dataset.append(get_data.data_import(file_names_y, data_folder))
dataset.append(get_data.data_import(file_names_z, data_folder))
point_index = get_data.load_mapping_index(mapping_index)
kcc_dataset = get_data.data_import(kcc_files, kcc_folder)
input_conv_data, kcc_subset_dump, kpi_subset_dump = get_data.data_convert_voxel_mc(
vrm_system, dataset, point_index, kcc_dataset)
#Added Function to split KCCs to regression and classification
kcc_regression, kcc_classification = hy_util.split_kcc(kcc_subset_dump)
print('Building 3D CNN model')
output_dimension = assembly_kccs
dl_model_unet = Encode_Decode_Model(output_dimension)
model = dl_model_unet.resnet_3d_cnn_hybrid(voxel_dim, voxel_channels,
kcc_classification.shape[1])
print(model.summary())
#sys.exit()
print('Training 3D CNN model')
model_outputs = [kcc_regression, kcc_classification]
train_model = TrainModel(batch_size, epocs, split_ratio)
trained_model, accuracy_metrics_df_reg, accuracy_metrics_df_cla = train_model.run_train_model(
model, input_conv_data, model_outputs, model_path, logs_path,
plots_path, activate_tensorboard)
accuracy_metrics_df_reg.to_csv(logs_path + '/metrics_train_regression.csv')
print("KCC sub-listing: ", kcc_sublist)
#Check for KCC sub-listing
if (kcc_sublist != 0):
output_dimension = len(kcc_sublist)
else:
output_dimension = assembly_kccs
print("Process Parameter Dimension: ", output_dimension)
input_size = (voxel_dim, voxel_dim, voxel_dim, voxel_channels)
model_depth = cftrain.encode_decode_params['model_depth']
inital_filter_dim = cftrain.encode_decode_params['inital_filter_dim']
dl_model_unet = Encode_Decode_Model(output_dimension)
model = dl_model_unet.encode_decode_3d(inital_filter_dim, model_depth,
input_size, voxel_channels)
print(model.summary())
#sys.exit()
#importing file names for model input
input_file_names_x = config.encode_decode_construct['input_data_files_x']
input_file_names_y = config.encode_decode_construct['input_data_files_y']
input_file_names_z = config.encode_decode_construct['input_data_files_z']
test_input_file_names_x = config.encode_decode_construct[
'input_test_data_files_x']
test_input_file_names_y = config.encode_decode_construct[
'input_test_data_files_y']
print("KCC sub-listing: ", kcc_sublist)
#Check for KCC sub-listing
if (kcc_sublist != 0):
output_dimension = len(kcc_sublist)
else:
output_dimension = assembly_kccs
print("Process Parameter Dimension: ", output_dimension)
input_size = (voxel_dim, voxel_dim, voxel_dim, voxel_channels)
model_depth = cftrain.encode_decode_params['model_depth']
inital_filter_dim = cftrain.encode_decode_params['inital_filter_dim']
dl_model_unet = Encode_Decode_Model(output_dimension)
#changed to attention model
model = dl_model_unet.encode_decode_3d_multi_output_attention(
inital_filter_dim, model_depth, input_size, output_heads,
voxel_channels)
print(model.summary())
#sys.exit()
#importing file names for model input
input_file_names_x = config.encode_decode_construct['input_data_files_x']
input_file_names_y = config.encode_decode_construct['input_data_files_y']
input_file_names_z = config.encode_decode_construct['input_data_files_z']
test_input_file_names_x = config.encode_decode_construct[