Python load_nnadl_dataset示例

示例#1

文件： test_fnn2.py 项目： mikbuch/pymri

import pickle

from pymri.dataset import load_nnadl_dataset

mode = 0

if mode == 0:
    path = '/home/jesmasta/amu/master/nifti/bold/'
    training_data, validation_data, test_data = load_nnadl_dataset(
        path,
        # (('ExeCtrl_0', 'ExeCtrl_5'), ('ExeTool_0', 'ExeTool_5')),
        (('ExeTool_0', 'ExeTool_5'), ('ExeCtrl_0', 'ExeCtrl_5')),
        k_features=784,
        normalize=True,
        scale_0_1=False,
        sizes=(0.5, 0.25, 0.25))

    pickle.dump([training_data, validation_data, test_data],
                open("/tmp/save.p", "wb"))

elif mode == 1:
    training_data, validation_data, test_data = pickle.load(
        open("/tmp/save.p", "rb"))


def perform(input_features=784):
    # from pymri.model import fnn
    # net = fnn.Network([input_features, 46, 2])
    # net.SGD(training_data, 100, 11, 2.961, test_data=test_data)

    import fnn2

示例#2

文件： test_fnn2.py 项目： mikbuch/pymri

import pickle

from pymri.dataset import load_nnadl_dataset


mode = 0


if mode == 0:
    path = '/home/jesmasta/amu/master/nifti/bold/'
    training_data, validation_data, test_data = load_nnadl_dataset(
        path,
        # (('ExeCtrl_0', 'ExeCtrl_5'), ('ExeTool_0', 'ExeTool_5')),
        (('ExeTool_0', 'ExeTool_5'), ('ExeCtrl_0', 'ExeCtrl_5')),
        k_features = 784,
        normalize=True,
        scale_0_1=False,
        sizes=(0.5, 0.25, 0.25)
        )

    pickle.dump(
        [training_data, validation_data, test_data],
        open("/tmp/save.p", "wb")
        )

elif mode == 1:
    training_data, validation_data, test_data = pickle.load(
        open("/tmp/save.p", "rb")
        )

def perform(input_features=784):

示例#3

文件： test_fnn.py 项目： mikbuch/pymri

import pickle

from pymri.dataset import load_nnadl_dataset


mode = 0


if mode == 0:
    path = "/home/jesmasta/amu/master/nifti/bold/"
    training_data, validation_data, test_data = load_nnadl_dataset(
        path,
        # (('ExeCtrl_0', 'ExeCtrl_5'), ('ExeTool_0', 'ExeTool_5')),
        (("ExeTool_0", "ExeTool_5"), ("ExeCtrl_0", "ExeCtrl_5")),
        sizes=(0.75, 0.25),
    )

    pickle.dump([training_data, validation_data, test_data], open("/tmp/save.p", "wb"))

elif mode == 1:
    training_data, validation_data, test_data = pickle.load(open("/tmp/save.p", "rb"))


def perform(input_features=784):
    from pymri.model import fnn

    # net = fnn.Network([784, 30, 2])
    # net.SGD(training_data, 100, 10, 3.0, test_data=test_data)
    net = fnn.Network([input_features, 46, 2])
    net.SGD(training_data, 100, 11, 2.961, test_data=test_data)
    return net

示例#4

import pickle

from pymri.dataset import load_nnadl_dataset


mode = 0


if mode == 0:
    path = '/home/jesmasta/amu/master/nifti/bold/'
    training_data, validation_data, test_data = load_nnadl_dataset(
        path,
        # (('ExeCtrl_0', 'ExeCtrl_5'), ('ExeTool_0', 'ExeTool_5')),
        (('ExeTool_0', 'ExeTool_5'), ('ExeCtrl_0', 'ExeCtrl_5')),
        k_features = 784,
        sizes=(0.75, 0.25)
        )

    pickle.dump(
        [training_data, validation_data, test_data],
        open("/tmp/save.p", "wb")
        )

elif mode == 1:
    training_data, validation_data, test_data = pickle.load(
        open("/tmp/save.p", "rb")
        )

def perform(input_features=784):
    from pymri.model import fnn
    # net = fnn.Network([784, 30, 2])