 4096, 1, 1, 512)
model.train(4, 10, 8, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 16, 4096, 1, 1, 512)
model.train(4, 1, 16, 16, 4096, 1, 1, 512)
model.train(4, 2, 16, 16, 4096, 1, 1, 512)
model.train(4, 5, 16, 16, 4096, 1, 1, 512)
model.train(4, 10, 16, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 16, 4096, 1, 1, 512)
model.train(4, 1, 32, 16, 4096, 1, 1, 512)
model.train(4, 2, 32, 16, 4096, 1, 1, 512)
model.train(4, 5, 32, 16, 4096, 1, 1, 512)
model.train(4, 10, 32, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 16, 4096, 1, 1, 512)
model.train(4, 1, 64, 16, 4096, 1, 1, 512)
model.train(4, 2, 64, 16, 4096, 1, 1, 512)
model.train(4, 5, 64, 16, 4096, 1, 1, 512)
model.train(4, 10, 64, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 16, 4096, 1, 1, 512)
model.train(4, 1, 128, 16, 4096, 1, 1, 512)
model.train(4, 2, 128, 16, 4096, 1, 1, 512)
model.train(4, 5, 128, 16, 4096, 1, 1, 512)
model.train(4, 10, 128, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 16, 4096, 1, 1, 512)
model.train(4, 1, 256, 16, 4096, 1, 1, 512)
model.train(4, 2, 256, 16, 4096, 1, 1, 512)
model.train(4, 5, 256, 16, 4096, 1, 1, 512)
model.train(4, 10, 256, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 16, 4096, 1, 1, 512)
model.train(4, 1, 512, 16, 4096, 1, 1, 512)
model.train(4, 2, 512, 16, 4096, 1, 1, 512)
model.train(4, 5, 512, 16, 4096, 1, 1, 512)
model.train(4, 10, 512, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 16, 4096, 1, 1, 512)
model.train(4, 1, 1024, 16, 4096, 1, 1, 512)
model.train(4, 2, 1024, 16, 4096, 1, 1, 512)
model.train(4, 5, 1024, 16, 4096, 1, 1, 512)
model.train(4, 10, 1024, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 16, 4096, 1, 1, 512)
model.train(4, 1, 2048, 16, 4096, 1, 1, 512)
model.train(4, 2, 2048, 16, 4096, 1, 1, 512)
model.train(4, 5, 2048, 16, 4096, 1, 1, 512)
model.train(4, 10, 2048, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 16, 4096, 1, 1, 512)
model.train(4, 1, 4096, 16, 4096, 1, 1, 512)
model.train(4, 2, 4096, 16, 4096, 1, 1, 512)
model.train(4, 5, 4096, 16, 4096, 1, 1, 512)
model.train(4, 10, 4096, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 16, 4096, 1, 1, 512)
model.train(4, 1, 8192, 16, 4096, 1, 1, 512)
model.train(4, 2, 8192, 16, 4096, 1, 1, 512)
model.train(4, 5, 8192, 16, 4096, 1, 1, 512)
model.train(4, 10, 8192, 16, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 32, 4096, 1, 1, 512)
model.train(4, 1, 1, 32, 4096, 1, 1, 512)
model.train(4, 2, 1, 32, 4096, 1, 1, 512)
model.train(4, 5, 1, 32, 4096, 1, 1, 512)
model.train(4, 10, 1, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 32, 4096, 1, 1, 512)
model.train(4, 1, 2, 32, 4096, 1, 1, 512)
model.train(4, 2, 2, 32, 4096, 1, 1, 512)
model.train(4, 5, 2, 32, 4096, 1, 1, 512)
model.train(4, 10, 2, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 32, 4096, 1, 1, 512)
model.train(4, 1, 4, 32, 4096, 1, 1, 512)
model.train(4, 2, 4, 32, 4096, 1, 1, 512)
model.train(4, 5, 4, 32, 4096, 1, 1, 512)
model.train(4, 10, 4, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 32, 4096, 1, 1, 512)
model.train(4, 1, 8, 32, 4096, 1, 1, 512)
model.train(4, 2, 8, 32, 4096, 1, 1, 512)
model.train(4, 5, 8, 32, 4096, 1, 1, 512)
model.train(4, 10, 8, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 32, 4096, 1, 1, 512)
model.train(4, 1, 16, 32, 4096, 1, 1, 512)
model.train(4, 2, 16, 32, 4096, 1, 1, 512)
model.train(4, 5, 16, 32, 4096, 1, 1, 512)
model.train(4, 10, 16, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 32, 4096, 1, 1, 512)
model.train(4, 1, 32, 32, 4096, 1, 1, 512)
model.train(4, 2, 32, 32, 4096, 1, 1, 512)
model.train(4, 5, 32, 32, 4096, 1, 1, 512)
model.train(4, 10, 32, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 32, 4096, 1, 1, 512)
model.train(4, 1, 64, 32, 4096, 1, 1, 512)
model.train(4, 2, 64, 32, 4096, 1, 1, 512)
model.train(4, 5, 64, 32, 4096, 1, 1, 512)
model.train(4, 10, 64, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 32, 4096, 1, 1, 512)
model.train(4, 1, 128, 32, 4096, 1, 1, 512)
model.train(4, 2, 128, 32, 4096, 1, 1, 512)
model.train(4, 5, 128, 32, 4096, 1, 1, 512)
model.train(4, 10, 128, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 32, 4096, 1, 1, 512)
model.train(4, 1, 256, 32, 4096, 1, 1, 512)
model.train(4, 2, 256, 32, 4096, 1, 1, 512)
model.train(4, 5, 256, 32, 4096, 1, 1, 512)
model.train(4, 10, 256, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 32, 4096, 1, 1, 512)
model.train(4, 1, 512, 32, 4096, 1, 1, 512)
model.train(4, 2, 512, 32, 4096, 1, 1, 512)
model.train(4, 5, 512, 32, 4096, 1, 1, 512)
model.train(4, 10, 512, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 32, 4096, 1, 1, 512)
model.train(4, 1, 1024, 32, 4096, 1, 1, 512)
model.train(4, 2, 1024, 32, 4096, 1, 1, 512)
model.train(4, 5, 1024, 32, 4096, 1, 1, 512)
model.train(4, 10, 1024, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 32, 4096, 1, 1, 512)
model.train(4, 1, 2048, 32, 4096, 1, 1, 512)
model.train(4, 2, 2048, 32, 4096, 1, 1, 512)
model.train(4, 5, 2048, 32, 4096, 1, 1, 512)
model.train(4, 10, 2048, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 32, 4096, 1, 1, 512)
model.train(4, 1, 4096, 32, 4096, 1, 1, 512)
model.train(4, 2, 4096, 32, 4096, 1, 1, 512)
model.train(4, 5, 4096, 32, 4096, 1, 1, 512)
model.train(4, 10, 4096, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 32, 4096, 1, 1, 512)
model.train(4, 1, 8192, 32, 4096, 1, 1, 512)
model.train(4, 2, 8192, 32, 4096, 1, 1, 512)
model.train(4, 5, 8192, 32, 4096, 1, 1, 512)
model.train(4, 10, 8192, 32, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 64, 4096, 1, 1, 512)
model.train(4, 1, 1, 64, 4096, 1, 1, 512)
model.train(4, 2, 1, 64, 4096, 1, 1, 512)
model.train(4, 5, 1, 64, 4096, 1, 1, 512)
model.train(4, 10, 1, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 64, 4096, 1, 1, 512)
model.train(4, 1, 2, 64, 4096, 1, 1, 512)
model.train(4, 2, 2, 64, 4096, 1, 1, 512)
model.train(4, 5, 2, 64, 4096, 1, 1, 512)
model.train(4, 10, 2, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 64, 4096, 1, 1, 512)
model.train(4, 1, 4, 64, 4096, 1, 1, 512)
model.train(4, 2, 4, 64, 4096, 1, 1, 512)
model.train(4, 5, 4, 64, 4096, 1, 1, 512)
model.train(4, 10, 4, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 64, 4096, 1, 1, 512)
model.train(4, 1, 8, 64, 4096, 1, 1, 512)
model.train(4, 2, 8, 64, 4096, 1, 1, 512)
model.train(4, 5, 8, 64, 4096, 1, 1, 512)
model.train(4, 10, 8, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 64, 4096, 1, 1, 512)
model.train(4, 1, 16, 64, 4096, 1, 1, 512)
model.train(4, 2, 16, 64, 4096, 1, 1, 512)
model.train(4, 5, 16, 64, 4096, 1, 1, 512)
model.train(4, 10, 16, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 64, 4096, 1, 1, 512)
model.train(4, 1, 32, 64, 4096, 1, 1, 512)
model.train(4, 2, 32, 64, 4096, 1, 1, 512)
model.train(4, 5, 32, 64, 4096, 1, 1, 512)
model.train(4, 10, 32, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 64, 4096, 1, 1, 512)
model.train(4, 1, 64, 64, 4096, 1, 1, 512)
model.train(4, 2, 64, 64, 4096, 1, 1, 512)
model.train(4, 5, 64, 64, 4096, 1, 1, 512)
model.train(4, 10, 64, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 64, 4096, 1, 1, 512)
model.train(4, 1, 128, 64, 4096, 1, 1, 512)
model.train(4, 2, 128, 64, 4096, 1, 1, 512)
model.train(4, 5, 128, 64, 4096, 1, 1, 512)
model.train(4, 10, 128, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 64, 4096, 1, 1, 512)
model.train(4, 1, 256, 64, 4096, 1, 1, 512)
model.train(4, 2, 256, 64, 4096, 1, 1, 512)
model.train(4, 5, 256, 64, 4096, 1, 1, 512)
model.train(4, 10, 256, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 64, 4096, 1, 1, 512)
model.train(4, 1, 512, 64, 4096, 1, 1, 512)
model.train(4, 2, 512, 64, 4096, 1, 1, 512)
model.train(4, 5, 512, 64, 4096, 1, 1, 512)
model.train(4, 10, 512, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 64, 4096, 1, 1, 512)
model.train(4, 1, 1024, 64, 4096, 1, 1, 512)
model.train(4, 2, 1024, 64, 4096, 1, 1, 512)
model.train(4, 5, 1024, 64, 4096, 1, 1, 512)
model.train(4, 10, 1024, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 64, 4096, 1, 1, 512)
model.train(4, 1, 2048, 64, 4096, 1, 1, 512)
model.train(4, 2, 2048, 64, 4096, 1, 1, 512)
model.train(4, 5, 2048, 64, 4096, 1, 1, 512)
model.train(4, 10, 2048, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 64, 4096, 1, 1, 512)
model.train(4, 1, 4096, 64, 4096, 1, 1, 512)
model.train(4, 2, 4096, 64, 4096, 1, 1, 512)
model.train(4, 5, 4096, 64, 4096, 1, 1, 512)
model.train(4, 10, 4096, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 64, 4096, 1, 1, 512)
model.train(4, 1, 8192, 64, 4096, 1, 1, 512)
model.train(4, 2, 8192, 64, 4096, 1, 1, 512)
model.train(4, 5, 8192, 64, 4096, 1, 1, 512)
model.train(4, 10, 8192, 64, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 128, 4096, 1, 1, 512)
model.train(4, 1, 1, 128, 4096, 1, 1, 512)
model.train(4, 2, 1, 128, 4096, 1, 1, 512)
model.train(4, 5, 1, 128, 4096, 1, 1, 512)
model.train(4, 10, 1, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 128, 4096, 1, 1, 512)
model.train(4, 1, 2, 128, 4096, 1, 1, 512)
model.train(4, 2, 2, 128, 4096, 1, 1, 512)
model.train(4, 5, 2, 128, 4096, 1, 1, 512)
model.train(4, 10, 2, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 128, 4096, 1, 1, 512)
model.train(4, 1, 4, 128, 4096, 1, 1, 512)
model.train(4, 2, 4, 128, 4096, 1, 1, 512)
model.train(4, 5, 4, 128, 4096, 1, 1, 512)
model.train(4, 10, 4, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 128, 4096, 1, 1, 512)
model.train(4, 1, 8, 128, 4096, 1, 1, 512)
model.train(4, 2, 8, 128, 4096, 1, 1, 512)
model.train(4, 5, 8, 128, 4096, 1, 1, 512)
model.train(4, 10, 8, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 128, 4096, 1, 1, 512)
model.train(4, 1, 16, 128, 4096, 1, 1, 512)
model.train(4, 2, 16, 128, 4096, 1, 1, 512)
model.train(4, 5, 16, 128, 4096, 1, 1, 512)
model.train(4, 10, 16, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 128, 4096, 1, 1, 512)
model.train(4, 1, 32, 128, 4096, 1, 1, 512)
model.train(4, 2, 32, 128, 4096, 1, 1, 512)
model.train(4, 5, 32, 128, 4096, 1, 1, 512)
model.train(4, 10, 32, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 128, 4096, 1, 1, 512)
model.train(4, 1, 64, 128, 4096, 1, 1, 512)
model.train(4, 2, 64, 128, 4096, 1, 1, 512)
model.train(4, 5, 64, 128, 4096, 1, 1, 512)
model.train(4, 10, 64, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 128, 4096, 1, 1, 512)
model.train(4, 1, 128, 128, 4096, 1, 1, 512)
model.train(4, 2, 128, 128, 4096, 1, 1, 512)
model.train(4, 5, 128, 128, 4096, 1, 1, 512)
model.train(4, 10, 128, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 128, 4096, 1, 1, 512)
model.train(4, 1, 256, 128, 4096, 1, 1, 512)
model.train(4, 2, 256, 128, 4096, 1, 1, 512)
model.train(4, 5, 256, 128, 4096, 1, 1, 512)
model.train(4, 10, 256, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 128, 4096, 1, 1, 512)
model.train(4, 1, 512, 128, 4096, 1, 1, 512)
model.train(4, 2, 512, 128, 4096, 1, 1, 512)
model.train(4, 5, 512, 128, 4096, 1, 1, 512)
model.train(4, 10, 512, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 128, 4096, 1, 1, 512)
model.train(4, 1, 1024, 128, 4096, 1, 1, 512)
model.train(4, 2, 1024, 128, 4096, 1, 1, 512)
model.train(4, 5, 1024, 128, 4096, 1, 1, 512)
model.train(4, 10, 1024, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 128, 4096, 1, 1, 512)
model.train(4, 1, 2048, 128, 4096, 1, 1, 512)
model.train(4, 2, 2048, 128, 4096, 1, 1, 512)
model.train(4, 5, 2048, 128, 4096, 1, 1, 512)
model.train(4, 10, 2048, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 128, 4096, 1, 1, 512)
model.train(4, 1, 4096, 128, 4096, 1, 1, 512)
model.train(4, 2, 4096, 128, 4096, 1, 1, 512)
model.train(4, 5, 4096, 128, 4096, 1, 1, 512)
model.train(4, 10, 4096, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 128, 4096, 1, 1, 512)
model.train(4, 1, 8192, 128, 4096, 1, 1, 512)
model.train(4, 2, 8192, 128, 4096, 1, 1, 512)
model.train(4, 5, 8192, 128, 4096, 1, 1, 512)
model.train(4, 10, 8192, 128, 4096, 1, 1, 512)
model.train(4, 0.001, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 1, 256, 4096, 1, 1, 512)
model.train(4, 1, 1, 256, 4096, 1, 1, 512)
model.train(4, 2, 1, 256, 4096, 1, 1, 512)
model.train(4, 5, 1, 256, 4096, 1, 1, 512)
model.train(4, 10, 1, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 2, 256, 4096, 1, 1, 512)
model.train(4, 1, 2, 256, 4096, 1, 1, 512)
model.train(4, 2, 2, 256, 4096, 1, 1, 512)
model.train(4, 5, 2, 256, 4096, 1, 1, 512)
model.train(4, 10, 2, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 4, 256, 4096, 1, 1, 512)
model.train(4, 1, 4, 256, 4096, 1, 1, 512)
model.train(4, 2, 4, 256, 4096, 1, 1, 512)
model.train(4, 5, 4, 256, 4096, 1, 1, 512)
model.train(4, 10, 4, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 8, 256, 4096, 1, 1, 512)
model.train(4, 1, 8, 256, 4096, 1, 1, 512)
model.train(4, 2, 8, 256, 4096, 1, 1, 512)
model.train(4, 5, 8, 256, 4096, 1, 1, 512)
model.train(4, 10, 8, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 16, 256, 4096, 1, 1, 512)
model.train(4, 1, 16, 256, 4096, 1, 1, 512)
model.train(4, 2, 16, 256, 4096, 1, 1, 512)
model.train(4, 5, 16, 256, 4096, 1, 1, 512)
model.train(4, 10, 16, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 32, 256, 4096, 1, 1, 512)
model.train(4, 1, 32, 256, 4096, 1, 1, 512)
model.train(4, 2, 32, 256, 4096, 1, 1, 512)
model.train(4, 5, 32, 256, 4096, 1, 1, 512)
model.train(4, 10, 32, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 64, 256, 4096, 1, 1, 512)
model.train(4, 1, 64, 256, 4096, 1, 1, 512)
model.train(4, 2, 64, 256, 4096, 1, 1, 512)
model.train(4, 5, 64, 256, 4096, 1, 1, 512)
model.train(4, 10, 64, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 128, 256, 4096, 1, 1, 512)
model.train(4, 1, 128, 256, 4096, 1, 1, 512)
model.train(4, 2, 128, 256, 4096, 1, 1, 512)
model.train(4, 5, 128, 256, 4096, 1, 1, 512)
model.train(4, 10, 128, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 256, 256, 4096, 1, 1, 512)
model.train(4, 1, 256, 256, 4096, 1, 1, 512)
model.train(4, 2, 256, 256, 4096, 1, 1, 512)
model.train(4, 5, 256, 256, 4096, 1, 1, 512)
model.train(4, 10, 256, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 512, 256, 4096, 1, 1, 512)
model.train(4, 1, 512, 256, 4096, 1, 1, 512)
model.train(4, 2, 512, 256, 4096, 1, 1, 512)
model.train(4, 5, 512, 256, 4096, 1, 1, 512)
model.train(4, 10, 512, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 1024, 256, 4096, 1, 1, 512)
model.train(4, 1, 1024, 256, 4096, 1, 1, 512)
model.train(4, 2, 1024, 256, 4096, 1, 1, 512)
model.train(4, 5, 1024, 256, 4096, 1, 1, 512)
model.train(4, 10, 1024, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 2048, 256, 4096, 1, 1, 512)
model.train(4, 1, 2048, 256, 4096, 1, 1, 512)
model.train(4, 2, 2048, 256, 4096, 1, 1, 512)
model.train(4, 5, 2048, 256, 4096, 1, 1, 512)
model.train(4, 10, 2048, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 4096, 256, 4096, 1, 1, 512)
model.train(4, 1, 4096, 256, 4096, 1, 1, 512)
model.train(4, 2, 4096, 256, 4096, 1, 1, 512)
model.train(4, 5, 4096, 256, 4096, 1, 1, 512)
model.train(4, 10, 4096, 256, 4096, 1, 1, 512)
model.train(4, 0.001, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.002, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.005, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.01, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.02, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.05, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.1, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.2, 8192, 256, 4096, 1, 1, 512)
model.train(4, 0.5, 8192, 256, 4096, 1, 1, 512)
model.train(4, 1, 8192, 256, 4096, 1, 1, 512)
model.train(4, 2, 8192, 256, 4096, 1, 1, 512)
model.train(4, 5, 8192, 256, 4096, 1, 1, 512)
model.train(4, 10, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 1, 4096, 1, 1, 512)
model.train(0, 1, 1, 1, 4096, 1, 1, 512)
model.train(0, 2, 1, 1, 4096, 1, 1, 512)
model.train(0, 5, 1, 1, 4096, 1, 1, 512)
model.train(0, 10, 1, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 1, 4096, 1, 1, 512)
model.train(0, 1, 2, 1, 4096, 1, 1, 512)
model.train(0, 2, 2, 1, 4096, 1, 1, 512)
model.train(0, 5, 2, 1, 4096, 1, 1, 512)
model.train(0, 10, 2, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 1, 4096, 1, 1, 512)
model.train(0, 1, 4, 1, 4096, 1, 1, 512)
model.train(0, 2, 4, 1, 4096, 1, 1, 512)
model.train(0, 5, 4, 1, 4096, 1, 1, 512)
model.train(0, 10, 4, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 1, 4096, 1, 1, 512)
model.train(0, 1, 8, 1, 4096, 1, 1, 512)
model.train(0, 2, 8, 1, 4096, 1, 1, 512)
model.train(0, 5, 8, 1, 4096, 1, 1, 512)
model.train(0, 10, 8, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 1, 4096, 1, 1, 512)
model.train(0, 1, 16, 1, 4096, 1, 1, 512)
model.train(0, 2, 16, 1, 4096, 1, 1, 512)
model.train(0, 5, 16, 1, 4096, 1, 1, 512)
model.train(0, 10, 16, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 1, 4096, 1, 1, 512)
model.train(0, 1, 32, 1, 4096, 1, 1, 512)
model.train(0, 2, 32, 1, 4096, 1, 1, 512)
model.train(0, 5, 32, 1, 4096, 1, 1, 512)
model.train(0, 10, 32, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 1, 4096, 1, 1, 512)
model.train(0, 1, 64, 1, 4096, 1, 1, 512)
model.train(0, 2, 64, 1, 4096, 1, 1, 512)
model.train(0, 5, 64, 1, 4096, 1, 1, 512)
model.train(0, 10, 64, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 1, 4096, 1, 1, 512)
model.train(0, 1, 128, 1, 4096, 1, 1, 512)
model.train(0, 2, 128, 1, 4096, 1, 1, 512)
model.train(0, 5, 128, 1, 4096, 1, 1, 512)
model.train(0, 10, 128, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 1, 4096, 1, 1, 512)
model.train(0, 1, 256, 1, 4096, 1, 1, 512)
model.train(0, 2, 256, 1, 4096, 1, 1, 512)
model.train(0, 5, 256, 1, 4096, 1, 1, 512)
model.train(0, 10, 256, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 1, 4096, 1, 1, 512)
model.train(0, 1, 512, 1, 4096, 1, 1, 512)
model.train(0, 2, 512, 1, 4096, 1, 1, 512)
model.train(0, 5, 512, 1, 4096, 1, 1, 512)
model.train(0, 10, 512, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 1, 4096, 1, 1, 512)
model.train(0, 1, 1024, 1, 4096, 1, 1, 512)
model.train(0, 2, 1024, 1, 4096, 1, 1, 512)
model.train(0, 5, 1024, 1, 4096, 1, 1, 512)
model.train(0, 10, 1024, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 1, 4096, 1, 1, 512)
model.train(0, 1, 2048, 1, 4096, 1, 1, 512)
model.train(0, 2, 2048, 1, 4096, 1, 1, 512)
model.train(0, 5, 2048, 1, 4096, 1, 1, 512)
model.train(0, 10, 2048, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 1, 4096, 1, 1, 512)
model.train(0, 1, 4096, 1, 4096, 1, 1, 512)
model.train(0, 2, 4096, 1, 4096, 1, 1, 512)
model.train(0, 5, 4096, 1, 4096, 1, 1, 512)
model.train(0, 10, 4096, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 1, 4096, 1, 1, 512)
model.train(0, 1, 8192, 1, 4096, 1, 1, 512)
model.train(0, 2, 8192, 1, 4096, 1, 1, 512)
model.train(0, 5, 8192, 1, 4096, 1, 1, 512)
model.train(0, 10, 8192, 1, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 2, 4096, 1, 1, 512)
model.train(0, 1, 1, 2, 4096, 1, 1, 512)
model.train(0, 2, 1, 2, 4096, 1, 1, 512)
model.train(0, 5, 1, 2, 4096, 1, 1, 512)
model.train(0, 10, 1, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 2, 4096, 1, 1, 512)
model.train(0, 1, 2, 2, 4096, 1, 1, 512)
model.train(0, 2, 2, 2, 4096, 1, 1, 512)
model.train(0, 5, 2, 2, 4096, 1, 1, 512)
model.train(0, 10, 2, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 2, 4096, 1, 1, 512)
model.train(0, 1, 4, 2, 4096, 1, 1, 512)
model.train(0, 2, 4, 2, 4096, 1, 1, 512)
model.train(0, 5, 4, 2, 4096, 1, 1, 512)
model.train(0, 10, 4, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 2, 4096, 1, 1, 512)
model.train(0, 1, 8, 2, 4096, 1, 1, 512)
model.train(0, 2, 8, 2, 4096, 1, 1, 512)
model.train(0, 5, 8, 2, 4096, 1, 1, 512)
model.train(0, 10, 8, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 2, 4096, 1, 1, 512)
model.train(0, 1, 16, 2, 4096, 1, 1, 512)
model.train(0, 2, 16, 2, 4096, 1, 1, 512)
model.train(0, 5, 16, 2, 4096, 1, 1, 512)
model.train(0, 10, 16, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 2, 4096, 1, 1, 512)
model.train(0, 1, 32, 2, 4096, 1, 1, 512)
model.train(0, 2, 32, 2, 4096, 1, 1, 512)
model.train(0, 5, 32, 2, 4096, 1, 1, 512)
model.train(0, 10, 32, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 2, 4096, 1, 1, 512)
model.train(0, 1, 64, 2, 4096, 1, 1, 512)
model.train(0, 2, 64, 2, 4096, 1, 1, 512)
model.train(0, 5, 64, 2, 4096, 1, 1, 512)
model.train(0, 10, 64, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 2, 4096, 1, 1, 512)
model.train(0, 1, 128, 2, 4096, 1, 1, 512)
model.train(0, 2, 128, 2, 4096, 1, 1, 512)
model.train(0, 5, 128, 2, 4096, 1, 1, 512)
model.train(0, 10, 128, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 2, 4096, 1, 1, 512)
model.train(0, 1, 256, 2, 4096, 1, 1, 512)
model.train(0, 2, 256, 2, 4096, 1, 1, 512)
model.train(0, 5, 256, 2, 4096, 1, 1, 512)
model.train(0, 10, 256, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 2, 4096, 1, 1, 512)
model.train(0, 1, 512, 2, 4096, 1, 1, 512)
model.train(0, 2, 512, 2, 4096, 1, 1, 512)
model.train(0, 5, 512, 2, 4096, 1, 1, 512)
model.train(0, 10, 512, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 2, 4096, 1, 1, 512)
model.train(0, 1, 1024, 2, 4096, 1, 1, 512)
model.train(0, 2, 1024, 2, 4096, 1, 1, 512)
model.train(0, 5, 1024, 2, 4096, 1, 1, 512)
model.train(0, 10, 1024, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 2, 4096, 1, 1, 512)
model.train(0, 1, 2048, 2, 4096, 1, 1, 512)
model.train(0, 2, 2048, 2, 4096, 1, 1, 512)
model.train(0, 5, 2048, 2, 4096, 1, 1, 512)
model.train(0, 10, 2048, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 2, 4096, 1, 1, 512)
model.train(0, 1, 4096, 2, 4096, 1, 1, 512)
model.train(0, 2, 4096, 2, 4096, 1, 1, 512)
model.train(0, 5, 4096, 2, 4096, 1, 1, 512)
model.train(0, 10, 4096, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 2, 4096, 1, 1, 512)
model.train(0, 1, 8192, 2, 4096, 1, 1, 512)
model.train(0, 2, 8192, 2, 4096, 1, 1, 512)
model.train(0, 5, 8192, 2, 4096, 1, 1, 512)
model.train(0, 10, 8192, 2, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 4, 4096, 1, 1, 512)
model.train(0, 1, 1, 4, 4096, 1, 1, 512)
model.train(0, 2, 1, 4, 4096, 1, 1, 512)
model.train(0, 5, 1, 4, 4096, 1, 1, 512)
model.train(0, 10, 1, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 4, 4096, 1, 1, 512)
model.train(0, 1, 2, 4, 4096, 1, 1, 512)
model.train(0, 2, 2, 4, 4096, 1, 1, 512)
model.train(0, 5, 2, 4, 4096, 1, 1, 512)
model.train(0, 10, 2, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 4, 4096, 1, 1, 512)
model.train(0, 1, 4, 4, 4096, 1, 1, 512)
model.train(0, 2, 4, 4, 4096, 1, 1, 512)
model.train(0, 5, 4, 4, 4096, 1, 1, 512)
model.train(0, 10, 4, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 4, 4096, 1, 1, 512)
model.train(0, 1, 8, 4, 4096, 1, 1, 512)
model.train(0, 2, 8, 4, 4096, 1, 1, 512)
model.train(0, 5, 8, 4, 4096, 1, 1, 512)
model.train(0, 10, 8, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 4, 4096, 1, 1, 512)
model.train(0, 1, 16, 4, 4096, 1, 1, 512)
model.train(0, 2, 16, 4, 4096, 1, 1, 512)
model.train(0, 5, 16, 4, 4096, 1, 1, 512)
model.train(0, 10, 16, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 4, 4096, 1, 1, 512)
model.train(0, 1, 32, 4, 4096, 1, 1, 512)
model.train(0, 2, 32, 4, 4096, 1, 1, 512)
model.train(0, 5, 32, 4, 4096, 1, 1, 512)
model.train(0, 10, 32, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 4, 4096, 1, 1, 512)
model.train(0, 1, 64, 4, 4096, 1, 1, 512)
model.train(0, 2, 64, 4, 4096, 1, 1, 512)
model.train(0, 5, 64, 4, 4096, 1, 1, 512)
model.train(0, 10, 64, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 4, 4096, 1, 1, 512)
model.train(0, 1, 128, 4, 4096, 1, 1, 512)
model.train(0, 2, 128, 4, 4096, 1, 1, 512)
model.train(0, 5, 128, 4, 4096, 1, 1, 512)
model.train(0, 10, 128, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 4, 4096, 1, 1, 512)
model.train(0, 1, 256, 4, 4096, 1, 1, 512)
model.train(0, 2, 256, 4, 4096, 1, 1, 512)
model.train(0, 5, 256, 4, 4096, 1, 1, 512)
model.train(0, 10, 256, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 4, 4096, 1, 1, 512)
model.train(0, 1, 512, 4, 4096, 1, 1, 512)
model.train(0, 2, 512, 4, 4096, 1, 1, 512)
model.train(0, 5, 512, 4, 4096, 1, 1, 512)
model.train(0, 10, 512, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 4, 4096, 1, 1, 512)
model.train(0, 1, 1024, 4, 4096, 1, 1, 512)
model.train(0, 2, 1024, 4, 4096, 1, 1, 512)
model.train(0, 5, 1024, 4, 4096, 1, 1, 512)
model.train(0, 10, 1024, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 4, 4096, 1, 1, 512)
model.train(0, 1, 2048, 4, 4096, 1, 1, 512)
model.train(0, 2, 2048, 4, 4096, 1, 1, 512)
model.train(0, 5, 2048, 4, 4096, 1, 1, 512)
model.train(0, 10, 2048, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 4, 4096, 1, 1, 512)
model.train(0, 1, 4096, 4, 4096, 1, 1, 512)
model.train(0, 2, 4096, 4, 4096, 1, 1, 512)
model.train(0, 5, 4096, 4, 4096, 1, 1, 512)
model.train(0, 10, 4096, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 4, 4096, 1, 1, 512)
model.train(0, 1, 8192, 4, 4096, 1, 1, 512)
model.train(0, 2, 8192, 4, 4096, 1, 1, 512)
model.train(0, 5, 8192, 4, 4096, 1, 1, 512)
model.train(0, 10, 8192, 4, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 8, 4096, 1, 1, 512)
model.train(0, 1, 1, 8, 4096, 1, 1, 512)
model.train(0, 2, 1, 8, 4096, 1, 1, 512)
model.train(0, 5, 1, 8, 4096, 1, 1, 512)
model.train(0, 10, 1, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 8, 4096, 1, 1, 512)
model.train(0, 1, 2, 8, 4096, 1, 1, 512)
model.train(0, 2, 2, 8, 4096, 1, 1, 512)
model.train(0, 5, 2, 8, 4096, 1, 1, 512)
model.train(0, 10, 2, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 8, 4096, 1, 1, 512)
model.train(0, 1, 4, 8, 4096, 1, 1, 512)
model.train(0, 2, 4, 8, 4096, 1, 1, 512)
model.train(0, 5, 4, 8, 4096, 1, 1, 512)
model.train(0, 10, 4, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 8, 4096, 1, 1, 512)
model.train(0, 1, 8, 8, 4096, 1, 1, 512)
model.train(0, 2, 8, 8, 4096, 1, 1, 512)
model.train(0, 5, 8, 8, 4096, 1, 1, 512)
model.train(0, 10, 8, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 8, 4096, 1, 1, 512)
model.train(0, 1, 16, 8, 4096, 1, 1, 512)
model.train(0, 2, 16, 8, 4096, 1, 1, 512)
model.train(0, 5, 16, 8, 4096, 1, 1, 512)
model.train(0, 10, 16, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 8, 4096, 1, 1, 512)
model.train(0, 1, 32, 8, 4096, 1, 1, 512)
model.train(0, 2, 32, 8, 4096, 1, 1, 512)
model.train(0, 5, 32, 8, 4096, 1, 1, 512)
model.train(0, 10, 32, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 8, 4096, 1, 1, 512)
model.train(0, 1, 64, 8, 4096, 1, 1, 512)
model.train(0, 2, 64, 8, 4096, 1, 1, 512)
model.train(0, 5, 64, 8, 4096, 1, 1, 512)
model.train(0, 10, 64, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 8, 4096, 1, 1, 512)
model.train(0, 1, 128, 8, 4096, 1, 1, 512)
model.train(0, 2, 128, 8, 4096, 1, 1, 512)
model.train(0, 5, 128, 8, 4096, 1, 1, 512)
model.train(0, 10, 128, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 8, 4096, 1, 1, 512)
model.train(0, 1, 256, 8, 4096, 1, 1, 512)
model.train(0, 2, 256, 8, 4096, 1, 1, 512)
model.train(0, 5, 256, 8, 4096, 1, 1, 512)
model.train(0, 10, 256, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 8, 4096, 1, 1, 512)
model.train(0, 1, 512, 8, 4096, 1, 1, 512)
model.train(0, 2, 512, 8, 4096, 1, 1, 512)
model.train(0, 5, 512, 8, 4096, 1, 1, 512)
model.train(0, 10, 512, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 8, 4096, 1, 1, 512)
model.train(0, 1, 1024, 8, 4096, 1, 1, 512)
model.train(0, 2, 1024, 8, 4096, 1, 1, 512)
model.train(0, 5, 1024, 8, 4096, 1, 1, 512)
model.train(0, 10, 1024, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 8, 4096, 1, 1, 512)
model.train(0, 1, 2048, 8, 4096, 1, 1, 512)
model.train(0, 2, 2048, 8, 4096, 1, 1, 512)
model.train(0, 5, 2048, 8, 4096, 1, 1, 512)
model.train(0, 10, 2048, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 8, 4096, 1, 1, 512)
model.train(0, 1, 4096, 8, 4096, 1, 1, 512)
model.train(0, 2, 4096, 8, 4096, 1, 1, 512)
model.train(0, 5, 4096, 8, 4096, 1, 1, 512)
model.train(0, 10, 4096, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 8, 4096, 1, 1, 512)
model.train(0, 1, 8192, 8, 4096, 1, 1, 512)
model.train(0, 2, 8192, 8, 4096, 1, 1, 512)
model.train(0, 5, 8192, 8, 4096, 1, 1, 512)
model.train(0, 10, 8192, 8, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 16, 4096, 1, 1, 512)
model.train(0, 1, 1, 16, 4096, 1, 1, 512)
model.train(0, 2, 1, 16, 4096, 1, 1, 512)
model.train(0, 5, 1, 16, 4096, 1, 1, 512)
model.train(0, 10, 1, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 16, 4096, 1, 1, 512)
model.train(0, 1, 2, 16, 4096, 1, 1, 512)
model.train(0, 2, 2, 16, 4096, 1, 1, 512)
model.train(0, 5, 2, 16, 4096, 1, 1, 512)
model.train(0, 10, 2, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 16, 4096, 1, 1, 512)
model.train(0, 1, 4, 16, 4096, 1, 1, 512)
model.train(0, 2, 4, 16, 4096, 1, 1, 512)
model.train(0, 5, 4, 16, 4096, 1, 1, 512)
model.train(0, 10, 4, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 16, 4096, 1, 1, 512)
model.train(0, 1, 8, 16, 4096, 1, 1, 512)
model.train(0, 2, 8, 16, 4096, 1, 1, 512)
model.train(0, 5, 8, 16, 4096, 1, 1, 512)
model.train(0, 10, 8, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 16, 4096, 1, 1, 512)
model.train(0, 1, 16, 16, 4096, 1, 1, 512)
model.train(0, 2, 16, 16, 4096, 1, 1, 512)
model.train(0, 5, 16, 16, 4096, 1, 1, 512)
model.train(0, 10, 16, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 16, 4096, 1, 1, 512)
model.train(0, 1, 32, 16, 4096, 1, 1, 512)
model.train(0, 2, 32, 16, 4096, 1, 1, 512)
model.train(0, 5, 32, 16, 4096, 1, 1, 512)
model.train(0, 10, 32, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 16, 4096, 1, 1, 512)
model.train(0, 1, 64, 16, 4096, 1, 1, 512)
model.train(0, 2, 64, 16, 4096, 1, 1, 512)
model.train(0, 5, 64, 16, 4096, 1, 1, 512)
model.train(0, 10, 64, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 16, 4096, 1, 1, 512)
model.train(0, 1, 128, 16, 4096, 1, 1, 512)
model.train(0, 2, 128, 16, 4096, 1, 1, 512)
model.train(0, 5, 128, 16, 4096, 1, 1, 512)
model.train(0, 10, 128, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 16, 4096, 1, 1, 512)
model.train(0, 1, 256, 16, 4096, 1, 1, 512)
model.train(0, 2, 256, 16, 4096, 1, 1, 512)
model.train(0, 5, 256, 16, 4096, 1, 1, 512)
model.train(0, 10, 256, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 16, 4096, 1, 1, 512)
model.train(0, 1, 512, 16, 4096, 1, 1, 512)
model.train(0, 2, 512, 16, 4096, 1, 1, 512)
model.train(0, 5, 512, 16, 4096, 1, 1, 512)
model.train(0, 10, 512, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 16, 4096, 1, 1, 512)
model.train(0, 1, 1024, 16, 4096, 1, 1, 512)
model.train(0, 2, 1024, 16, 4096, 1, 1, 512)
model.train(0, 5, 1024, 16, 4096, 1, 1, 512)
model.train(0, 10, 1024, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 16, 4096, 1, 1, 512)
model.train(0, 1, 2048, 16, 4096, 1, 1, 512)
model.train(0, 2, 2048, 16, 4096, 1, 1, 512)
model.train(0, 5, 2048, 16, 4096, 1, 1, 512)
model.train(0, 10, 2048, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 16, 4096, 1, 1, 512)
model.train(0, 1, 4096, 16, 4096, 1, 1, 512)
model.train(0, 2, 4096, 16, 4096, 1, 1, 512)
model.train(0, 5, 4096, 16, 4096, 1, 1, 512)
model.train(0, 10, 4096, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 16, 4096, 1, 1, 512)
model.train(0, 1, 8192, 16, 4096, 1, 1, 512)
model.train(0, 2, 8192, 16, 4096, 1, 1, 512)
model.train(0, 5, 8192, 16, 4096, 1, 1, 512)
model.train(0, 10, 8192, 16, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 32, 4096, 1, 1, 512)
model.train(0, 1, 1, 32, 4096, 1, 1, 512)
model.train(0, 2, 1, 32, 4096, 1, 1, 512)
model.train(0, 5, 1, 32, 4096, 1, 1, 512)
model.train(0, 10, 1, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 32, 4096, 1, 1, 512)
model.train(0, 1, 2, 32, 4096, 1, 1, 512)
model.train(0, 2, 2, 32, 4096, 1, 1, 512)
model.train(0, 5, 2, 32, 4096, 1, 1, 512)
model.train(0, 10, 2, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 32, 4096, 1, 1, 512)
model.train(0, 1, 4, 32, 4096, 1, 1, 512)
model.train(0, 2, 4, 32, 4096, 1, 1, 512)
model.train(0, 5, 4, 32, 4096, 1, 1, 512)
model.train(0, 10, 4, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 32, 4096, 1, 1, 512)
model.train(0, 1, 8, 32, 4096, 1, 1, 512)
model.train(0, 2, 8, 32, 4096, 1, 1, 512)
model.train(0, 5, 8, 32, 4096, 1, 1, 512)
model.train(0, 10, 8, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 32, 4096, 1, 1, 512)
model.train(0, 1, 16, 32, 4096, 1, 1, 512)
model.train(0, 2, 16, 32, 4096, 1, 1, 512)
model.train(0, 5, 16, 32, 4096, 1, 1, 512)
model.train(0, 10, 16, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 32, 4096, 1, 1, 512)
model.train(0, 1, 32, 32, 4096, 1, 1, 512)
model.train(0, 2, 32, 32, 4096, 1, 1, 512)
model.train(0, 5, 32, 32, 4096, 1, 1, 512)
model.train(0, 10, 32, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 32, 4096, 1, 1, 512)
model.train(0, 1, 64, 32, 4096, 1, 1, 512)
model.train(0, 2, 64, 32, 4096, 1, 1, 512)
model.train(0, 5, 64, 32, 4096, 1, 1, 512)
model.train(0, 10, 64, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 32, 4096, 1, 1, 512)
model.train(0, 1, 128, 32, 4096, 1, 1, 512)
model.train(0, 2, 128, 32, 4096, 1, 1, 512)
model.train(0, 5, 128, 32, 4096, 1, 1, 512)
model.train(0, 10, 128, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 32, 4096, 1, 1, 512)
model.train(0, 1, 256, 32, 4096, 1, 1, 512)
model.train(0, 2, 256, 32, 4096, 1, 1, 512)
model.train(0, 5, 256, 32, 4096, 1, 1, 512)
model.train(0, 10, 256, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 32, 4096, 1, 1, 512)
model.train(0, 1, 512, 32, 4096, 1, 1, 512)
model.train(0, 2, 512, 32, 4096, 1, 1, 512)
model.train(0, 5, 512, 32, 4096, 1, 1, 512)
model.train(0, 10, 512, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 32, 4096, 1, 1, 512)
model.train(0, 1, 1024, 32, 4096, 1, 1, 512)
model.train(0, 2, 1024, 32, 4096, 1, 1, 512)
model.train(0, 5, 1024, 32, 4096, 1, 1, 512)
model.train(0, 10, 1024, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 32, 4096, 1, 1, 512)
model.train(0, 1, 2048, 32, 4096, 1, 1, 512)
model.train(0, 2, 2048, 32, 4096, 1, 1, 512)
model.train(0, 5, 2048, 32, 4096, 1, 1, 512)
model.train(0, 10, 2048, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 32, 4096, 1, 1, 512)
model.train(0, 1, 4096, 32, 4096, 1, 1, 512)
model.train(0, 2, 4096, 32, 4096, 1, 1, 512)
model.train(0, 5, 4096, 32, 4096, 1, 1, 512)
model.train(0, 10, 4096, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 32, 4096, 1, 1, 512)
model.train(0, 1, 8192, 32, 4096, 1, 1, 512)
model.train(0, 2, 8192, 32, 4096, 1, 1, 512)
model.train(0, 5, 8192, 32, 4096, 1, 1, 512)
model.train(0, 10, 8192, 32, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 64, 4096, 1, 1, 512)
model.train(0, 1, 1, 64, 4096, 1, 1, 512)
model.train(0, 2, 1, 64, 4096, 1, 1, 512)
model.train(0, 5, 1, 64, 4096, 1, 1, 512)
model.train(0, 10, 1, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 64, 4096, 1, 1, 512)
model.train(0, 1, 2, 64, 4096, 1, 1, 512)
model.train(0, 2, 2, 64, 4096, 1, 1, 512)
model.train(0, 5, 2, 64, 4096, 1, 1, 512)
model.train(0, 10, 2, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 64, 4096, 1, 1, 512)
model.train(0, 1, 4, 64, 4096, 1, 1, 512)
model.train(0, 2, 4, 64, 4096, 1, 1, 512)
model.train(0, 5, 4, 64, 4096, 1, 1, 512)
model.train(0, 10, 4, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 64, 4096, 1, 1, 512)
model.train(0, 1, 8, 64, 4096, 1, 1, 512)
model.train(0, 2, 8, 64, 4096, 1, 1, 512)
model.train(0, 5, 8, 64, 4096, 1, 1, 512)
model.train(0, 10, 8, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 64, 4096, 1, 1, 512)
model.train(0, 1, 16, 64, 4096, 1, 1, 512)
model.train(0, 2, 16, 64, 4096, 1, 1, 512)
model.train(0, 5, 16, 64, 4096, 1, 1, 512)
model.train(0, 10, 16, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 64, 4096, 1, 1, 512)
model.train(0, 1, 32, 64, 4096, 1, 1, 512)
model.train(0, 2, 32, 64, 4096, 1, 1, 512)
model.train(0, 5, 32, 64, 4096, 1, 1, 512)
model.train(0, 10, 32, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 64, 4096, 1, 1, 512)
model.train(0, 1, 64, 64, 4096, 1, 1, 512)
model.train(0, 2, 64, 64, 4096, 1, 1, 512)
model.train(0, 5, 64, 64, 4096, 1, 1, 512)
model.train(0, 10, 64, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 64, 4096, 1, 1, 512)
model.train(0, 1, 128, 64, 4096, 1, 1, 512)
model.train(0, 2, 128, 64, 4096, 1, 1, 512)
model.train(0, 5, 128, 64, 4096, 1, 1, 512)
model.train(0, 10, 128, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 64, 4096, 1, 1, 512)
model.train(0, 1, 256, 64, 4096, 1, 1, 512)
model.train(0, 2, 256, 64, 4096, 1, 1, 512)
model.train(0, 5, 256, 64, 4096, 1, 1, 512)
model.train(0, 10, 256, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 64, 4096, 1, 1, 512)
model.train(0, 1, 512, 64, 4096, 1, 1, 512)
model.train(0, 2, 512, 64, 4096, 1, 1, 512)
model.train(0, 5, 512, 64, 4096, 1, 1, 512)
model.train(0, 10, 512, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 64, 4096, 1, 1, 512)
model.train(0, 1, 1024, 64, 4096, 1, 1, 512)
model.train(0, 2, 1024, 64, 4096, 1, 1, 512)
model.train(0, 5, 1024, 64, 4096, 1, 1, 512)
model.train(0, 10, 1024, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 64, 4096, 1, 1, 512)
model.train(0, 1, 2048, 64, 4096, 1, 1, 512)
model.train(0, 2, 2048, 64, 4096, 1, 1, 512)
model.train(0, 5, 2048, 64, 4096, 1, 1, 512)
model.train(0, 10, 2048, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 64, 4096, 1, 1, 512)
model.train(0, 1, 4096, 64, 4096, 1, 1, 512)
model.train(0, 2, 4096, 64, 4096, 1, 1, 512)
model.train(0, 5, 4096, 64, 4096, 1, 1, 512)
model.train(0, 10, 4096, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 64, 4096, 1, 1, 512)
model.train(0, 1, 8192, 64, 4096, 1, 1, 512)
model.train(0, 2, 8192, 64, 4096, 1, 1, 512)
model.train(0, 5, 8192, 64, 4096, 1, 1, 512)
model.train(0, 10, 8192, 64, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 128, 4096, 1, 1, 512)
model.train(0, 1, 1, 128, 4096, 1, 1, 512)
model.train(0, 2, 1, 128, 4096, 1, 1, 512)
model.train(0, 5, 1, 128, 4096, 1, 1, 512)
model.train(0, 10, 1, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 128, 4096, 1, 1, 512)
model.train(0, 1, 2, 128, 4096, 1, 1, 512)
model.train(0, 2, 2, 128, 4096, 1, 1, 512)
model.train(0, 5, 2, 128, 4096, 1, 1, 512)
model.train(0, 10, 2, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 128, 4096, 1, 1, 512)
model.train(0, 1, 4, 128, 4096, 1, 1, 512)
model.train(0, 2, 4, 128, 4096, 1, 1, 512)
model.train(0, 5, 4, 128, 4096, 1, 1, 512)
model.train(0, 10, 4, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 128, 4096, 1, 1, 512)
model.train(0, 1, 8, 128, 4096, 1, 1, 512)
model.train(0, 2, 8, 128, 4096, 1, 1, 512)
model.train(0, 5, 8, 128, 4096, 1, 1, 512)
model.train(0, 10, 8, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 128, 4096, 1, 1, 512)
model.train(0, 1, 16, 128, 4096, 1, 1, 512)
model.train(0, 2, 16, 128, 4096, 1, 1, 512)
model.train(0, 5, 16, 128, 4096, 1, 1, 512)
model.train(0, 10, 16, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 128, 4096, 1, 1, 512)
model.train(0, 1, 32, 128, 4096, 1, 1, 512)
model.train(0, 2, 32, 128, 4096, 1, 1, 512)
model.train(0, 5, 32, 128, 4096, 1, 1, 512)
model.train(0, 10, 32, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 128, 4096, 1, 1, 512)
model.train(0, 1, 64, 128, 4096, 1, 1, 512)
model.train(0, 2, 64, 128, 4096, 1, 1, 512)
model.train(0, 5, 64, 128, 4096, 1, 1, 512)
model.train(0, 10, 64, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 128, 4096, 1, 1, 512)
model.train(0, 1, 128, 128, 4096, 1, 1, 512)
model.train(0, 2, 128, 128, 4096, 1, 1, 512)
model.train(0, 5, 128, 128, 4096, 1, 1, 512)
model.train(0, 10, 128, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 128, 4096, 1, 1, 512)
model.train(0, 1, 256, 128, 4096, 1, 1, 512)
model.train(0, 2, 256, 128, 4096, 1, 1, 512)
model.train(0, 5, 256, 128, 4096, 1, 1, 512)
model.train(0, 10, 256, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 128, 4096, 1, 1, 512)
model.train(0, 1, 512, 128, 4096, 1, 1, 512)
model.train(0, 2, 512, 128, 4096, 1, 1, 512)
model.train(0, 5, 512, 128, 4096, 1, 1, 512)
model.train(0, 10, 512, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 128, 4096, 1, 1, 512)
model.train(0, 1, 1024, 128, 4096, 1, 1, 512)
model.train(0, 2, 1024, 128, 4096, 1, 1, 512)
model.train(0, 5, 1024, 128, 4096, 1, 1, 512)
model.train(0, 10, 1024, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 128, 4096, 1, 1, 512)
model.train(0, 1, 2048, 128, 4096, 1, 1, 512)
model.train(0, 2, 2048, 128, 4096, 1, 1, 512)
model.train(0, 5, 2048, 128, 4096, 1, 1, 512)
model.train(0, 10, 2048, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 128, 4096, 1, 1, 512)
model.train(0, 1, 4096, 128, 4096, 1, 1, 512)
model.train(0, 2, 4096, 128, 4096, 1, 1, 512)
model.train(0, 5, 4096, 128, 4096, 1, 1, 512)
model.train(0, 10, 4096, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 128, 4096, 1, 1, 512)
model.train(0, 1, 8192, 128, 4096, 1, 1, 512)
model.train(0, 2, 8192, 128, 4096, 1, 1, 512)
model.train(0, 5, 8192, 128, 4096, 1, 1, 512)
model.train(0, 10, 8192, 128, 4096, 1, 1, 512)
model.train(0, 0.001, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 1, 256, 4096, 1, 1, 512)
model.train(0, 1, 1, 256, 4096, 1, 1, 512)
model.train(0, 2, 1, 256, 4096, 1, 1, 512)
model.train(0, 5, 1, 256, 4096, 1, 1, 512)
model.train(0, 10, 1, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 2, 256, 4096, 1, 1, 512)
model.train(0, 1, 2, 256, 4096, 1, 1, 512)
model.train(0, 2, 2, 256, 4096, 1, 1, 512)
model.train(0, 5, 2, 256, 4096, 1, 1, 512)
model.train(0, 10, 2, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 4, 256, 4096, 1, 1, 512)
model.train(0, 1, 4, 256, 4096, 1, 1, 512)
model.train(0, 2, 4, 256, 4096, 1, 1, 512)
model.train(0, 5, 4, 256, 4096, 1, 1, 512)
model.train(0, 10, 4, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 8, 256, 4096, 1, 1, 512)
model.train(0, 1, 8, 256, 4096, 1, 1, 512)
model.train(0, 2, 8, 256, 4096, 1, 1, 512)
model.train(0, 5, 8, 256, 4096, 1, 1, 512)
model.train(0, 10, 8, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 16, 256, 4096, 1, 1, 512)
model.train(0, 1, 16, 256, 4096, 1, 1, 512)
model.train(0, 2, 16, 256, 4096, 1, 1, 512)
model.train(0, 5, 16, 256, 4096, 1, 1, 512)
model.train(0, 10, 16, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 32, 256, 4096, 1, 1, 512)
model.train(0, 1, 32, 256, 4096, 1, 1, 512)
model.train(0, 2, 32, 256, 4096, 1, 1, 512)
model.train(0, 5, 32, 256, 4096, 1, 1, 512)
model.train(0, 10, 32, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 64, 256, 4096, 1, 1, 512)
model.train(0, 1, 64, 256, 4096, 1, 1, 512)
model.train(0, 2, 64, 256, 4096, 1, 1, 512)
model.train(0, 5, 64, 256, 4096, 1, 1, 512)
model.train(0, 10, 64, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 128, 256, 4096, 1, 1, 512)
model.train(0, 1, 128, 256, 4096, 1, 1, 512)
model.train(0, 2, 128, 256, 4096, 1, 1, 512)
model.train(0, 5, 128, 256, 4096, 1, 1, 512)
model.train(0, 10, 128, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 256, 256, 4096, 1, 1, 512)
model.train(0, 1, 256, 256, 4096, 1, 1, 512)
model.train(0, 2, 256, 256, 4096, 1, 1, 512)
model.train(0, 5, 256, 256, 4096, 1, 1, 512)
model.train(0, 10, 256, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 512, 256, 4096, 1, 1, 512)
model.train(0, 1, 512, 256, 4096, 1, 1, 512)
model.train(0, 2, 512, 256, 4096, 1, 1, 512)
model.train(0, 5, 512, 256, 4096, 1, 1, 512)
model.train(0, 10, 512, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 1024, 256, 4096, 1, 1, 512)
model.train(0, 1, 1024, 256, 4096, 1, 1, 512)
model.train(0, 2, 1024, 256, 4096, 1, 1, 512)
model.train(0, 5, 1024, 256, 4096, 1, 1, 512)
model.train(0, 10, 1024, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 2048, 256, 4096, 1, 1, 512)
model.train(0, 1, 2048, 256, 4096, 1, 1, 512)
model.train(0, 2, 2048, 256, 4096, 1, 1, 512)
model.train(0, 5, 2048, 256, 4096, 1, 1, 512)
model.train(0, 10, 2048, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 4096, 256, 4096, 1, 1, 512)
model.train(0, 1, 4096, 256, 4096, 1, 1, 512)
model.train(0, 2, 4096, 256, 4096, 1, 1, 512)
model.train(0, 5, 4096, 256, 4096, 1, 1, 512)
model.train(0, 10, 4096, 256, 4096, 1, 1, 512)
model.train(0, 0.001, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.002, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.005, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.01, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.02, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.05, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.1, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.2, 8192, 256, 4096, 1, 1, 512)
model.train(0, 0.5, 8192, 256, 4096, 1, 1, 512)
model.train(0, 1, 8192, 256, 4096, 1, 1, 512)
model.train(0, 2, 8192, 256, 4096, 1, 1, 512)
model.train(0, 5, 8192, 256, 4096, 1, 1, 512)
model.train(0, 10, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 1, 4096, 1, 1, 512)
model.train(1, 1, 1, 1, 4096, 1, 1, 512)
model.train(1, 2, 1, 1, 4096, 1, 1, 512)
model.train(1, 5, 1, 1, 4096, 1, 1, 512)
model.train(1, 10, 1, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 1, 4096, 1, 1, 512)
model.train(1, 1, 2, 1, 4096, 1, 1, 512)
model.train(1, 2, 2, 1, 4096, 1, 1, 512)
model.train(1, 5, 2, 1, 4096, 1, 1, 512)
model.train(1, 10, 2, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 1, 4096, 1, 1, 512)
model.train(1, 1, 4, 1, 4096, 1, 1, 512)
model.train(1, 2, 4, 1, 4096, 1, 1, 512)
model.train(1, 5, 4, 1, 4096, 1, 1, 512)
model.train(1, 10, 4, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 1, 4096, 1, 1, 512)
model.train(1, 1, 8, 1, 4096, 1, 1, 512)
model.train(1, 2, 8, 1, 4096, 1, 1, 512)
model.train(1, 5, 8, 1, 4096, 1, 1, 512)
model.train(1, 10, 8, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 1, 4096, 1, 1, 512)
model.train(1, 1, 16, 1, 4096, 1, 1, 512)
model.train(1, 2, 16, 1, 4096, 1, 1, 512)
model.train(1, 5, 16, 1, 4096, 1, 1, 512)
model.train(1, 10, 16, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 1, 4096, 1, 1, 512)
model.train(1, 1, 32, 1, 4096, 1, 1, 512)
model.train(1, 2, 32, 1, 4096, 1, 1, 512)
model.train(1, 5, 32, 1, 4096, 1, 1, 512)
model.train(1, 10, 32, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 1, 4096, 1, 1, 512)
model.train(1, 1, 64, 1, 4096, 1, 1, 512)
model.train(1, 2, 64, 1, 4096, 1, 1, 512)
model.train(1, 5, 64, 1, 4096, 1, 1, 512)
model.train(1, 10, 64, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 1, 4096, 1, 1, 512)
model.train(1, 1, 128, 1, 4096, 1, 1, 512)
model.train(1, 2, 128, 1, 4096, 1, 1, 512)
model.train(1, 5, 128, 1, 4096, 1, 1, 512)
model.train(1, 10, 128, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 1, 4096, 1, 1, 512)
model.train(1, 1, 256, 1, 4096, 1, 1, 512)
model.train(1, 2, 256, 1, 4096, 1, 1, 512)
model.train(1, 5, 256, 1, 4096, 1, 1, 512)
model.train(1, 10, 256, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 1, 4096, 1, 1, 512)
model.train(1, 1, 512, 1, 4096, 1, 1, 512)
model.train(1, 2, 512, 1, 4096, 1, 1, 512)
model.train(1, 5, 512, 1, 4096, 1, 1, 512)
model.train(1, 10, 512, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 1, 4096, 1, 1, 512)
model.train(1, 1, 1024, 1, 4096, 1, 1, 512)
model.train(1, 2, 1024, 1, 4096, 1, 1, 512)
model.train(1, 5, 1024, 1, 4096, 1, 1, 512)
model.train(1, 10, 1024, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 1, 4096, 1, 1, 512)
model.train(1, 1, 2048, 1, 4096, 1, 1, 512)
model.train(1, 2, 2048, 1, 4096, 1, 1, 512)
model.train(1, 5, 2048, 1, 4096, 1, 1, 512)
model.train(1, 10, 2048, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 1, 4096, 1, 1, 512)
model.train(1, 1, 4096, 1, 4096, 1, 1, 512)
model.train(1, 2, 4096, 1, 4096, 1, 1, 512)
model.train(1, 5, 4096, 1, 4096, 1, 1, 512)
model.train(1, 10, 4096, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 1, 4096, 1, 1, 512)
model.train(1, 1, 8192, 1, 4096, 1, 1, 512)
model.train(1, 2, 8192, 1, 4096, 1, 1, 512)
model.train(1, 5, 8192, 1, 4096, 1, 1, 512)
model.train(1, 10, 8192, 1, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 2, 4096, 1, 1, 512)
model.train(1, 1, 1, 2, 4096, 1, 1, 512)
model.train(1, 2, 1, 2, 4096, 1, 1, 512)
model.train(1, 5, 1, 2, 4096, 1, 1, 512)
model.train(1, 10, 1, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 2, 4096, 1, 1, 512)
model.train(1, 1, 2, 2, 4096, 1, 1, 512)
model.train(1, 2, 2, 2, 4096, 1, 1, 512)
model.train(1, 5, 2, 2, 4096, 1, 1, 512)
model.train(1, 10, 2, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 2, 4096, 1, 1, 512)
model.train(1, 1, 4, 2, 4096, 1, 1, 512)
model.train(1, 2, 4, 2, 4096, 1, 1, 512)
model.train(1, 5, 4, 2, 4096, 1, 1, 512)
model.train(1, 10, 4, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 2, 4096, 1, 1, 512)
model.train(1, 1, 8, 2, 4096, 1, 1, 512)
model.train(1, 2, 8, 2, 4096, 1, 1, 512)
model.train(1, 5, 8, 2, 4096, 1, 1, 512)
model.train(1, 10, 8, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 2, 4096, 1, 1, 512)
model.train(1, 1, 16, 2, 4096, 1, 1, 512)
model.train(1, 2, 16, 2, 4096, 1, 1, 512)
model.train(1, 5, 16, 2, 4096, 1, 1, 512)
model.train(1, 10, 16, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 2, 4096, 1, 1, 512)
model.train(1, 1, 32, 2, 4096, 1, 1, 512)
model.train(1, 2, 32, 2, 4096, 1, 1, 512)
model.train(1, 5, 32, 2, 4096, 1, 1, 512)
model.train(1, 10, 32, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 2, 4096, 1, 1, 512)
model.train(1, 1, 64, 2, 4096, 1, 1, 512)
model.train(1, 2, 64, 2, 4096, 1, 1, 512)
model.train(1, 5, 64, 2, 4096, 1, 1, 512)
model.train(1, 10, 64, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 2, 4096, 1, 1, 512)
model.train(1, 1, 128, 2, 4096, 1, 1, 512)
model.train(1, 2, 128, 2, 4096, 1, 1, 512)
model.train(1, 5, 128, 2, 4096, 1, 1, 512)
model.train(1, 10, 128, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 2, 4096, 1, 1, 512)
model.train(1, 1, 256, 2, 4096, 1, 1, 512)
model.train(1, 2, 256, 2, 4096, 1, 1, 512)
model.train(1, 5, 256, 2, 4096, 1, 1, 512)
model.train(1, 10, 256, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 2, 4096, 1, 1, 512)
model.train(1, 1, 512, 2, 4096, 1, 1, 512)
model.train(1, 2, 512, 2, 4096, 1, 1, 512)
model.train(1, 5, 512, 2, 4096, 1, 1, 512)
model.train(1, 10, 512, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 2, 4096, 1, 1, 512)
model.train(1, 1, 1024, 2, 4096, 1, 1, 512)
model.train(1, 2, 1024, 2, 4096, 1, 1, 512)
model.train(1, 5, 1024, 2, 4096, 1, 1, 512)
model.train(1, 10, 1024, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 2, 4096, 1, 1, 512)
model.train(1, 1, 2048, 2, 4096, 1, 1, 512)
model.train(1, 2, 2048, 2, 4096, 1, 1, 512)
model.train(1, 5, 2048, 2, 4096, 1, 1, 512)
model.train(1, 10, 2048, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 2, 4096, 1, 1, 512)
model.train(1, 1, 4096, 2, 4096, 1, 1, 512)
model.train(1, 2, 4096, 2, 4096, 1, 1, 512)
model.train(1, 5, 4096, 2, 4096, 1, 1, 512)
model.train(1, 10, 4096, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 2, 4096, 1, 1, 512)
model.train(1, 1, 8192, 2, 4096, 1, 1, 512)
model.train(1, 2, 8192, 2, 4096, 1, 1, 512)
model.train(1, 5, 8192, 2, 4096, 1, 1, 512)
model.train(1, 10, 8192, 2, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 4, 4096, 1, 1, 512)
model.train(1, 1, 1, 4, 4096, 1, 1, 512)
model.train(1, 2, 1, 4, 4096, 1, 1, 512)
model.train(1, 5, 1, 4, 4096, 1, 1, 512)
model.train(1, 10, 1, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 4, 4096, 1, 1, 512)
model.train(1, 1, 2, 4, 4096, 1, 1, 512)
model.train(1, 2, 2, 4, 4096, 1, 1, 512)
model.train(1, 5, 2, 4, 4096, 1, 1, 512)
model.train(1, 10, 2, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 4, 4096, 1, 1, 512)
model.train(1, 1, 4, 4, 4096, 1, 1, 512)
model.train(1, 2, 4, 4, 4096, 1, 1, 512)
model.train(1, 5, 4, 4, 4096, 1, 1, 512)
model.train(1, 10, 4, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 4, 4096, 1, 1, 512)
model.train(1, 1, 8, 4, 4096, 1, 1, 512)
model.train(1, 2, 8, 4, 4096, 1, 1, 512)
model.train(1, 5, 8, 4, 4096, 1, 1, 512)
model.train(1, 10, 8, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 4, 4096, 1, 1, 512)
model.train(1, 1, 16, 4, 4096, 1, 1, 512)
model.train(1, 2, 16, 4, 4096, 1, 1, 512)
model.train(1, 5, 16, 4, 4096, 1, 1, 512)
model.train(1, 10, 16, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 4, 4096, 1, 1, 512)
model.train(1, 1, 32, 4, 4096, 1, 1, 512)
model.train(1, 2, 32, 4, 4096, 1, 1, 512)
model.train(1, 5, 32, 4, 4096, 1, 1, 512)
model.train(1, 10, 32, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 4, 4096, 1, 1, 512)
model.train(1, 1, 64, 4, 4096, 1, 1, 512)
model.train(1, 2, 64, 4, 4096, 1, 1, 512)
model.train(1, 5, 64, 4, 4096, 1, 1, 512)
model.train(1, 10, 64, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 4, 4096, 1, 1, 512)
model.train(1, 1, 128, 4, 4096, 1, 1, 512)
model.train(1, 2, 128, 4, 4096, 1, 1, 512)
model.train(1, 5, 128, 4, 4096, 1, 1, 512)
model.train(1, 10, 128, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 4, 4096, 1, 1, 512)
model.train(1, 1, 256, 4, 4096, 1, 1, 512)
model.train(1, 2, 256, 4, 4096, 1, 1, 512)
model.train(1, 5, 256, 4, 4096, 1, 1, 512)
model.train(1, 10, 256, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 4, 4096, 1, 1, 512)
model.train(1, 1, 512, 4, 4096, 1, 1, 512)
model.train(1, 2, 512, 4, 4096, 1, 1, 512)
model.train(1, 5, 512, 4, 4096, 1, 1, 512)
model.train(1, 10, 512, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 4, 4096, 1, 1, 512)
model.train(1, 1, 1024, 4, 4096, 1, 1, 512)
model.train(1, 2, 1024, 4, 4096, 1, 1, 512)
model.train(1, 5, 1024, 4, 4096, 1, 1, 512)
model.train(1, 10, 1024, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 4, 4096, 1, 1, 512)
model.train(1, 1, 2048, 4, 4096, 1, 1, 512)
model.train(1, 2, 2048, 4, 4096, 1, 1, 512)
model.train(1, 5, 2048, 4, 4096, 1, 1, 512)
model.train(1, 10, 2048, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 4, 4096, 1, 1, 512)
model.train(1, 1, 4096, 4, 4096, 1, 1, 512)
model.train(1, 2, 4096, 4, 4096, 1, 1, 512)
model.train(1, 5, 4096, 4, 4096, 1, 1, 512)
model.train(1, 10, 4096, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 4, 4096, 1, 1, 512)
model.train(1, 1, 8192, 4, 4096, 1, 1, 512)
model.train(1, 2, 8192, 4, 4096, 1, 1, 512)
model.train(1, 5, 8192, 4, 4096, 1, 1, 512)
model.train(1, 10, 8192, 4, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 8, 4096, 1, 1, 512)
model.train(1, 1, 1, 8, 4096, 1, 1, 512)
model.train(1, 2, 1, 8, 4096, 1, 1, 512)
model.train(1, 5, 1, 8, 4096, 1, 1, 512)
model.train(1, 10, 1, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 8, 4096, 1, 1, 512)
model.train(1, 1, 2, 8, 4096, 1, 1, 512)
model.train(1, 2, 2, 8, 4096, 1, 1, 512)
model.train(1, 5, 2, 8, 4096, 1, 1, 512)
model.train(1, 10, 2, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 8, 4096, 1, 1, 512)
model.train(1, 1, 4, 8, 4096, 1, 1, 512)
model.train(1, 2, 4, 8, 4096, 1, 1, 512)
model.train(1, 5, 4, 8, 4096, 1, 1, 512)
model.train(1, 10, 4, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 8, 4096, 1, 1, 512)
model.train(1, 1, 8, 8, 4096, 1, 1, 512)
model.train(1, 2, 8, 8, 4096, 1, 1, 512)
model.train(1, 5, 8, 8, 4096, 1, 1, 512)
model.train(1, 10, 8, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 8, 4096, 1, 1, 512)
model.train(1, 1, 16, 8, 4096, 1, 1, 512)
model.train(1, 2, 16, 8, 4096, 1, 1, 512)
model.train(1, 5, 16, 8, 4096, 1, 1, 512)
model.train(1, 10, 16, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 8, 4096, 1, 1, 512)
model.train(1, 1, 32, 8, 4096, 1, 1, 512)
model.train(1, 2, 32, 8, 4096, 1, 1, 512)
model.train(1, 5, 32, 8, 4096, 1, 1, 512)
model.train(1, 10, 32, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 8, 4096, 1, 1, 512)
model.train(1, 1, 64, 8, 4096, 1, 1, 512)
model.train(1, 2, 64, 8, 4096, 1, 1, 512)
model.train(1, 5, 64, 8, 4096, 1, 1, 512)
model.train(1, 10, 64, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 8, 4096, 1, 1, 512)
model.train(1, 1, 128, 8, 4096, 1, 1, 512)
model.train(1, 2, 128, 8, 4096, 1, 1, 512)
model.train(1, 5, 128, 8, 4096, 1, 1, 512)
model.train(1, 10, 128, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 8, 4096, 1, 1, 512)
model.train(1, 1, 256, 8, 4096, 1, 1, 512)
model.train(1, 2, 256, 8, 4096, 1, 1, 512)
model.train(1, 5, 256, 8, 4096, 1, 1, 512)
model.train(1, 10, 256, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 8, 4096, 1, 1, 512)
model.train(1, 1, 512, 8, 4096, 1, 1, 512)
model.train(1, 2, 512, 8, 4096, 1, 1, 512)
model.train(1, 5, 512, 8, 4096, 1, 1, 512)
model.train(1, 10, 512, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 8, 4096, 1, 1, 512)
model.train(1, 1, 1024, 8, 4096, 1, 1, 512)
model.train(1, 2, 1024, 8, 4096, 1, 1, 512)
model.train(1, 5, 1024, 8, 4096, 1, 1, 512)
model.train(1, 10, 1024, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 8, 4096, 1, 1, 512)
model.train(1, 1, 2048, 8, 4096, 1, 1, 512)
model.train(1, 2, 2048, 8, 4096, 1, 1, 512)
model.train(1, 5, 2048, 8, 4096, 1, 1, 512)
model.train(1, 10, 2048, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 8, 4096, 1, 1, 512)
model.train(1, 1, 4096, 8, 4096, 1, 1, 512)
model.train(1, 2, 4096, 8, 4096, 1, 1, 512)
model.train(1, 5, 4096, 8, 4096, 1, 1, 512)
model.train(1, 10, 4096, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 8, 4096, 1, 1, 512)
model.train(1, 1, 8192, 8, 4096, 1, 1, 512)
model.train(1, 2, 8192, 8, 4096, 1, 1, 512)
model.train(1, 5, 8192, 8, 4096, 1, 1, 512)
model.train(1, 10, 8192, 8, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 16, 4096, 1, 1, 512)
model.train(1, 1, 1, 16, 4096, 1, 1, 512)
model.train(1, 2, 1, 16, 4096, 1, 1, 512)
model.train(1, 5, 1, 16, 4096, 1, 1, 512)
model.train(1, 10, 1, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 16, 4096, 1, 1, 512)
model.train(1, 1, 2, 16, 4096, 1, 1, 512)
model.train(1, 2, 2, 16, 4096, 1, 1, 512)
model.train(1, 5, 2, 16, 4096, 1, 1, 512)
model.train(1, 10, 2, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 16, 4096, 1, 1, 512)
model.train(1, 1, 4, 16, 4096, 1, 1, 512)
model.train(1, 2, 4, 16, 4096, 1, 1, 512)
model.train(1, 5, 4, 16, 4096, 1, 1, 512)
model.train(1, 10, 4, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 16, 4096, 1, 1, 512)
model.train(1, 1, 8, 16, 4096, 1, 1, 512)
model.train(1, 2, 8, 16, 4096, 1, 1, 512)
model.train(1, 5, 8, 16, 4096, 1, 1, 512)
model.train(1, 10, 8, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 16, 4096, 1, 1, 512)
model.train(1, 1, 16, 16, 4096, 1, 1, 512)
model.train(1, 2, 16, 16, 4096, 1, 1, 512)
model.train(1, 5, 16, 16, 4096, 1, 1, 512)
model.train(1, 10, 16, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 16, 4096, 1, 1, 512)
model.train(1, 1, 32, 16, 4096, 1, 1, 512)
model.train(1, 2, 32, 16, 4096, 1, 1, 512)
model.train(1, 5, 32, 16, 4096, 1, 1, 512)
model.train(1, 10, 32, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 16, 4096, 1, 1, 512)
model.train(1, 1, 64, 16, 4096, 1, 1, 512)
model.train(1, 2, 64, 16, 4096, 1, 1, 512)
model.train(1, 5, 64, 16, 4096, 1, 1, 512)
model.train(1, 10, 64, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 16, 4096, 1, 1, 512)
model.train(1, 1, 128, 16, 4096, 1, 1, 512)
model.train(1, 2, 128, 16, 4096, 1, 1, 512)
model.train(1, 5, 128, 16, 4096, 1, 1, 512)
model.train(1, 10, 128, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 16, 4096, 1, 1, 512)
model.train(1, 1, 256, 16, 4096, 1, 1, 512)
model.train(1, 2, 256, 16, 4096, 1, 1, 512)
model.train(1, 5, 256, 16, 4096, 1, 1, 512)
model.train(1, 10, 256, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 16, 4096, 1, 1, 512)
model.train(1, 1, 512, 16, 4096, 1, 1, 512)
model.train(1, 2, 512, 16, 4096, 1, 1, 512)
model.train(1, 5, 512, 16, 4096, 1, 1, 512)
model.train(1, 10, 512, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 16, 4096, 1, 1, 512)
model.train(1, 1, 1024, 16, 4096, 1, 1, 512)
model.train(1, 2, 1024, 16, 4096, 1, 1, 512)
model.train(1, 5, 1024, 16, 4096, 1, 1, 512)
model.train(1, 10, 1024, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 16, 4096, 1, 1, 512)
model.train(1, 1, 2048, 16, 4096, 1, 1, 512)
model.train(1, 2, 2048, 16, 4096, 1, 1, 512)
model.train(1, 5, 2048, 16, 4096, 1, 1, 512)
model.train(1, 10, 2048, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 16, 4096, 1, 1, 512)
model.train(1, 1, 4096, 16, 4096, 1, 1, 512)
model.train(1, 2, 4096, 16, 4096, 1, 1, 512)
model.train(1, 5, 4096, 16, 4096, 1, 1, 512)
model.train(1, 10, 4096, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 16, 4096, 1, 1, 512)
model.train(1, 1, 8192, 16, 4096, 1, 1, 512)
model.train(1, 2, 8192, 16, 4096, 1, 1, 512)
model.train(1, 5, 8192, 16, 4096, 1, 1, 512)
model.train(1, 10, 8192, 16, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 32, 4096, 1, 1, 512)
model.train(1, 1, 1, 32, 4096, 1, 1, 512)
model.train(1, 2, 1, 32, 4096, 1, 1, 512)
model.train(1, 5, 1, 32, 4096, 1, 1, 512)
model.train(1, 10, 1, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 32, 4096, 1, 1, 512)
model.train(1, 1, 2, 32, 4096, 1, 1, 512)
model.train(1, 2, 2, 32, 4096, 1, 1, 512)
model.train(1, 5, 2, 32, 4096, 1, 1, 512)
model.train(1, 10, 2, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 32, 4096, 1, 1, 512)
model.train(1, 1, 4, 32, 4096, 1, 1, 512)
model.train(1, 2, 4, 32, 4096, 1, 1, 512)
model.train(1, 5, 4, 32, 4096, 1, 1, 512)
model.train(1, 10, 4, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 32, 4096, 1, 1, 512)
model.train(1, 1, 8, 32, 4096, 1, 1, 512)
model.train(1, 2, 8, 32, 4096, 1, 1, 512)
model.train(1, 5, 8, 32, 4096, 1, 1, 512)
model.train(1, 10, 8, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 32, 4096, 1, 1, 512)
model.train(1, 1, 16, 32, 4096, 1, 1, 512)
model.train(1, 2, 16, 32, 4096, 1, 1, 512)
model.train(1, 5, 16, 32, 4096, 1, 1, 512)
model.train(1, 10, 16, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 32, 4096, 1, 1, 512)
model.train(1, 1, 32, 32, 4096, 1, 1, 512)
model.train(1, 2, 32, 32, 4096, 1, 1, 512)
model.train(1, 5, 32, 32, 4096, 1, 1, 512)
model.train(1, 10, 32, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 32, 4096, 1, 1, 512)
model.train(1, 1, 64, 32, 4096, 1, 1, 512)
model.train(1, 2, 64, 32, 4096, 1, 1, 512)
model.train(1, 5, 64, 32, 4096, 1, 1, 512)
model.train(1, 10, 64, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 32, 4096, 1, 1, 512)
model.train(1, 1, 128, 32, 4096, 1, 1, 512)
model.train(1, 2, 128, 32, 4096, 1, 1, 512)
model.train(1, 5, 128, 32, 4096, 1, 1, 512)
model.train(1, 10, 128, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 32, 4096, 1, 1, 512)
model.train(1, 1, 256, 32, 4096, 1, 1, 512)
model.train(1, 2, 256, 32, 4096, 1, 1, 512)
model.train(1, 5, 256, 32, 4096, 1, 1, 512)
model.train(1, 10, 256, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 32, 4096, 1, 1, 512)
model.train(1, 1, 512, 32, 4096, 1, 1, 512)
model.train(1, 2, 512, 32, 4096, 1, 1, 512)
model.train(1, 5, 512, 32, 4096, 1, 1, 512)
model.train(1, 10, 512, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 32, 4096, 1, 1, 512)
model.train(1, 1, 1024, 32, 4096, 1, 1, 512)
model.train(1, 2, 1024, 32, 4096, 1, 1, 512)
model.train(1, 5, 1024, 32, 4096, 1, 1, 512)
model.train(1, 10, 1024, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 32, 4096, 1, 1, 512)
model.train(1, 1, 2048, 32, 4096, 1, 1, 512)
model.train(1, 2, 2048, 32, 4096, 1, 1, 512)
model.train(1, 5, 2048, 32, 4096, 1, 1, 512)
model.train(1, 10, 2048, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 32, 4096, 1, 1, 512)
model.train(1, 1, 4096, 32, 4096, 1, 1, 512)
model.train(1, 2, 4096, 32, 4096, 1, 1, 512)
model.train(1, 5, 4096, 32, 4096, 1, 1, 512)
model.train(1, 10, 4096, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 32, 4096, 1, 1, 512)
model.train(1, 1, 8192, 32, 4096, 1, 1, 512)
model.train(1, 2, 8192, 32, 4096, 1, 1, 512)
model.train(1, 5, 8192, 32, 4096, 1, 1, 512)
model.train(1, 10, 8192, 32, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 64, 4096, 1, 1, 512)
model.train(1, 1, 1, 64, 4096, 1, 1, 512)
model.train(1, 2, 1, 64, 4096, 1, 1, 512)
model.train(1, 5, 1, 64, 4096, 1, 1, 512)
model.train(1, 10, 1, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 64, 4096, 1, 1, 512)
model.train(1, 1, 2, 64, 4096, 1, 1, 512)
model.train(1, 2, 2, 64, 4096, 1, 1, 512)
model.train(1, 5, 2, 64, 4096, 1, 1, 512)
model.train(1, 10, 2, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 64, 4096, 1, 1, 512)
model.train(1, 1, 4, 64, 4096, 1, 1, 512)
model.train(1, 2, 4, 64, 4096, 1, 1, 512)
model.train(1, 5, 4, 64, 4096, 1, 1, 512)
model.train(1, 10, 4, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 64, 4096, 1, 1, 512)
model.train(1, 1, 8, 64, 4096, 1, 1, 512)
model.train(1, 2, 8, 64, 4096, 1, 1, 512)
model.train(1, 5, 8, 64, 4096, 1, 1, 512)
model.train(1, 10, 8, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 64, 4096, 1, 1, 512)
model.train(1, 1, 16, 64, 4096, 1, 1, 512)
model.train(1, 2, 16, 64, 4096, 1, 1, 512)
model.train(1, 5, 16, 64, 4096, 1, 1, 512)
model.train(1, 10, 16, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 64, 4096, 1, 1, 512)
model.train(1, 1, 32, 64, 4096, 1, 1, 512)
model.train(1, 2, 32, 64, 4096, 1, 1, 512)
model.train(1, 5, 32, 64, 4096, 1, 1, 512)
model.train(1, 10, 32, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 64, 4096, 1, 1, 512)
model.train(1, 1, 64, 64, 4096, 1, 1, 512)
model.train(1, 2, 64, 64, 4096, 1, 1, 512)
model.train(1, 5, 64, 64, 4096, 1, 1, 512)
model.train(1, 10, 64, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 64, 4096, 1, 1, 512)
model.train(1, 1, 128, 64, 4096, 1, 1, 512)
model.train(1, 2, 128, 64, 4096, 1, 1, 512)
model.train(1, 5, 128, 64, 4096, 1, 1, 512)
model.train(1, 10, 128, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 64, 4096, 1, 1, 512)
model.train(1, 1, 256, 64, 4096, 1, 1, 512)
model.train(1, 2, 256, 64, 4096, 1, 1, 512)
model.train(1, 5, 256, 64, 4096, 1, 1, 512)
model.train(1, 10, 256, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 64, 4096, 1, 1, 512)
model.train(1, 1, 512, 64, 4096, 1, 1, 512)
model.train(1, 2, 512, 64, 4096, 1, 1, 512)
model.train(1, 5, 512, 64, 4096, 1, 1, 512)
model.train(1, 10, 512, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 64, 4096, 1, 1, 512)
model.train(1, 1, 1024, 64, 4096, 1, 1, 512)
model.train(1, 2, 1024, 64, 4096, 1, 1, 512)
model.train(1, 5, 1024, 64, 4096, 1, 1, 512)
model.train(1, 10, 1024, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 64, 4096, 1, 1, 512)
model.train(1, 1, 2048, 64, 4096, 1, 1, 512)
model.train(1, 2, 2048, 64, 4096, 1, 1, 512)
model.train(1, 5, 2048, 64, 4096, 1, 1, 512)
model.train(1, 10, 2048, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 64, 4096, 1, 1, 512)
model.train(1, 1, 4096, 64, 4096, 1, 1, 512)
model.train(1, 2, 4096, 64, 4096, 1, 1, 512)
model.train(1, 5, 4096, 64, 4096, 1, 1, 512)
model.train(1, 10, 4096, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 64, 4096, 1, 1, 512)
model.train(1, 1, 8192, 64, 4096, 1, 1, 512)
model.train(1, 2, 8192, 64, 4096, 1, 1, 512)
model.train(1, 5, 8192, 64, 4096, 1, 1, 512)
model.train(1, 10, 8192, 64, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 128, 4096, 1, 1, 512)
model.train(1, 1, 1, 128, 4096, 1, 1, 512)
model.train(1, 2, 1, 128, 4096, 1, 1, 512)
model.train(1, 5, 1, 128, 4096, 1, 1, 512)
model.train(1, 10, 1, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 128, 4096, 1, 1, 512)
model.train(1, 1, 2, 128, 4096, 1, 1, 512)
model.train(1, 2, 2, 128, 4096, 1, 1, 512)
model.train(1, 5, 2, 128, 4096, 1, 1, 512)
model.train(1, 10, 2, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 128, 4096, 1, 1, 512)
model.train(1, 1, 4, 128, 4096, 1, 1, 512)
model.train(1, 2, 4, 128, 4096, 1, 1, 512)
model.train(1, 5, 4, 128, 4096, 1, 1, 512)
model.train(1, 10, 4, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 128, 4096, 1, 1, 512)
model.train(1, 1, 8, 128, 4096, 1, 1, 512)
model.train(1, 2, 8, 128, 4096, 1, 1, 512)
model.train(1, 5, 8, 128, 4096, 1, 1, 512)
model.train(1, 10, 8, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 128, 4096, 1, 1, 512)
model.train(1, 1, 16, 128, 4096, 1, 1, 512)
model.train(1, 2, 16, 128, 4096, 1, 1, 512)
model.train(1, 5, 16, 128, 4096, 1, 1, 512)
model.train(1, 10, 16, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 128, 4096, 1, 1, 512)
model.train(1, 1, 32, 128, 4096, 1, 1, 512)
model.train(1, 2, 32, 128, 4096, 1, 1, 512)
model.train(1, 5, 32, 128, 4096, 1, 1, 512)
model.train(1, 10, 32, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 128, 4096, 1, 1, 512)
model.train(1, 1, 64, 128, 4096, 1, 1, 512)
model.train(1, 2, 64, 128, 4096, 1, 1, 512)
model.train(1, 5, 64, 128, 4096, 1, 1, 512)
model.train(1, 10, 64, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 128, 4096, 1, 1, 512)
model.train(1, 1, 128, 128, 4096, 1, 1, 512)
model.train(1, 2, 128, 128, 4096, 1, 1, 512)
model.train(1, 5, 128, 128, 4096, 1, 1, 512)
model.train(1, 10, 128, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 128, 4096, 1, 1, 512)
model.train(1, 1, 256, 128, 4096, 1, 1, 512)
model.train(1, 2, 256, 128, 4096, 1, 1, 512)
model.train(1, 5, 256, 128, 4096, 1, 1, 512)
model.train(1, 10, 256, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 128, 4096, 1, 1, 512)
model.train(1, 1, 512, 128, 4096, 1, 1, 512)
model.train(1, 2, 512, 128, 4096, 1, 1, 512)
model.train(1, 5, 512, 128, 4096, 1, 1, 512)
model.train(1, 10, 512, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 128, 4096, 1, 1, 512)
model.train(1, 1, 1024, 128, 4096, 1, 1, 512)
model.train(1, 2, 1024, 128, 4096, 1, 1, 512)
model.train(1, 5, 1024, 128, 4096, 1, 1, 512)
model.train(1, 10, 1024, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 128, 4096, 1, 1, 512)
model.train(1, 1, 2048, 128, 4096, 1, 1, 512)
model.train(1, 2, 2048, 128, 4096, 1, 1, 512)
model.train(1, 5, 2048, 128, 4096, 1, 1, 512)
model.train(1, 10, 2048, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 128, 4096, 1, 1, 512)
model.train(1, 1, 4096, 128, 4096, 1, 1, 512)
model.train(1, 2, 4096, 128, 4096, 1, 1, 512)
model.train(1, 5, 4096, 128, 4096, 1, 1, 512)
model.train(1, 10, 4096, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 128, 4096, 1, 1, 512)
model.train(1, 1, 8192, 128, 4096, 1, 1, 512)
model.train(1, 2, 8192, 128, 4096, 1, 1, 512)
model.train(1, 5, 8192, 128, 4096, 1, 1, 512)
model.train(1, 10, 8192, 128, 4096, 1, 1, 512)
model.train(1, 0.001, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 1, 256, 4096, 1, 1, 512)
model.train(1, 1, 1, 256, 4096, 1, 1, 512)
model.train(1, 2, 1, 256, 4096, 1, 1, 512)
model.train(1, 5, 1, 256, 4096, 1, 1, 512)
model.train(1, 10, 1, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 2, 256, 4096, 1, 1, 512)
model.train(1, 1, 2, 256, 4096, 1, 1, 512)
model.train(1, 2, 2, 256, 4096, 1, 1, 512)
model.train(1, 5, 2, 256, 4096, 1, 1, 512)
model.train(1, 10, 2, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 4, 256, 4096, 1, 1, 512)
model.train(1, 1, 4, 256, 4096, 1, 1, 512)
model.train(1, 2, 4, 256, 4096, 1, 1, 512)
model.train(1, 5, 4, 256, 4096, 1, 1, 512)
model.train(1, 10, 4, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 8, 256, 4096, 1, 1, 512)
model.train(1, 1, 8, 256, 4096, 1, 1, 512)
model.train(1, 2, 8, 256, 4096, 1, 1, 512)
model.train(1, 5, 8, 256, 4096, 1, 1, 512)
model.train(1, 10, 8, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 16, 256, 4096, 1, 1, 512)
model.train(1, 1, 16, 256, 4096, 1, 1, 512)
model.train(1, 2, 16, 256, 4096, 1, 1, 512)
model.train(1, 5, 16, 256, 4096, 1, 1, 512)
model.train(1, 10, 16, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 32, 256, 4096, 1, 1, 512)
model.train(1, 1, 32, 256, 4096, 1, 1, 512)
model.train(1, 2, 32, 256, 4096, 1, 1, 512)
model.train(1, 5, 32, 256, 4096, 1, 1, 512)
model.train(1, 10, 32, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 64, 256, 4096, 1, 1, 512)
model.train(1, 1, 64, 256, 4096, 1, 1, 512)
model.train(1, 2, 64, 256, 4096, 1, 1, 512)
model.train(1, 5, 64, 256, 4096, 1, 1, 512)
model.train(1, 10, 64, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 128, 256, 4096, 1, 1, 512)
model.train(1, 1, 128, 256, 4096, 1, 1, 512)
model.train(1, 2, 128, 256, 4096, 1, 1, 512)
model.train(1, 5, 128, 256, 4096, 1, 1, 512)
model.train(1, 10, 128, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 256, 256, 4096, 1, 1, 512)
model.train(1, 1, 256, 256, 4096, 1, 1, 512)
model.train(1, 2, 256, 256, 4096, 1, 1, 512)
model.train(1, 5, 256, 256, 4096, 1, 1, 512)
model.train(1, 10, 256, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 512, 256, 4096, 1, 1, 512)
model.train(1, 1, 512, 256, 4096, 1, 1, 512)
model.train(1, 2, 512, 256, 4096, 1, 1, 512)
model.train(1, 5, 512, 256, 4096, 1, 1, 512)
model.train(1, 10, 512, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 1024, 256, 4096, 1, 1, 512)
model.train(1, 1, 1024, 256, 4096, 1, 1, 512)
model.train(1, 2, 1024, 256, 4096, 1, 1, 512)
model.train(1, 5, 1024, 256, 4096, 1, 1, 512)
model.train(1, 10, 1024, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 2048, 256, 4096, 1, 1, 512)
model.train(1, 1, 2048, 256, 4096, 1, 1, 512)
model.train(1, 2, 2048, 256, 4096, 1, 1, 512)
model.train(1, 5, 2048, 256, 4096, 1, 1, 512)
model.train(1, 10, 2048, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 4096, 256, 4096, 1, 1, 512)
model.train(1, 1, 4096, 256, 4096, 1, 1, 512)
model.train(1, 2, 4096, 256, 4096, 1, 1, 512)
model.train(1, 5, 4096, 256, 4096, 1, 1, 512)
model.train(1, 10, 4096, 256, 4096, 1, 1, 512)
model.train(1, 0.001, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.002, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.005, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.01, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.02, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.05, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.1, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.2, 8192, 256, 4096, 1, 1, 512)
model.train(1, 0.5, 8192, 256, 4096, 1, 1, 512)
model.train(1, 1, 8192, 256, 4096, 1, 1, 512)
model.train(1, 2, 8192, 256, 4096, 1, 1, 512)
model.train(1, 5, 8192, 256, 4096, 1, 1, 512)
model.train(1, 10, 8192, 256, 4096, 1, 1, 512)