all_methods_on_mnist.py 文件源码

def _check_forward():
    w_100 = []
    for i in range(1):
        for _mode in HO_MODES[0:1]:
            for _model in IMPLEMENTED_MODEL_TYPES[0:2]:
                _model_kwargs = {}  # {'dims': [None, 300, 300, None]}
                tf.reset_default_graph()
                # set random seeds!!!!
                np.random.seed(1)
                tf.set_random_seed(1)

                results = experiment('test_with_model_' + _model, collect_data=False, hyper_iterations=10, mode=_mode,
                                     epochs=None,
                                     model=_model,
                                     model_kwargs=_model_kwargs,
                                     set_T=1000,
                                     synthetic_hypers=None,
                                     hyper_batch_size=100
                                     # optimizer=rf.GradientDescentOptimizer,
                                     # optimizer_kwargs={'lr': tf.Variable(.01, name='eta')}
                                     )
                w_100.append(results[0]['weights'])
    # rf.save_obj(w_100, 'check_forward')
    return w_100