<jupyter_start><jupyter_text>split off 40 validation trials<jupyter_code>valid_less = [t[-40:] for t in train_less]
train_less = [t[:-40] for t in train_less]
n_chans = train_less[0].shape[1]
n_time = train_less[0].shape[2]



n_chan_pad = 0
filter_length_time = 11
    
model = larger_model(n_chans, n_time, final_fft=True, kernel_length=11, constant_memory=False)
model.cuda()
from reversible2.models import add_dropout_before_convs
#add_dropout_before_convs(model,p_conv=0.3, p_full=0.3)
dist = TwoClassIndependentDist(np.prod(train_less[0].size()[1:]))
dist.cuda()
model_and_dist = ModelAndDist(model, dist)
model_and_dist.set_dist_to_empirical(train_less)

optim = th.optim.Adam([{'params': dist.parameters(), 'lr':1e-2},
                      {'params': list(model_and_dist.model.parameters()),
                      'lr': 1e-4}])
p 0,0.1,0.3,0.5
w decay 0 1e-4, 1e-3, 1e-2,
noise factor 1e-2, 5e-2, 1e-1<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '277105', 'license': 'MIT', 'n_tokens_mistral': 407, 'n_tokens_neox': 381, 'n_words': 65, 'path': 'notebooks/simpler-invnet-20-june-2019/.ipynb_checkpoints/NllDropoutEtc-checkpoint.ipynb', 'repo_name': 'robintibor/reversible2', 'size': '940'}