def __init__(self, X_train, Y_train, Net='LeNet5', opti='SGDMomentum'):
# Prepare Data: Load, Shuffle, Normalization, Batching, Preprocessing
self.X_train = X_train
self.Y_train = Y_train
self.batch_size = 64
# D_in: input depth of network, 784, 28*28 input grayscale image
self.D_in = 784
# D_out: output depth of network = 10, the 10 digits
self.D_out = 10
print(' Net: ' + str(Net))
print(' batch_size: ' + str(self.batch_size))
print(' D_in: ' + str(self.D_in))
print(' D_out: ' + str(self.D_out))
print(' Optimizer: ' + opti)
# =======================
if Net == 'TwoLayerNet':
# H is the size of the one hidden layer.
H = 400
self.model = ANN.TwoLayerNet(self.D_in, H, self.D_out)
elif Net == 'ThreeLayerNet':
#######################################
############ TODO ##################
#######################################
# H1, H2 are the size of the two hidden layers.
#self.model = ANN.ThreeLayerNet (self.D_in, H1, H2, self.D_out)
print('Not Implemented.')
exit(0)
elif Net == 'LeNet5':
self.model = CNN.LeNet5()
# store training loss over iterations, for later visualization
self.losses = []
if opti == 'SGD':
self.opti = optimizer.SGD(self.model.get_params(),
lr=0.0001,
reg=0)
else:
self.opti = optimizer.SGDMomentum(self.model.get_params(),
lr=0.0001,
momentum=0.80,
reg=0.00003)
self.criterion = loss.CrossEntropyLoss()
