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.,