def build_model(config,
train_dataset,
train_labels,
set_non_trainable=False,
train_stats=None):
"""
Build the NN model based on the input
"""
ModelArchitect = config['MODEL']['ModelArchitect']
NodesList = mrnn_utility.getlist_int(config['MODEL']['NodesList'])
Activation = mrnn_utility.getlist_str(config['MODEL']['Activation'])
if (len(NodesList) != len(Activation)):
raise ValueError(
'In the config file, number of NodesList != Activation list with NodesList = ',
NodesList, ' and Activation = ', Activation)
if (ModelArchitect.lower() == "dnn_kregl1l2_gauss".lower()):
model = DNN_kregl1l2_gauss(config, train_dataset, train_labels,
NodesList, Activation)
elif (ModelArchitect.lower() == "user_dnn_kregl1l2_gauss_grad".lower()):
model = user_DNN_kregl1l2_gauss_grad_setup(config, train_dataset,
train_labels, NodesList,
Activation, train_stats)
elif (ModelArchitect.lower().find('cnn') >= 0):
LayerName = mrnn_utility.getlist_str(config['MODEL']['LayerName'])
Padding = mrnn_utility.getlist_str(config['MODEL']['Padding'])
if (len(NodesList) != len(LayerName)
or len(NodesList) != len(Padding)):
raise ValueError(
'In the config file, number of NodesList != LayerName with NodesList = ',
NodesList, len(NodesList), ' and LayerName = ', LayerName,
len(LayerName), 'and Padding = ', Padding, len(Padding))
elif (ModelArchitect.lower() == "CNN_user_supervise".lower()):
model = CNN_user_supervise_setup(config, train_dataset,
train_labels, NodesList,
Activation, LayerName, Padding)
elif (ModelArchitect.lower() == "CNN_supervise".lower()):
model = CNN_supervise(config, train_dataset, train_labels,
NodesList, Activation, LayerName, Padding)
else:
raise ValueError('Model architect = ', ModelArchitect,
' is chosen, but is not implemented!')
else:
raise ValueError('Model architect = ', ModelArchitect,
' is chosen, but is not implemented!')
if set_non_trainable:
model.trainable = False
return model
def build_learningrate(config):
"""
Build different learning rates based on the input
"""
LR = mrnn_utility.getlist_str(config['MODEL']['LearningRate'])
# print('LR str: ', LR)
if (len(LR) == 1):
LearningRate = float(LR[0])
# print('--Decay in mono rate: rate = ', LearningRate)
elif (len(LR) > 1):
LR_type = LR[0]
if (LR_type == 'mono'):
LearningRate = float(LR[1])
elif (LR_type == 'decay_exp'):
initial_learning_rate = 0.001
decay_steps = 1000
decay_rate = 0.96
initial_learning_rate = float(LR[1])
if len(LR) > 2:
decay_steps = int(LR[2])
if len(LR) > 3:
decay_rate = float(LR[3])
# print('--Decay in exponential rate: initial rate = ', initial_learning_rate, ', decay steps = ', decay_steps, ', decay_rate = ', decay_rate)
if (mrnn_utility.get_package_version(tf.__version__)[0] == 1 and
mrnn_utility.get_package_version(tf.__version__)[1] <= 13):
global_step = tf.Variable(0,
name='global_step',
trainable=False)
global_step = tf.train.get_global_step()
LearningRate = tf.train.exponential_decay(
initial_learning_rate,
global_step,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=True)
else:
# print("!!!! Caution: use Learning rate with care, there were occasions that tf1.13 should better performance on training. !!!")
global_step = tf.Variable(0,
name='global_step',
trainable=False)
LearningRate = tf.compat.v1.train.exponential_decay(
initial_learning_rate,
global_step,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=True)
else:
raise ValueError(
'unknown choice for learning rate (mono, decay_exp): ', LR)
return LearningRate
def build_callbacks(config):
"""
Build different call back functions based on the input
"""
callbacks = []
callback_names = mrnn_utility.getlist_str(config['MODEL']['CallBacks'])
if 'checkpoint' in callback_names:
callbacks.append(check_point_callback(config))
if 'tensorboard' in callback_names:
callbacks.append(tensor_board_callback(config))
if 'printdot' in callback_names:
callbacks.append(callback_PrintDot())
return callbacks
train_dataset,
train_labels,
train_stats=train_stats)
if (config['RESTART']['RestartWeight'].lower() == 'y'):
print('checkpoint_dir for restart: ', checkpoint_dir)
latest = tf.train.latest_checkpoint(checkpoint_dir)
print("latest checkpoint: ", latest)
if (latest != None):
model.load_weights(latest)
print("Successfully load weight: ", latest)
else:
print("No saved weights, start to train the model from the beginning!")
pass
metrics = mrnn_utility.getlist_str(config['MODEL']['Metrics'])
optimizer = mrnn_models.build_optimizer(config)
loss = mrnn_models.my_mse_loss_with_grad(BetaP=0.0)
model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
label_scale = float(config['TEST']['LabelScale'])
callbacks = mrnn_models.build_callbacks(config)
train_dataset = train_dataset.to_numpy()
train_labels = train_labels.to_numpy()
val_dataset = val_dataset.to_numpy()
val_labels = val_labels.to_numpy()
test_dataset = test_dataset.to_numpy()
test_labels = test_labels.to_numpy()
# make sure that the derivative data is scaled correctly
def shift_labels(config, dataset, dataset_index, dataset_frame, data_file):
"""
Shift label based on the trained-NN predictions
"""
# print("---!!!!--- reach shift_labels!!!!")
# print("---!!!!--- Remember to modify old 'std', old 'mean' for DNN based KBNN")
trained_model_lists = mrnn_utility.getlist_str(
config['KBNN']['LabelShiftingModels'])
if len(trained_model_lists) > 0:
# all_fields = mrnn_utility.getlist_str(config['TEST']['AllFields'])
label_fields = mrnn_utility.getlist_str(config['TEST']['LabelFields'])
if len(label_fields) > 1:
# raise ValueError(
# 'Shift labels is not working for two labels shifting yet!')
print('Shift labels is not working for two labels shifting yet!')
# if label_fields[0] != all_fields[-1]:
# raise ValueError('the single label for KBNN should put at the end of all label fields!')
# print("---!!!!--- load trained model!!!!")
old_models = load_trained_model(trained_model_lists)
# print("---!!!!--- after load trained model!!!!")
key0 = label_fields[0]
old_label_scale = mrnn_utility.getlist_float(
config['KBNN']['OldShiftLabelScale'])
# print('old shift features: ', config['KBNN']['OldShiftFeatures'])
# to switch between vtk and other features
if (config['KBNN']['OldShiftFeatures'].find('.vtk') >= 0):
# """ """
# print("--- here: vtk for label shift")
# index should not be used anymore.
# use base frame info to do the base free energy shifting
dataset_old = mrnn_utility.load_data_from_vtk_for_label_shift_frame(
config, dataset_frame, normalization_flag=True, verbose=0)
elif (config['KBNN']['OldShiftFeatures'].find('.npy') >= 0):
# """ """
# print("--- here: npy for label shift")
# index should not be used anymore.
# use base frame info to do the base free energy shifting
dataset_old = mrnn_utility.load_data_from_npy_for_label_shift_frame(
config, dataset_frame, normalization_flag=True, verbose=0)
else:
old_feature_fields = mrnn_utility.getlist_str(
config['KBNN']['OldShiftFeatures'])
raw_dataset_old = mrnn_utility.read_csv_fields(
data_file, old_feature_fields)
dataset_old = raw_dataset_old.copy()
if len(old_feature_fields) > 0:
old_mean = mrnn_utility.getlist_float(
config['KBNN']['OldShiftMean'])
old_std = mrnn_utility.getlist_float(
config['KBNN']['OldShiftStd'])
old_data_norm = int(config['KBNN']['OldShiftDataNormOption'])
if (old_data_norm != 2):
dataset_old = (dataset_old - old_mean) / old_std
else:
dataset_old = (dataset_old - old_mean) / old_std + 0.5
raise ValueError(
"This part is not carefully checked. Please check it before you disable it."
)
if (len(old_models) > 0):
# convert dataset_old to numpy data array in case it is not
try:
dataset_old = dataset_old.to_numpy()
except:
try:
dataset_old = dataset_old.numpy()
except:
pass
pass
for model0 in old_models:
label_shift_amount = []
batch_size = int(config['MODEL']['BatchSize'])
print('run...', model0)
# use model.predict() will run it in the eager mode and evaluate the tensor properly.
for i0 in range(0, len(dataset_old), batch_size):
tmp_shift = model0.predict(
mrnn_utility.special_input_case(
dataset_old[i0:i0 + batch_size])
) / old_label_scale # numpy type
label_shift_amount.extend(tmp_shift)
for i0 in range(0, len(dataset[key0])):
a = dataset[key0][i0] - label_shift_amount[i0]
# tf1.13
if (i0 % 200 == 0):
print('--i0--', i0, 'DNS:', dataset[key0][i0], '\t', 'NN:',
label_shift_amount[i0], ' key0 = ', key0,
' dataset size = ', len(dataset[key0]),
' label shift size = ', len(label_shift_amount))
# for tf2.0
# print('--i0--',i0, 'DNS:', dataset[key0][i0],'\t', 'NN:',label_shift_amount[i0].numpy()[0], '\t', a.numpy()[0], '\t', abs(a.numpy()[0]/dataset[key0][i0])*100, 'new label', new_label[key0][i0])
dataset[key0][i0] = dataset[key0][i0] - label_shift_amount[i0]
