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)