def _training(jdata):
# training
training = {}
seed = None
if j_have (jdata, 'seed') :
seed = jdata['seed']
training['systems'] = jdata['systems']
training['set_prefix'] = jdata['set_prefix']
training['stop_batch'] = jdata['stop_batch']
training['batch_size'] = jdata['batch_size']
if seed is not None:
training['seed'] = seed
training['disp_file'] = "lcurve.out"
if j_have (jdata, "disp_file") : training['disp_file'] = jdata["disp_file"]
training['disp_freq'] = j_must_have (jdata, 'disp_freq')
training['numb_test'] = j_must_have (jdata, 'numb_test')
training['save_freq'] = j_must_have (jdata, 'save_freq')
training['save_ckpt'] = j_must_have (jdata, 'save_ckpt')
training['disp_training'] = j_must_have (jdata, 'disp_training')
training['time_training'] = j_must_have (jdata, 'time_training')
if j_have (jdata, 'profiling') :
training['profiling'] = jdata['profiling']
if training['profiling'] :
training['profiling_file'] = j_must_have (jdata, 'profiling_file')
return training
def _loss(jdata):
# loss
loss = {}
loss['start_pref_e'] = j_must_have (jdata, 'start_pref_e')
loss['limit_pref_e'] = j_must_have (jdata, 'limit_pref_e')
loss['start_pref_f'] = j_must_have (jdata, 'start_pref_f')
loss['limit_pref_f'] = j_must_have (jdata, 'limit_pref_f')
loss['start_pref_v'] = j_must_have (jdata, 'start_pref_v')
loss['limit_pref_v'] = j_must_have (jdata, 'limit_pref_v')
if j_have(jdata, 'start_pref_ae') :
loss['start_pref_ae'] = jdata['start_pref_ae']
if j_have(jdata, 'limit_pref_ae') :
loss['limit_pref_ae'] = jdata['limit_pref_ae']
return loss
def _fitting_net(jdata):
fitting_net = {}
seed = None
if j_have (jdata, 'seed') :
seed = jdata['seed']
fitting_net['neuron']= j_must_have_d (jdata, 'fitting_neuron', ['n_neuron'])
fitting_net['resnet_dt'] = True
if j_have(jdata, 'resnet_dt') :
fitting_net['resnet_dt'] = jdata['resnet_dt']
if j_have(jdata, 'fitting_resnet_dt') :
fitting_net['resnet_dt'] = jdata['fitting_resnet_dt']
if seed is not None:
fitting_net['seed'] = seed
return fitting_net
def _smth_descriptor(jdata):
descriptor = {}
seed = None
if j_have (jdata, 'seed') :
seed = jdata['seed']
descriptor['type'] = 'se_a'
descriptor['sel'] = jdata['sel_a']
if j_have(jdata, 'rcut_smth') :
descriptor['rcut_smth'] = jdata['rcut_smth']
else :
descriptor['rcut_smth'] = descriptor['rcut']
descriptor['rcut'] = jdata['rcut']
descriptor['neuron'] = j_must_have (jdata, 'filter_neuron')
descriptor['axis_neuron'] = j_must_have_d (jdata, 'axis_neuron', ['n_axis_neuron'])
descriptor['resnet_dt'] = False
if j_have(jdata, 'resnet_dt') :
descriptor['resnet_dt'] = jdata['filter_resnet_dt']
if seed is not None:
descriptor['seed'] = seed
return descriptor
def _nonsmth_descriptor(jdata) :
output = {}
seed = None
if j_have (jdata, 'seed') :
seed = jdata['seed']
# model
descriptor = {}
descriptor['type'] = 'loc_frame'
descriptor['sel_a'] = jdata['sel_a']
descriptor['sel_r'] = jdata['sel_r']
descriptor['rcut'] = jdata['rcut']
descriptor['axis_rule'] = jdata['axis_rule']
return descriptor