config.p.r = 128 config.regularization = 1.0e-4 config.p.lr_reduction = 2 config.p.gru_depth = 1 config.p.dropout = None config.p.augmentation = False config.p.structure_data = True config.p.attention = True config.p.full_state_attention = False config.p.bidirectional = False config.p.out_layers = 1 config.p.max_epochs = None train_object = trainer.Trainer(config, load=False, draw_plots=False, save_location='./weights/gen2000', model=model) train_object.v.optimizer.gradient_clipping = 100.0 # clip the norm to this amount print() print() train_object.run_many_epochs(language_model, plot_every=1000, write_every=10, early_stop=None, save_every=30, validate_every=150000, batch_size=10,
config.p.r = 128 config.regularization = 1.0e-4 config.p.lr_reduction = 10 config.p.gru_depth = 2 #config.p.dropout = None #config.p.augmentation= False config.p.structure_data = True #config.p.attention=True #config.p.full_state_attention=False config.p.bidirectional = True config.p.out_layers = 1 config.p.max_epochs = None train_object = trainer.Trainer(config, load=False, draw_plots=False, save_location='./weights/payout_5_med', model=model) train_object.v.optimizer.gradient_clipping = 100.0 # clip the norm to this amount print() print() train_object.run_many_epochs(language_model, plot_every=10000, write_every=10, early_stop=None, save_every=120, validate_every=400000, batch_size=100,