'hidden_units': 2**(9 + hp.randint('hidden_units_log2', 3)) } # Fixed parameters search_space.update({ 'max_layers': n_layers, 'lr_decay': 0.5, 'lr_decay_epocs': 250, 'n_threads': 4, 'memory_factor': 2, 'max_epochs': MAX_EPOCHS, 'strip_length': 5, 'progress_thresh': 0.1, }) stats = tune_model(dataset=datasets.Datasets.COVERTYPE, settings_fn=datasets.CoverTypeSettings, search_space=search_space, n_trials=CV_TRIALS, cross_validate=False, folder='cover', runs=SIM_RUNS, test_batch_size=1, fine_tune=FineTuningType.ExtraLayerwise( epochs_per_layer=20, policy=CyclicPolicy)) metrics = stats[0].keys() for m in metrics: values = [x[m] for x in stats] logger.info('%s: %f +- %f' % (m, np.mean(values), np.std(values)))
'batch_norm': False, 'padding': 'VALID', 'max_layers': n_layers, # These are cnn layers here 'layerwise_progress_thresh': 0.1, 'n_threads': 4, 'memory_factor': 2, 'max_epochs': MAX_EPOCHS, 'tune_folder': 'incremental_2l_kernel', 'strip_length': 5, 'progress_thresh': 0.1, 'network_fn': cnn_kernel_example_layout_fn, 'policy': {'switch_policy': CyclicPolicy}, 'fc_layers': 2 }) stats = tune_model( dataset=datasets.Datasets.FASHION_MNIST, settings_fn=datasets.FashionMnistSettings, search_space=search_space, n_trials=CV_TRIALS, cross_validate=False, folder='incremental_2l_kernel_stats', runs=SIM_RUNS, test_batch_size=128 ) metrics = stats[0].keys() for m in metrics: values = [x[m] for x in stats] logger.info('%s: %f +- %f' % (m, np.mean(values), np.std(values)))
'padding': 'VALID', 'max_layers': n_layers, # These are cnn layers here 'layerwise_progress_thresh': 0.1, 'n_threads': 4, 'memory_factor': 2, 'max_epochs': MAX_EPOCHS, 'strip_length': 5, 'kernel_dropout_rate': 0.95, 'tune_folder': name + '_validate', 'progress_thresh': 0.1, 'network_fn': cnn_kernel_example_layout_fn, 'policy': {'switch_policy': CyclicPolicy}, 'fc_layers': 2 }) stats = tune_model( dataset=datasets.Datasets.CIFAR10, settings_fn=datasets.Cifar10Settings, search_space=search_space, n_trials=CV_TRIALS, cross_validate=False, folder=name + '_stats', runs=SIM_RUNS, test_batch_size=128 ) metrics = stats[0].keys() for m in metrics: values = [x[m] for x in stats] logger.info('%s: %f +- %f' % (m, np.mean(values), np.std(values)))
'hidden_units': 2**(6 + hp.randint('hidden_units_log2', 3)) } # Fixed parameters search_space.update({ 'max_layers': n_layers, 'lr_decay': 0.5, 'lr_decay_epocs': 250, 'n_threads': 4, 'strip_length': 5, 'memory_factor': 1, 'max_epochs': MAX_EPOCHS, 'progress_thresh': 0.1, }) stats = tune_model(dataset=datasets.Datasets.TITANIC, settings_fn=datasets.TitanicSettings, search_space=search_space, n_trials=CV_TRIALS, cross_validate=True, folder='titanic', runs=SIM_RUNS, test_batch_size=1, fine_tune=FineTuningType.ExtraLayerwise( epochs_per_layer=20, policy=CyclicPolicy)) metrics = stats[0].keys() for m in metrics: values = [x[m] for x in stats] logger.info('%s: %f +- %f' % (m, np.mean(values), np.std(values)))
# Fixed parameters search_space.update({ 'max_layers': n_layers, 'n_threads': 4, 'memory_factor': 2, 'batch_norm': False, 'max_epochs': MAX_EPOCHS, 'strip_length': 5, 'progress_thresh': 0.1, 'network_fn': kernel_example_layout_fn }) stats = tune_model( dataset=datasets.Datasets.MOTOR, settings_fn=datasets.MotorSettings, search_space=search_space, n_trials=CV_TRIALS, cross_validate=False, folder='motor', runs=SIM_RUNS, test_batch_size=1, #fine_tune=FineTuningType.ExtraLayerwise( # epochs_per_layer=20, policy=CyclicPolicy #) ) metrics = stats[0].keys() for m in metrics: values = [x[m] for x in stats] logger.info('%s: %f +- %f' % (m, np.mean(values), np.std(values)))