if __name__ == '__main__':
freeze_support()
num_hidden_units = 500
num_hidden_layers = 5
num_passes = 30
# data = dataset.mnist_dataset.load('dataset/mnist')
data = dataset.cifar10_dataset.load()
initializers = [
weight_initializer.Fill(0),
weight_initializer.Fill(1e-3),
weight_initializer.Fill(1),
weight_initializer.RandomUniform(-1, 1),
weight_initializer.RandomUniform(-1/np.sqrt(num_hidden_units), 1/np.sqrt(num_hidden_units)),
weight_initializer.RandomUniform(-1/num_hidden_units, 1/num_hidden_units),
weight_initializer.RandomNormal(1, 0),
weight_initializer.RandomNormal(1 / np.sqrt(num_hidden_units))
]
labels = [
'Fill(0)',
'Fill(0.001)',
'Fill(1)',
'Uniform(low=-1, high=1)',
'Uniform(low=-1/sqrt(fan_out), high=1/sqrt(fan_out))',
'Uniform(low=-1/fan_out, high=1/fan_out)',
'Normal(sigma=1, mu=0)',
'Normal(sigma=1/sqrt(fan_out), mu=0)',
from network.optimizer import GDMomentumOptimizer
if __name__ == '__main__':
freeze_support()
data = dataset.cifar10_dataset.load()
num_passes = 30
initializers = [[], [], [], [], [], [], []]
for i in [8*16*16, 16*8*8, 32*4*4]:
initializers[0].append(weight_initializer.Fill(0)),
initializers[1].append(weight_initializer.Fill(1e-3)),
initializers[2].append(weight_initializer.Fill(1)),
initializers[3].append(weight_initializer.RandomUniform(-1, 1))
initializers[4].append(weight_initializer.RandomUniform(-1/np.sqrt(i), 1/np.sqrt( i)))
initializers[5].append(weight_initializer.RandomNormal())
initializers[6].append(weight_initializer.RandomNormal(1/np.sqrt(i)))
labels = [
'Fill(0)',
'Fill(0.001)',
'Fill(1)',
'Uniform(low=-1, high=1)',
'Uniform(low=-1/sqrt(fan_out), high=1/sqrt(fan_out))',
'Normal(sigma=1, mu=0)',
'Normal(sigma=1/sqrt(fan_out), mu=0)',
]
statistics = []
from network.layers.conv_to_fully_connected import ConvToFullyConnected
from network.layers.fully_connected import FullyConnected
from network.model import Model
from network.optimizer import GDMomentumOptimizer
if __name__ == '__main__':
"""
Goal: Compare
"""
freeze_support()
num_hidden_units = 240
initializers = [
weight_initializer.RandomUniform(-1, 1),
weight_initializer.RandomUniform(-100, 100),
]
lrs = [1e-2, 1e-4]
statistics = []
for initializer, lr in zip(initializers, lrs):
layers = [
ConvToFullyConnected(),
FullyConnected(size=num_hidden_units,
activation=activation.tanh,
fb_weight_initializer=initializer),
FullyConnected(size=num_hidden_units,
activation=activation.tanh,