def setUp(self): gen_data() self.data_dir = "system" coord = np.load(os.path.join(self.data_dir, "set.000/coord.npy")) box = np.load(os.path.join(self.data_dir, "set.000/box.npy")) self.atype = np.loadtxt(os.path.join(self.data_dir, "type.raw")) self.coord = np.vstack([coord, coord]) self.box = np.vstack([box, box]) self.freq = 10 self.pbtxts = [ os.path.join(tests_path, "infer/deeppot.pbtxt"), os.path.join(tests_path, "infer/deeppot-1.pbtxt") ] self.graph_dirs = [ pbtxt.replace("pbtxt", "pb") for pbtxt in self.pbtxts ] for pbtxt, pb in zip(self.pbtxts, self.graph_dirs): convert_pbtxt_to_pb(pbtxt, pb) self.graphs = [DeepPotential(pb) for pb in self.graph_dirs] self.output = os.path.join(tests_path, "model_devi.out") self.expect = np.array([ 0, 1.670048e-01, 4.182279e-04, 8.048649e-02, 5.095047e-01, 4.584241e-01, 4.819783e-01 ])
def op0(): """ generate lowest 15000 confident images :return: none """ data = common.Data("mnist/mnist_train/train_data.npy", "mnist/mnist_train/mnist_train_label", "mnist/mnist_test/test_data.npy", "mnist/mnist_test/mnist_test_label", 1, 28) res = common.predict('model/1.4.0', 60000, data.train_x, 28) common.gen_data(res, data.train_x, data.train_y_no_one_hot, 15000)
def setUp(self): gen_data()
import common as c import torch import torch.nn as nn import torchkeras as tk import torch.utils.data as tud epochs = 10000 LOAD = False TRAIN = True SAVE = False torch.manual_seed(0) x, y = c.gen_data() class NetWork3(tk.Model): def __init__(self): super(NetWork3, self).__init__() self.line = nn.Linear(1, 20) self.active = nn.ReLU() self.line2 = nn.Linear(20, 20) self.active2 = nn.ReLU() self.line3 = nn.Linear(20, 20) self.active3 = nn.ReLU() self.out = nn.Linear(20, 1) nn.init.normal_(self.line.weight) nn.init.normal_(self.line2.weight) nn.init.normal_(self.line3.weight) # for i in range(2000):
def setUp(self): gen_data(nframes=2)
def setUp(self) : gen_data() _make_tab(2)