[big] hidden_size = 10 """ from __future__ import division, print_function, unicode_literals from mlizard.caches import ShelveCache from mlizard.experiment import createExperiment from datasets import load_iris from sklearn.preprocessing import LabelBinarizer from neural_nets.connections import FullConnectionWithBias, SigmoidLayer from neural_nets.fann import FANN import matplotlib.pyplot as plt cache = ShelveCache("iris.shelve") ex = createExperiment("Iris", config_string=__doc__, cache=cache) @ex.stage def binarize_labels(y): lb = LabelBinarizer() return lb.fit_transform(y), lb @ex.stage def create_neural_network(in_size, hidden_size, out_size, rnd, logger): logger.info( "Creating a NN with {} inputs, {} hidden units, and {} output units.". format(in_size, hidden_size, out_size)) c0 = FullConnectionWithBias(in_size, hidden_size) s0 = SigmoidLayer(hidden_size)
# contain the configuration file. seed = 1234567 hidden_units = 10 iterations = 1000 learning_rate = 0.01 """ from __future__ import division, print_function, unicode_literals from mlizard.experiment import createExperiment from neural_nets.connections import FullConnectionWithBias, SigmoidLayer from neural_nets.fann import FANN from datasets import load_iris from sklearn.preprocessing import LabelBinarizer ex = createExperiment("demo", config_string=__doc__) @ex.stage def binarize_labels(y): lb = LabelBinarizer() return lb, lb.fit_transform(y) @ex.stage def build_nn(hidden_units): l0 = FullConnectionWithBias(4, hidden_units) s0 = SigmoidLayer(hidden_units) l1 = FullConnectionWithBias(hidden_units, 3) s1 = SigmoidLayer(3) return FANN([l0, s0, l1, s1])
# contain the configuration file. seed = 1234567 hidden_units = 10 iterations = 1000 learning_rate = 0.01 """ from __future__ import division, print_function, unicode_literals from mlizard.experiment import createExperiment from neural_nets.connections import FullConnectionWithBias, SigmoidLayer from neural_nets.fann import FANN from datasets import load_iris from sklearn.preprocessing import LabelBinarizer ex = createExperiment("demo", config_string=__doc__) @ex.stage def binarize_labels(y): lb = LabelBinarizer() return lb, lb.fit_transform(y) @ex.stage def build_nn(hidden_units): l0 = FullConnectionWithBias(4, hidden_units) s0 = SigmoidLayer(hidden_units) l1 = FullConnectionWithBias(hidden_units, 3) s1 = SigmoidLayer(3) return FANN([l0, s0, l1, s1]) @ex.stage
[big] hidden_size = 10 """ from __future__ import division, print_function, unicode_literals from mlizard.caches import ShelveCache from mlizard.experiment import createExperiment from datasets import load_iris from sklearn.preprocessing import LabelBinarizer from neural_nets.connections import FullConnectionWithBias, SigmoidLayer from neural_nets.fann import FANN import matplotlib.pyplot as plt cache = ShelveCache("iris.shelve") ex = createExperiment("Iris", config_string=__doc__, cache=cache) @ex.stage def binarize_labels(y): lb = LabelBinarizer() return lb.fit_transform(y), lb @ex.stage def create_neural_network(in_size, hidden_size, out_size, rnd, logger): logger.info("Creating a NN with {} inputs, {} hidden units, and {} output units.".format(in_size, hidden_size, out_size)) c0 = FullConnectionWithBias(in_size, hidden_size) s0 = SigmoidLayer(hidden_size) c1 = FullConnectionWithBias(hidden_size, out_size) s1 = SigmoidLayer(out_size) nn = FANN([c0, s0, c1, s1]) theta = rnd.randn(nn.get_param_dim())