'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 = []
for initializer in initializers:
layers = [
MaxPool(size=2, stride=2),
Convolution((8, 3, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh, weight_initializer=initializer[0]),
MaxPool(size=2, stride=2),
Convolution((16, 8, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh, weight_initializer=initializer[1]),
MaxPool(size=2, stride=2),
Convolution((32, 16, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh, weight_initializer=initializer[2]),
MaxPool(size=2, stride=2),
ConvToFullyConnected(),
FullyConnected(size=64, activation=activation.tanh),
FullyConnected(size=10, activation=None, last_layer=True)
]
model = Model(
layers=layers,
num_classes=10,
optimizer=GDMomentumOptimizer(lr=1e-3, mu=0.9),
)
print("\n\n------------------------------------")
print("Initialize: {}".format(initializer))
print("\nRun training:\n------------------------------------")
from network import activation
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__':
"""
"""
freeze_support()
num_iteration = 20
data = dataset.cifar10_dataset.load()
layers = [
ConvToFullyConnected(),
FullyConnected(size=1000, activation=activation.tanh),
FullyConnected(size=10, activation=None, last_layer=True)
]
# -------------------------------------------------------
# Train with BP
# -------------------------------------------------------
model = Model(
layers=layers,
num_classes=10,
optimizer=GDMomentumOptimizer(lr=1e-3, mu=0.9),
)
print("\nRun training:\n------------------------------------")
