Python close_save_fileの例

コード例 #1

ファイル: run_combined_data.py プロジェクト: ZijingMao/ROICNN

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)

コード例 #2

ファイル: test_single_model.py プロジェクト: jingxiachen/ROICNN

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)

コード例 #3

ファイル: gslt_model_binarylabel.py プロジェクト: AliMiraftab/ROICNN

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)

コード例 #4

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)

コード例 #5

ファイル: autorun_rsvp_cnn.py プロジェクト: jingxiachen/ROICNN

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)

コード例 #6

ファイル: autorun_rsvp_deconv_cnn.py プロジェクト: ZijingMao/ROICNN

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)

コード例 #7

ファイル: rsvp_deconv_cnn.py プロジェクト: ZijingMao/ROICNN

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)

コード例 #8

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)

コード例 #9

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)

コード例 #10

ファイル: run_combined_data.py プロジェクト: jingxiachen/ROICNN

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)

コード例 #11

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)