def setUp(self):
self.tm = training_manager.TrainingManager()
self.tm.N_PACKAGES = 1
self.PROJECT_DIR = tb.setup_module()
# load in spm_data
data_path = self.PROJECT_DIR + "/dcm_rnn/resources/template0.pkl"
self.du = tb.load_template(data_path)
self.dr = tfm.DcmRnn()
self.dr.collect_parameters(self.du)
self.tm.prepare_dcm_rnn(self.dr, tag='initializer')
# STEP_SIZE = 0.002 # for 32
# STEP_SIZE = 0.5
# STEP_SIZE = 0.001 # for 64
# STEP_SIZE = 0.001 # 128
# STEP_SIZE = 0.0005 # for 256
STEP_SIZE = 1e-5
# DATA_SHIFT = int(N_RECURRENT_STEP / 4)
DATA_SHIFT = 1
LEARNING_RATE = 0.01 / N_RECURRENT_STEP
print(os.getcwd())
PROJECT_DIR = '/Users/yuanwang/Google_Drive/projects/Gits/DCM_RNN'
data_path = PROJECT_DIR + "/dcm_rnn/resources/template0.pkl"
du = tb.load_template(data_path)
dr = tfm.DcmRnn()
dr.collect_parameters(du)
dr.learning_rate = LEARNING_RATE
dr.shift_data = DATA_SHIFT
dr.n_recurrent_step = N_RECURRENT_STEP
neural_parameter_initial = {
'A': du.get('A'),
'B': du.get('B'),
'C': du.get('C')
}
dr.loss_weighting = {
'prediction': 1.,
'sparsity': 0.1,
'prior': 10,
'Wxx': 1.,
'Wxxu': 1.,
