def exp_g(name): global source try: a = source except NameError: source = RealApplianceSource(**source_dict) source.lag = 5 net_dict_copy = deepcopy(net_dict) net_dict_copy.update(dict(experiment_name=name, source=source)) net_dict_copy['layers_config'] = [ { 'type': LSTMLayer, 'num_units': 200, 'gradient_steps': GRADIENT_STEPS, 'peepholes': False, 'W_in_to_cell': Normal(std=1.) }, { 'type': DenseLayer, 'num_units': source.n_outputs, 'nonlinearity': None, 'W': Normal(std=(1/sqrt(200))) } ] net = Net(**net_dict_copy) return net
def exp_b(name): global source try: a = source except NameError: source = RealApplianceSource(**source_dict) source.lag = 5 net_dict_copy = deepcopy(net_dict) net_dict_copy.update(dict(experiment_name=name, source=source)) net_dict_copy['layers_config'].append({ 'type': DenseLayer, 'num_units': source.n_outputs, 'nonlinearity': None, 'W': Normal(std=(1 / sqrt(100))) }) net = Net(**net_dict_copy) return net
def exp_b(name): global source try: a = source except NameError: source = RealApplianceSource(**source_dict) source.lag = 5 net_dict_copy = deepcopy(net_dict) net_dict_copy.update(dict(experiment_name=name, source=source)) net_dict_copy['layers_config'].append( { 'type': DenseLayer, 'num_units': source.n_outputs, 'nonlinearity': None, 'W': Normal(std=(1/sqrt(100))) } ) net = Net(**net_dict_copy) return net
def exp_g(name): global source try: a = source except NameError: source = RealApplianceSource(**source_dict) source.lag = 5 net_dict_copy = deepcopy(net_dict) net_dict_copy.update(dict(experiment_name=name, source=source)) net_dict_copy['layers_config'] = [{ 'type': LSTMLayer, 'num_units': 200, 'gradient_steps': GRADIENT_STEPS, 'peepholes': False, 'W_in_to_cell': Normal(std=1.) }, { 'type': DenseLayer, 'num_units': source.n_outputs, 'nonlinearity': None, 'W': Normal(std=(1 / sqrt(200))) }] net = Net(**net_dict_copy) return net