def db_from_df(train, test, bs=1024):
# Create TensorDatasets
train_ds = TensorDataset(torch.tensor(train.values),
torch.tensor(train.values))
valid_ds = TensorDataset(torch.tensor(test.values),
torch.tensor(test.values))
# Create DataLoaders
train_dl, valid_dl = get_data(train_ds, valid_ds, bs=bs)
# Return DataBunch
return basic_data.DataBunch(train_dl, valid_dl)
test = test[test['m'] > 1e-3]
if input_dim == 25:
train.pop('Width')
train.pop('WidthPhi')
test.pop('Width')
test.pop('WidthPhi')
bs = 1024
# Create TensorDatasets
train_ds = TensorDataset(torch.tensor(train.values, dtype=torch.float),
torch.tensor(train.values, dtype=torch.float))
valid_ds = TensorDataset(torch.tensor(test.values, dtype=torch.float),
torch.tensor(test.values, dtype=torch.float))
# Create DataLoaders
train_dl, valid_dl = get_data(train_ds, valid_ds, bs=bs)
# Return DataBunch
db = basic_data.DataBunch(train_dl, valid_dl)
module_name = 'AE_bn_LeakyReLU'
module = AE_bn_LeakyReLU
grid_search_folder = module_name + '_AOD_grid_search_custom_normalization_1500epochs/'
model_folder = 'AE_%d_200_200_200_%d_200_200_200_%d' % (input_dim, latent_dim,
input_dim)
train_folder = 'AE_bn_LeakyReLU_bs4096_lr1e-02_wd1e-02_ppNA'
save = False
loss_func = nn.MSELoss()
plt.close('all')