def main(_):
# hyper_param_list = def_hyper_param()
hyper_param_list = [{'layer': 2, 'feat': [32, 64]}]
# {'layer': 1, 'feat': [128]},
# {'layer': 2, 'feat': [128, 8]},
# {'layer': 2, 'feat': [128, 16]},
# {'layer': 2, 'feat': [128, 32]},
#
# hyper_param_list = [{'layer': 3, 'feat': [4, 4, 512]},
# {'layer': 3, 'feat': [8, 8, 512]},
# {'layer': 3, 'feat': [4, 4, 256]},
# {'layer': 4, 'feat': [4, 4, 4, 512]},
# {'layer': 4, 'feat': [8, 8, 8, 512]},
# {'layer': 4, 'feat': [4, 4, 4, 256]},
# {'layer': 5, 'feat': [4, 4, 4, 4, 512]},
# {'layer': 5, 'feat': [8, 8, 8, 8, 512]},
# {'layer': 5, 'feat': [4, 4, 4, 4, 128]}]
models = [1]
for model in models:
for hyper_param in hyper_param_list:
print("Currently running model: "+str(model))
print("FeatMap: ")
print(hyper_param['feat'])
# for idx in range(3, len(roi_property.DAT_TYPE_STR)):
for idx in range(0, 5):
print("Data: " + roi_property.DAT_TYPE_STR[idx])
for subIdx in range(4, 5):
print("Subject: " + str(subIdx+1))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'], name_idx=idx, sub_idx=subIdx)
run_training(hyper_param, model, name_idx=idx, sub_idx=subIdx)
autorun_util.close_save_file(orig_stdout, f)
def main(_):
hyper_param_list = def_hyper_param()
# hyper_param_list = [{'layer': 2, 'feat': [64, 64]},
# {'layer': 3, 'feat': [64, 64, 64]},
# {'layer': 3, 'feat': [32, 16, 16]}]
# {'layer': 1, 'feat': [128]},
# {'layer': 2, 'feat': [128, 8]},
# {'layer': 2, 'feat': [128, 16]},
# {'layer': 2, 'feat': [128, 32]},
#
# hyper_param_list = [{'layer': 3, 'feat': [4, 4, 512]},
# {'layer': 3, 'feat': [8, 8, 512]},
# {'layer': 3, 'feat': [4, 4, 256]},
# {'layer': 4, 'feat': [4, 4, 4, 512]},
# {'layer': 4, 'feat': [8, 8, 8, 512]},
# {'layer': 4, 'feat': [4, 4, 4, 256]},
# {'layer': 5, 'feat': [4, 4, 4, 4, 512]},
# {'layer': 5, 'feat': [8, 8, 8, 8, 512]},
# {'layer': 5, 'feat': [4, 4, 4, 4, 128]}]
model = 7
for hyper_param in hyper_param_list:
print("Currently running: ")
print("FeatMap: ")
print(hyper_param['feat'])
print("Model" + str(model))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'])
run_training(hyper_param, model)
autorun_util.close_save_file(orig_stdout, f)
def main(_):
hyper_param_list = def_hyper_param()
for model in range(4, 11):
for hyper_param in hyper_param_list:
print("Currently running: ")
print("FeatMap: ")
print(hyper_param['feat'])
print("Model" + str(model))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'])
run_training(hyper_param, model)
autorun_util.close_save_file(orig_stdout, f)
def main(_):
hyper_param_list = def_hyper_param()
for model in range(0, 11):
for hyper_param in hyper_param_list:
print("Currently running: ")
print("FeatMap: ")
print(hyper_param['feat'])
print("Model" + str(model))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'])
run_training(hyper_param, model, isPool=False) # test on no pooling case
autorun_util.close_save_file(orig_stdout, f)
def main(_):
#hyper_param_list = def_hyper_param()
for model in range(0, 1):
#for hyper_param in hyper_param_list:
hyper_param = {'layer': 3, 'feat': [128, 128, 128]}
print("Currently running: ")
print("FeatMap: ")
print(hyper_param['feat'])
print("Model" + str(model))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'])
run_training(hyper_param, model)
autorun_util.close_save_file(orig_stdout, f)
def main(_):
#hyper_param_list = def_hyper_param()
hyper_param_list = [{'layer': 2, 'feat': [layer1_feat, layer2_feat]}]
#for model in range(0, 1):
#
model = autorun_deconv_lasso.DECONV_CVCNN
for hyper_param in hyper_param_list:
print("Currently running: ")
print("FeatMap: ")
print(hyper_param['feat'])
print("Model" + str(model))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'])
run_training(hyper_param, model)
autorun_util.close_save_file(orig_stdout, f)
def main(_):
#hyper_param_list = def_hyper_param()
hyper_param_list = [{'layer': 3, 'feat': [32, 32, 32]}]
#for model in range(0, 1):
#
model = autorun_deconv_lasso.DECONV_CVCNN
for hyper_param in hyper_param_list:
print("Currently running: ")
print("FeatMap: ")
print(hyper_param['feat'])
print("Model" + str(model))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'])
run_training(hyper_param, model, name_idx=6, sub_idx=167) # 'sub' and subject 12
autorun_util.close_save_file(orig_stdout, f)
def main(_):
#hyper_param_list = def_hyper_param()
hyper_param_list = [{'layer': 2, 'feat': [layer1_feat, layer2_feat]}]
#for model in range(0, 1):
#
model = autorun_deconv_lasso.DECONV_CVCNN
for hyper_param in hyper_param_list:
print("Currently running: ")
print("FeatMap: ")
print(hyper_param['feat'])
print("Model" + str(model))
orig_stdout, f = autorun_util.open_save_file(model,
hyper_param['feat'])
run_training(hyper_param, model)
autorun_util.close_save_file(orig_stdout, f)
def main(_):
#hyper_param_list = def_hyper_param()
#hyper_param_list = [{'layer': 3, 'feat': [32, 32, 32]}]
hyper_param_list = [{'layer': 2, 'feat': [32, 64]}]
#for model in range(0, 1):
#
model = autorun_deconv_lasso.DECONV_CVCNN
for hyper_param in hyper_param_list:
print("Currently running: ")
print("FeatMap: ")
print(hyper_param['feat'])
print("Model" + str(model))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'])
run_training(hyper_param, model, name_idx=6, sub_idx=0) # 'sub' and subject 12
autorun_util.close_save_file(orig_stdout, f)
def main(_):
# hyper_param_list = def_hyper_param()
hyper_param_list = [{'layer': 2, 'feat': [32, 64]}]
# {'layer': 1, 'feat': [128]},
# {'layer': 2, 'feat': [128, 8]},
# {'layer': 2, 'feat': [128, 16]},
# {'layer': 2, 'feat': [128, 32]},
#
# hyper_param_list = [{'layer': 3, 'feat': [4, 4, 512]},
# {'layer': 3, 'feat': [8, 8, 512]},
# {'layer': 3, 'feat': [4, 4, 256]},
# {'layer': 4, 'feat': [4, 4, 4, 512]},
# {'layer': 4, 'feat': [8, 8, 8, 512]},
# {'layer': 4, 'feat': [4, 4, 4, 256]},
# {'layer': 5, 'feat': [4, 4, 4, 4, 512]},
# {'layer': 5, 'feat': [8, 8, 8, 8, 512]},
# {'layer': 5, 'feat': [4, 4, 4, 4, 128]}]
models = [1]
for model in models:
for hyper_param in hyper_param_list:
print("Currently running model: " + str(model))
print("FeatMap: ")
print(hyper_param['feat'])
# for idx in range(3, len(roi_property.DAT_TYPE_STR)):
for idx in range(4, 5):
print("Data: " + roi_property.DAT_TYPE_STR[idx])
for subIdx in range(0, 1):
print("Subject: " + str(subIdx + 1))
orig_stdout, f = autorun_util.open_save_file(
model,
hyper_param['feat'],
name_idx=idx,
sub_idx=subIdx)
run_training(hyper_param,
model,
name_idx=idx,
sub_idx=subIdx)
autorun_util.close_save_file(orig_stdout, f)
def main(_):
# models = [1]
# test the DNN-CNN model
models = [1]
# hyper_param_list = def_hyper_param()
# hyper_param_list = [{'layer': 3, 'feat': [128, 32, 32]}]
hyper_param_list = [{'layer': 2, 'feat': [32, 64]}]
for model in models:
for hyper_param in hyper_param_list:
print("Currently running model: "+str(model))
print("FeatMap: ")
print(hyper_param['feat'])
# for idx in range(3, len(roi_property.DAT_TYPE_STR)):
for idx in range(4, 5):
print("Data: " + roi_property.DAT_TYPE_STR[idx])
# for subIdx in range(107, 108):
for subIdx in range(0, 1):
print("Subject: " + str(subIdx+1))
orig_stdout, f = autorun_util.open_save_file(model, hyper_param['feat'], name_idx=idx, sub_idx=subIdx)
run_training(hyper_param, model, name_idx=idx, sub_idx=subIdx)
autorun_util.close_save_file(orig_stdout, f)