def base(train_dp, valid_dp, logger, learning_rate): # learning_rate = 0.01 rng = numpy.random.RandomState([2016,02,26]) max_epochs = 1000 cost = CECost() stats = list() test_dp = deepcopy(valid_dp) train_dp.reset() valid_dp.reset() test_dp.reset() # NETWORK TOPOLOGY: model = MLP(cost=cost) model.add_layer(Relu(idim=125, odim=125, irange=1.6, rng=rng)) model.add_layer(Softmax(idim=125, odim=19, rng=rng)) # define the optimiser, here stochasitc gradient descent # with fixed learning rate and max_epochs lr_scheduler = LearningRateFixed( learning_rate=learning_rate, max_epochs=max_epochs) optimiser = SGDOptimiser(lr_scheduler=lr_scheduler) logger.info('Training started...') tr_stats_b, valid_stats_b = optimiser.train(model, train_dp, valid_dp) logger.info('Testing the model on test set:') tst_cost, tst_accuracy = optimiser.validate(model, test_dp) logger.info('ACL test set accuracy is %.2f %%, cost (%s) is %.3f' % (tst_accuracy*100., cost.get_name(), tst_cost))
fft=True, name='RLAx') for dt in pandas.date_range("2015-01-10", "2015-10-10"): print "date: " + str(dt) train_dp.reset() test_dp.reset() valid_dp.reset() rng = numpy.random.RandomState([dt.year, dt.month, dt.day]) # define the model structure, here just one linear layer # and mean square error cost cost = CECost() model = MLP(cost=cost) model.add_layer(ConvRelu_Opt(1, 1, rng=rng, stride=(1, 1))) model.add_layer(Sigmoid(idim=122, odim=122, rng=rng)) model.add_layer(Softmax(idim=122, odim=19, rng=rng)) #one can stack more layers here # print map(lambda x: (x.idim, x.odim), model.layers) lr_scheduler = LearningRateFixed(learning_rate=0.01, max_epochs=500) optimiser = SGDOptimiser(lr_scheduler=lr_scheduler) tr_stats, valid_stats = optimiser.train(model, train_dp, valid_dp) tst_cost, tst_accuracy = optimiser.validate(model, test_dp) seeds.append((tr_stats, valid_stats, (tst_cost, tst_accuracy))) end = time.time() print "scipy.correlate time: " + str(end - start)
logger.setLevel(logging.INFO) from mlp.layers import MLP, Sigmoid, Linear, Softmax #import required layer types from mlp.conv import ConvLinear, ConvRelu, ConvSigmoid from mlp.maxpooling import ConvMaxPool2D from mlp.optimisers import SGDOptimiser, Optimiser #import the optimiser from mlp.dataset import MNISTDataProvider #import data provider #Ruslan Burakov - s1569105 from mlp.costs import CECost, MSECost #import the cost we want to use for optimisation from mlp.schedulers import LearningRateFixed rng = numpy.random.RandomState([2015, 10, 10]) # define the model structure, here just one linear layer # and mean square error cost tsk8_1_cost = CECost() tsk8_1_model = MLP(cost=tsk8_1_cost) """ num_inp_feat_maps, num_out_feat_maps, image_shape=(28, 28), kernel_shape=(5, 5), stride=(1, 1), irange=0.2, rng=None, conv_fwd=my_conv_fwd, conv_bck=my_conv_bck, conv_grad=my_conv_grad) """ tsk8_1_model.add_layer( ConvSigmoid(num_inp_feat_maps=1, num_out_feat_maps=1,
logger.setLevel(logging.INFO) from mlp.layers import MLP, Sigmoid, Linear, Softmax, Relu #import required layer types from mlp.conv import ConvLinear, ConvRelu, ConvSigmoid from mlp.maxpooling import ConvMaxPool2D from mlp.optimisers import SGDOptimiser, Optimiser #import the optimiser from mlp.dataset import MNISTDataProvider #import data provider #Ruslan Burakov - s1569105 from mlp.costs import CECost, MSECost #import the cost we want to use for optimisation from mlp.schedulers import LearningRateFixed rng = numpy.random.RandomState([2015, 10, 10]) # define the model structure, here just one linear layer # and mean square error cost cost = CECost() tsk8_2_model = MLP(cost=cost) """ num_inp_feat_maps, num_out_feat_maps, image_shape=(28, 28), kernel_shape=(5, 5), stride=(1, 1), irange=0.2, rng=None, conv_fwd=my_conv_fwd, conv_bck=my_conv_bck, conv_grad=my_conv_grad) """ tsk8_2_model.add_layer( ConvRelu(num_inp_feat_maps=1, num_out_feat_maps=5,
from mlp.layers import MLP, Sigmoid, Linear, Softmax, Relu #import required layer types from mlp.conv import ConvLinear, ConvRelu, ConvSigmoid from mlp.maxpooling import ConvMaxPool2D from mlp.optimisers import SGDOptimiser, Optimiser#import the optimiser from mlp.dataset import MNISTDataProvider #import data provider #Ruslan Burakov - s1569105 from mlp.costs import CECost, MSECost #import the cost we want to use for optimisation from mlp.schedulers import LearningRateFixed rng = numpy.random.RandomState([2015,10,10]) # define the model structure, here just one linear layer # and mean square error cost cost = CECost() tsk8_1_1_model = MLP(cost=cost) """ num_inp_feat_maps, num_out_feat_maps, image_shape=(28, 28), kernel_shape=(5, 5), stride=(1, 1), irange=0.2, rng=None, conv_fwd=my_conv_fwd, conv_bck=my_conv_bck, conv_grad=my_conv_grad) """ tsk8_1_1_model.add_layer(ConvSigmoid(num_inp_feat_maps=1, num_out_feat_maps=1, image_shape=(28,28),