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)
