def nielson_3layer_300rows(): labeled_data = mnist.MnistDataset.from_pickled_file(30) config = network_config.NetworkConfig() config.neuron_counts = [784, 30, 10] net = nn.Network(config) net.train(labeled_data.train, labeled_data.validate)
def invgrad_3layer(): labeled_data = mnist.MnistDataset.from_pickled_file() config = network_config.NetworkConfig() config.param_update_c = param_update_fns.InvGradParamUpdate() config.neuron_counts = [784, 30, 10] net = nn.Network(config) net.train(labeled_data.train, labeled_data.validate)
def neilson_3layer_200rows_100epochs(): labeled_data = mnist.MnistDataset.from_pickled_file(20) config = network_config.NetworkConfig() config.neuron_counts = [784, 30, 10] config.epochs = 100 net = nn.Network(config) net.train(labeled_data.train, labeled_data.validate)
def delta_boosted_3layer_300rows(): labeled_data = mnist.MnistDataset.from_pickled_file(30) config = network_config.NetworkConfig() config.neuron_counts = [784, 30, 10] config.delta_boost = 4 # config.eta = 1 net = nn.Network(config) net.train(labeled_data.train, labeled_data.validate)
def neilson_5layer_200rows_100epochs(): labeled_data = mnist.MnistDataset.from_pickled_file(20) config = network_config.NetworkConfig() config.init_c = init_fns.LeCunNormalInit() # config.activation_c = activation_fns.ReLUActivation() config.neuron_counts = [784, 30, 20, 15, 10] net = nn.Network(config) net.train(labeled_data.train, labeled_data.validate)
def sq_grad_3layer_300rows(): labeled_data = mnist.MnistDataset.from_pickled_file(30, True) config = network_config.NetworkConfig() config.neuron_counts = [784, 30, 10] config.param_update_c = param_update_fns.SquaredParamUpdate() config.eta = 18 config.epochs = 100 net = nn.Network(config) net.train(labeled_data.train, labeled_data.test)
def twin_3layer_300rows(): labeled_data = mnist.MnistDataset.from_pickled_file(30) config = network_config.NetworkConfig() config.batch_size = 3 config.epochs = 3000000 config.eta = 0.003 config.neuron_counts = [784, 30, 10] net = tnn.TwinNetwork(config) net.train(labeled_data.train, labeled_data.validate)
def delta_unboosted_5layer_300rows(): labeled_data = mnist.MnistDataset.from_pickled_file(30) config = network_config.NetworkConfig() #config.init_c = init_fns.LeCunNormalInit() # config.activation_c = activation_fns.ReLUActivation() config.neuron_counts = [784, 30, 20, 15, 10] config.eta = 0.1 config.delta_boost = 1 net = nn.Network(config) net.train(labeled_data.train, labeled_data.validate)
def nielson_6layer_full_data(): labeled_data = mnist.MnistDataset.from_pickled_file(30) config = network_config.NetworkConfig() config.init_c = init_fns.LeCunNormalInit() #config.loss_c = loss_fns.QuadraticLoss() #config.activation_c = activation_fns.SigmoidActivation() config.neuron_counts = [784, 30, 30, 30, 30, 10] config.eta = 0.001 config.lmbda = 0.025 config.epochs = 500 net = nn.Network(config) net.train(labeled_data.train, labeled_data.validate)
def linear_data_classical(): def get_data(count): inputs = [ np.reshape(k, (1, 1)) for k in np.random.uniform(-5, 5, count) ] outputs = [[[0], [1]] if k[0] < 0 else [[1], [0]] for k in inputs] data = list(zip(inputs, outputs, range(len(inputs)))) return data dataset = ld.LabeledData() dataset.train = get_data(10) dataset.test = get_data(100) dataset.validate = get_data(100) config = network_config.NetworkConfig() #config.param_update_c = param_update_fns.InvGradParamUpdate() config.neuron_counts = [1, 3, 5, 2] config.epochs = 50 net = nn.Network(config) net.train(dataset)