我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用lasagne.updates.adadelta()。
def extract_encoder(dbn): dbn_layers = dbn.get_all_layers() encoder = NeuralNet( layers=[ (InputLayer, {'name': 'input', 'shape': dbn_layers[0].shape}), (DenseLayer, {'name': 'l1', 'num_units': dbn_layers[1].num_units, 'nonlinearity': sigmoid, 'W': dbn_layers[1].W, 'b': dbn_layers[1].b}), (DenseLayer, {'name': 'l2', 'num_units': dbn_layers[2].num_units, 'nonlinearity': sigmoid, 'W': dbn_layers[2].W, 'b': dbn_layers[2].b}), (DenseLayer, {'name': 'l3', 'num_units': dbn_layers[3].num_units, 'nonlinearity': sigmoid, 'W': dbn_layers[3].W, 'b': dbn_layers[3].b}), (DenseLayer, {'name': 'l4', 'num_units': dbn_layers[4].num_units, 'nonlinearity': linear, 'W': dbn_layers[4].W, 'b': dbn_layers[4].b}), ], update=adadelta, update_learning_rate=0.01, objective_l2=0.005, verbose=1, regression=True ) encoder.initialize() return encoder
def get_updates(nnet, train_obj, trainable_params): implemented_solvers = ("nesterov", "adagrad", "adadelta", "adam") if not hasattr(nnet, "solver") or nnet.solver not in implemented_solvers: nnet.sgd_solver = "nesterov" else: nnet.sgd_solver = nnet.solver if nnet.sgd_solver == "nesterov": updates = l_updates.nesterov_momentum(train_obj, trainable_params, learning_rate=Cfg.learning_rate, momentum=0.9) elif nnet.sgd_solver == "adagrad": updates = l_updates.adagrad(train_obj, trainable_params, learning_rate=Cfg.learning_rate) elif nnet.sgd_solver == "adadelta": updates = l_updates.adadelta(train_obj, trainable_params, learning_rate=Cfg.learning_rate) elif nnet.sgd_solver == "adam": updates = l_updates.adam(train_obj, trainable_params, learning_rate=Cfg.learning_rate) return updates
def create_decoder(input_size, decode_size, weights): decoder_layers = [ (InputLayer, {'shape': (None, input_size)}), (DenseLayer, {'name': 'decoder', 'num_units': decode_size, 'nonlinearity': linear, 'W': weights}) ] decoder = NeuralNet( layers=decoder_layers, max_epochs=50, objective_loss_function=squared_error, update=adadelta, regression=True, verbose=1 ) return decoder
def load_dbn(path='models/oulu_ae.mat'): """ load a pretrained dbn from path :param path: path to the .mat dbn :return: pretrained deep belief network """ # create the network using weights from pretrain_nn.mat nn = sio.loadmat(path) w1 = nn['w1'] w2 = nn['w2'] w3 = nn['w3'] w4 = nn['w4'] w5 = nn['w5'] w6 = nn['w6'] w7 = nn['w7'] w8 = nn['w8'] b1 = nn['b1'][0] b2 = nn['b2'][0] b3 = nn['b3'][0] b4 = nn['b4'][0] b5 = nn['b5'][0] b6 = nn['b6'][0] b7 = nn['b7'][0] b8 = nn['b8'][0] layers = [ (InputLayer, {'name': 'input', 'shape': (None, 1144)}), (DenseLayer, {'name': 'l1', 'num_units': 2000, 'nonlinearity': sigmoid, 'W': w1, 'b': b1}), (DenseLayer, {'name': 'l2', 'num_units': 1000, 'nonlinearity': sigmoid, 'W': w2, 'b': b2}), (DenseLayer, {'name': 'l3', 'num_units': 500, 'nonlinearity': sigmoid, 'W': w3, 'b': b3}), (DenseLayer, {'name': 'l4', 'num_units': 50, 'nonlinearity': linear, 'W': w4, 'b': b4}), (DenseLayer, {'name': 'l5', 'num_units': 500, 'nonlinearity': sigmoid, 'W': w5, 'b': b5}), (DenseLayer, {'name': 'l6', 'num_units': 1000, 'nonlinearity': sigmoid, 'W': w6, 'b': b6}), (DenseLayer, {'name': 'l7', 'num_units': 2000, 'nonlinearity': sigmoid, 'W': w7, 'b': b7}), (DenseLayer, {'name': 'output', 'num_units': 1144, 'nonlinearity': linear, 'W': w8, 'b': b8}), ] dbn = NeuralNet( layers=layers, max_epochs=10, objective_loss_function=squared_error, update=adadelta, regression=True, verbose=1, update_learning_rate=0.01, objective_l2=0.005, ) return dbn
def load_dbn(path='models/avletters_ae.mat'): """ load a pretrained dbn from path :param path: path to the .mat dbn :return: pretrained deep belief network """ # create the network using weights from pretrain_nn.mat nn = sio.loadmat(path) w1 = nn['w1'].astype('float32') w2 = nn['w2'].astype('float32') w3 = nn['w3'].astype('float32') w4 = nn['w4'].astype('float32') w5 = nn['w5'].astype('float32') w6 = nn['w6'].astype('float32') w7 = nn['w7'].astype('float32') w8 = nn['w8'].astype('float32') b1 = nn['b1'][0].astype('float32') b2 = nn['b2'][0].astype('float32') b3 = nn['b3'][0].astype('float32') b4 = nn['b4'][0].astype('float32') b5 = nn['b5'][0].astype('float32') b6 = nn['b6'][0].astype('float32') b7 = nn['b7'][0].astype('float32') b8 = nn['b8'][0].astype('float32') layers = [ (InputLayer, {'name': 'input', 'shape': (None, 1200)}), (DenseLayer, {'name': 'l1', 'num_units': 2000, 'nonlinearity': sigmoid, 'W': w1, 'b': b1}), (DenseLayer, {'name': 'l2', 'num_units': 1000, 'nonlinearity': sigmoid, 'W': w2, 'b': b2}), (DenseLayer, {'name': 'l3', 'num_units': 500, 'nonlinearity': sigmoid, 'W': w3, 'b': b3}), (DenseLayer, {'name': 'l4', 'num_units': 50, 'nonlinearity': linear, 'W': w4, 'b': b4}), (DenseLayer, {'name': 'l5', 'num_units': 500, 'nonlinearity': sigmoid, 'W': w5, 'b': b5}), (DenseLayer, {'name': 'l6', 'num_units': 1000, 'nonlinearity': sigmoid, 'W': w6, 'b': b6}), (DenseLayer, {'name': 'l7', 'num_units': 2000, 'nonlinearity': sigmoid, 'W': w7, 'b': b7}), (DenseLayer, {'name': 'output', 'num_units': 1200, 'nonlinearity': linear, 'W': w8, 'b': b8}), ] dbn = NeuralNet( layers=layers, max_epochs=10, objective_loss_function=squared_error, update=adadelta, regression=True, verbose=1, update_learning_rate=0.01, # update_learning_rate=0.001, # update_momentum=0.05, objective_l2=0.005, ) return dbn
def parse_options(): options = dict() options['config'] = 'config/bimodal.ini' options['no_plot'] = False parser = argparse.ArgumentParser() parser.add_argument('--config', help='config file to use, default=config/bimodal.ini') parser.add_argument('--write_results', help='write results to file') parser.add_argument('--update_rule', help='adadelta, sgdm, sgdnm, adam') parser.add_argument('--learning_rate', help='learning rate') parser.add_argument('--decay_rate', help='learning rate decay') parser.add_argument('--momentum', help='momentum') parser.add_argument('--momentum_schedule', help='eg: 0.9,0.9,0.95,0.99') parser.add_argument('--validation_window', help='validation window length, eg: 6') parser.add_argument('--t1', help='epoch to start learning rate decay, eg: 10') parser.add_argument('--weight_init', help='norm,glorot,ortho,uniform') parser.add_argument('--num_epoch', help='number of epochs to run') parser.add_argument('--use_peepholes', help='use peephole connections in LSTM') parser.add_argument('--no_plot', dest='no_plot', action='store_true', help='disable plots') parser.set_defaults(no_plot=False) parser.set_defaults(use_peepholes=False) args = parser.parse_args() if args.config: options['config'] = args.config if args.write_results: options['write_results'] = args.write_results if args.update_rule: options['update_rule'] = args.update_rule if args.learning_rate: options['learning_rate'] = args.learning_rate if args.decay_rate: options['decay_rate'] = args.decay_rate if args.momentum: options['momentum'] = args.momentum if args.momentum_schedule: options['momentum_schedule'] = args.momentum_schedule if args.validation_window: options['validation_window'] = args.validation_window if args.t1: options['t1'] = args.t1 if args.weight_init: options['weight_init'] = args.weight_init if args.num_epoch: options['num_epoch'] = args.num_epoch if args.no_plot: options['no_plot'] = True if args.use_peepholes: options['use_peepholes'] = True return options
def load_dbn(path='models/cuave_ae.mat'): """ load a pretrained dbn from path :param path: path to the .mat dbn :return: pretrained deep belief network """ # create the network using weights from pretrain_nn.mat nn = sio.loadmat(path) w1 = nn['w1'] w2 = nn['w2'] w3 = nn['w3'] w4 = nn['w4'] w5 = nn['w5'] w6 = nn['w6'] w7 = nn['w7'] w8 = nn['w8'] b1 = nn['b1'][0] b2 = nn['b2'][0] b3 = nn['b3'][0] b4 = nn['b4'][0] b5 = nn['b5'][0] b6 = nn['b6'][0] b7 = nn['b7'][0] b8 = nn['b8'][0] layers = [ (InputLayer, {'name': 'input', 'shape': (None, 1500)}), (DenseLayer, {'name': 'l1', 'num_units': 2000, 'nonlinearity': sigmoid, 'W': w1, 'b': b1}), (DenseLayer, {'name': 'l2', 'num_units': 1000, 'nonlinearity': sigmoid, 'W': w2, 'b': b2}), (DenseLayer, {'name': 'l3', 'num_units': 500, 'nonlinearity': sigmoid, 'W': w3, 'b': b3}), (DenseLayer, {'name': 'l4', 'num_units': 50, 'nonlinearity': linear, 'W': w4, 'b': b4}), (DenseLayer, {'name': 'l5', 'num_units': 500, 'nonlinearity': sigmoid, 'W': w5, 'b': b5}), (DenseLayer, {'name': 'l6', 'num_units': 1000, 'nonlinearity': sigmoid, 'W': w6, 'b': b6}), (DenseLayer, {'name': 'l7', 'num_units': 2000, 'nonlinearity': sigmoid, 'W': w7, 'b': b7}), (DenseLayer, {'name': 'output', 'num_units': 1144, 'nonlinearity': linear, 'W': w8, 'b': b8}), ] dbn = NeuralNet( layers=layers, max_epochs=10, objective_loss_function=squared_error, update=adadelta, regression=True, verbose=1, update_learning_rate=0.01, objective_l2=0.005, ) return dbn
def load_ae_encoder(path, nonlinearity=sigmoid): nn = sio.loadmat(path) w1 = nn['w1'] w2 = nn['w2'] w3 = nn['w3'] w4 = nn['w4'] b1 = nn['b1'][0] b2 = nn['b2'][0] b3 = nn['b3'][0] b4 = nn['b4'][0] layers = [ (InputLayer, {'name': 'input', 'shape': (None, 1500)}), (DenseLayer, {'name': 'l1', 'num_units': 2000, 'nonlinearity': nonlinearity, 'W': w1, 'b': b1}), (DenseLayer, {'name': 'l2', 'num_units': 1000, 'nonlinearity': nonlinearity, 'W': w2, 'b': b2}), (DenseLayer, {'name': 'l3', 'num_units': 500, 'nonlinearity': nonlinearity, 'W': w3, 'b': b3}), (DenseLayer, {'name': 'l4', 'num_units': 50, 'nonlinearity': linear, 'W': w4, 'b': b4}) ] ''' dbn = NeuralNet( layers=layers, max_epochs=30, objective_loss_function=squared_error, update=nesterov_momentum, regression=True, verbose=1, update_learning_rate=0.001, update_momentum=0.05, objective_l2=0.005, ) ''' dbn = NeuralNet( layers=layers, max_epochs=10, objective_loss_function=squared_error, update=adadelta, regression=True, verbose=1, update_learning_rate=0.01, # update_learning_rate=0.001, # update_momentum=0.05, objective_l2=0.005, ) return dbn
def build_instrument_model(self, n_vars, **kwargs): targets = TT.vector() instrument_vars = TT.matrix() instruments = layers.InputLayer((None, n_vars), instrument_vars) instruments = layers.DropoutLayer(instruments, p=0.2) dense_layer = layers.DenseLayer(instruments, kwargs['dense_size'], nonlinearity=nonlinearities.tanh) dense_layer = layers.DropoutLayer(dense_layer, p=0.2) for _ in xrange(kwargs['n_dense_layers'] - 1): dense_layer = layers.DenseLayer(dense_layer, kwargs['dense_size'], nonlinearity=nonlinearities.tanh) dense_layer = layers.DropoutLayer(dense_layer, p=0.5) self.instrument_output = layers.DenseLayer(dense_layer, 1, nonlinearity=nonlinearities.linear) init_params = layers.get_all_param_values(self.instrument_output) prediction = layers.get_output(self.instrument_output, deterministic=False) test_prediction = layers.get_output(self.instrument_output, deterministic=True) # flexible here, endog variable can be categorical, continuous, etc. l2_cost = regularization.regularize_network_params(self.instrument_output, regularization.l2) loss = objectives.squared_error(prediction.flatten(), targets.flatten()).mean() + 1e-4 * l2_cost loss_total = objectives.squared_error(prediction.flatten(), targets.flatten()).mean() params = layers.get_all_params(self.instrument_output, trainable=True) param_updates = updates.adadelta(loss, params) self._instrument_train_fn = theano.function( [ targets, instrument_vars, ], loss, updates=param_updates ) self._instrument_loss_fn = theano.function( [ targets, instrument_vars, ], loss_total ) self._instrument_output_fn = theano.function([instrument_vars], test_prediction) return init_params
def build_treatment_model(self, n_vars, **kwargs): input_vars = TT.matrix() instrument_vars = TT.matrix() targets = TT.vector() inputs = layers.InputLayer((None, n_vars), input_vars) inputs = layers.DropoutLayer(inputs, p=0.2) dense_layer = layers.DenseLayer(inputs, 2 * kwargs['dense_size'], nonlinearity=nonlinearities.rectify) dense_layer = layers.batch_norm(dense_layer) dense_layer= layers.DropoutLayer(dense_layer, p=0.2) for _ in xrange(kwargs['n_dense_layers'] - 1): dense_layer = layers.DenseLayer(dense_layer, kwargs['dense_size'], nonlinearity=nonlinearities.rectify) dense_layer = layers.batch_norm(dense_layer) self.treatment_output = layers.DenseLayer(dense_layer, 1, nonlinearity=nonlinearities.linear) init_params = layers.get_all_param_values(self.treatment_output) prediction = layers.get_output(self.treatment_output, deterministic=False) test_prediction = layers.get_output(self.treatment_output, deterministic=True) l2_cost = regularization.regularize_network_params(self.treatment_output, regularization.l2) loss = gmm_loss(prediction, targets, instrument_vars) + 1e-4 * l2_cost params = layers.get_all_params(self.treatment_output, trainable=True) param_updates = updates.adadelta(loss, params) self._train_fn = theano.function( [ input_vars, targets, instrument_vars, ], loss, updates=param_updates ) self._loss_fn = theano.function( [ input_vars, targets, instrument_vars, ], loss, ) self._output_fn = theano.function( [ input_vars, ], test_prediction, ) return init_params
