def exp_a(name): global source source_dict_copy = deepcopy(source_dict) source = RealApplianceSource(**source_dict_copy) source.subsample_target = 4 net_dict_copy = deepcopy(net_dict) net_dict_copy.update(dict(experiment_name=name, source=source)) net_dict_copy['layers_config'] = [ { 'type': BidirectionalRecurrentLayer, 'num_units': 25, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1.), 'nonlinearity': tanh }, { 'type': FeaturePoolLayer, 'ds': 2, # number of feature maps to be pooled together 'axis': 1, # pool over the time axis 'pool_function': T.mean }, { 'type': BidirectionalRecurrentLayer, 'num_units': 10, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1/sqrt(25)), 'nonlinearity': tanh }, { 'type': FeaturePoolLayer, 'ds': 2, # number of feature maps to be pooled together 'axis': 1, # pool over the time axis 'pool_function': T.mean }, { 'type': BidirectionalRecurrentLayer, 'num_units': 5, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1/sqrt(10)), 'nonlinearity': tanh }, { 'type': BidirectionalRecurrentLayer, 'num_units': 10, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1/sqrt(5)), 'nonlinearity': tanh }, { 'type': BidirectionalRecurrentLayer, 'num_units': 25, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1/sqrt(10)), 'nonlinearity': tanh }, { 'type': DenseLayer, 'num_units': source.n_outputs, 'nonlinearity': None, 'W': Normal(std=(1/sqrt(25))) } ] net = Net(**net_dict_copy) return net
def exp_a(name): global source source_dict_copy = deepcopy(source_dict) source = RealApplianceSource(**source_dict_copy) source.subsample_target = 4 net_dict_copy = deepcopy(net_dict) net_dict_copy.update(dict(experiment_name=name, source=source)) net_dict_copy['layers_config'] = [ { 'type': BidirectionalRecurrentLayer, 'num_units': 25, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1.), 'nonlinearity': tanh }, { 'type': FeaturePoolLayer, 'ds': 2, # number of feature maps to be pooled together 'axis': 1, # pool over the time axis 'pool_function': T.mean }, { 'type': BidirectionalRecurrentLayer, 'num_units': 10, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1 / sqrt(25)), 'nonlinearity': tanh }, { 'type': FeaturePoolLayer, 'ds': 2, # number of feature maps to be pooled together 'axis': 1, # pool over the time axis 'pool_function': T.mean }, { 'type': BidirectionalRecurrentLayer, 'num_units': 5, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1 / sqrt(10)), 'nonlinearity': tanh }, { 'type': BidirectionalRecurrentLayer, 'num_units': 10, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1 / sqrt(5)), 'nonlinearity': tanh }, { 'type': BidirectionalRecurrentLayer, 'num_units': 25, 'gradient_steps': GRADIENT_STEPS, 'W_in_to_hid': Normal(std=1 / sqrt(10)), 'nonlinearity': tanh }, { 'type': DenseLayer, 'num_units': source.n_outputs, 'nonlinearity': None, 'W': Normal(std=(1 / sqrt(25))) } ] net = Net(**net_dict_copy) return net