config = { 'layout': [layer_1, layer_2], 'weight_type': 'binary', 'act_func_names': ['bs', 'bs'], 'act_func_params': [[], []], 'bias_vals': [None, None, None], 'keep_probs': [keep_probs1, keep_probs2, keep_probs2], 'flat_factor': [1., 1., 1.], 'act_noise': [0., 0., 0.], 'prior_value': 0.85, 'sampling_sequence': 'stochastic' } va_acc_list = [] va_ce_list = [] ens_acc_list = [] ens_ce_list = [] for run in range(n_runs): ens_acc, ens_ce, va_acc, va_ce = run_experiment(run_config, init_config, config, 'mnist_basic') va_acc_list.append(va_acc) va_ce_list.append(va_ce) ens_acc_list.append(ens_acc) ens_ce_list.append(ens_ce) print_stats('Mod Accuracy', va_acc_list) print_stats('Ens Accuracy', ens_acc_list) print_stats('Mod CE', va_ce_list) print_stats('Ens CE', ens_ce_list)
'store_method': 'both', 'burn_in': 15, 'thinning': 5, 'path': path} act_func1 = get_activation_function('bs') act_func2 = get_activation_function('bs') act_func1.set_params([]) act_func2.set_params([]) act_funcs = [act_func1, act_func2] layer_1 = 200# + 20 * int(task_id / 2) layer_2 = 200# + 40 * int(task_id % 2) keep_probs1 = 1.#.5 + (0.2 * int(task_id / 2)) keep_probs2 = 1.#.7 + (0.2 * int(task_id % 2)) config = {'layout': [layer_1, layer_2], 'weight_type': 'binary', 'act_funcs': act_funcs, 'bias_vals': [None, None, None], 'keep_probs': [keep_probs1, keep_probs2, keep_probs2], 'flat_factor': [1., 1., 1.], 'act_noise': [0., 0., 0.], 'prior_value': 0.7, 'sampling_sequence': 'stochastic'} run_experiment(run_config, init_config, config, 'mnist_basic')