cfg = Config(config_file)
avg_file = Utils.avg_file_name(cfg.netFile)
if test == 'te':
tr = DataSet([cfg.te_data[0]], cfg, sub_sample=0.1)
else:
tr = DataSet([cfg.tr_data[0]], cfg)
tr.subtract_avg(avg_file, save_im=False)
inputs = {}
output = {}
cfg.Nout = 3
cfg.att = 4
cfg.ref_node = cfg.nodes[0][-1]
cfg.refs = np.ones(cfg.ref_node)
cfg.refs = cfg.refs.reshape((1, cfg.ref_node))
inputs[0] = tf.placeholder(tf.float32, [None, cfg.ref_node])
for a in range(cfg.feature_len):
inputs[a + 1] = tf.placeholder(tf.float32, [None, cfg.att])
input_dic = {}
for a in range(cfg.feature_len + 1):
input_dic['input_{}'.format(a)] = inputs[a]
net = rNet(input_dic)
net.real_setup(cfg, SIG=(cfg.SIG == 1))
#if len(sys.argv)>1:
# config_file = sys.argv[1]
test = None
if len(sys.argv) > 1:
test = sys.argv[1]
cfg = Config(config_file)
avg_file = avg_file_name(cfg.netFile)
if test is None:
tr = DataSet(cfg.tr_data, cfg)
get_avg_file(tr, avg_file)
te = DataSet(cfg.te_data, cfg, sub_sample=0.15)
tr0 = DataSet([cfg.tr_data[0]], cfg, sub_sample=0.1)
cfg.att = te.sz[1]
tr.avg_correction(avg_file)
tr0.avg_correction(avg_file)
else:
if test == 'te':
te = DataSet([cfg.te_data[0]], cfg)
else:
te = DataSet([cfg.tr_data[0]], cfg)
cfg.att = te.sz[1]
te.avg_correction(avg_file)
iterations = 10000
loop = cfg.loop
print "input attribute", cfg.att, "LR", cfg.lr, \
'feature', cfg.feature_len, 'add', cfg.add_len
cfg = Config(config_file)
avg_file = avg_file_name(cfg.netFile)
dt = 'te'
if len(sys.argv)>1:
dt = sys.argv[1]
if dt=='te':
te = DataSet(cfg.te_data, cfg)
else:
te = DataSet(cfg.tr_data, cfg)
cfg.att = te.att
te.avg_correction(avg_file)
print("input attribute", cfg.att, "LR", cfg.lr,
'feature', cfg.feature_len, 'add', cfg.add_len)
inputs = {}
Nout = cfg.num_output - cfg.num_output1
setattr(cfg, 'Nout', Nout)
# output = tf.placeholder(tf.float32, [None, cfg.num_output])
output = tf.placeholder(tf.float32, [None, Nout])
cfg.ref_node = cfg.nodes[0][-1]
cfg.refs = np.ones(cfg.ref_node) #(np.array(range(cfg.ref_node)) + 1.0)/cfg.ref_node - 0.5
