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')