def initial_cifar():
# initial cifar net
cnn = ConvNet()
conv1_params = {
'HF': 5,
'WF': 5,
'DF': 3,
'NF': 32,
'stride': 1,
'pad': 2,
'var': 0.01
}
cnn.add_layer('conv', conv1_params)
pooling1_params = {'HF': 3, 'WF': 3, 'stride': 2, 'pad': [0, 1, 0, 1]}
cnn.add_layer('max_pooling', pooling1_params)
cnn.add_layer('relu', {})
conv2_params = {
'HF': 5,
'WF': 5,
'DF': 32,
'NF': 32,
'stride': 1,
'pad': 2,
'var': 0.02
}
cnn.add_layer('conv', conv2_params)
cnn.add_layer('relu', {})
pooling2_params = {'HF': 3, 'WF': 3, 'stride': 2, 'pad': [0, 1, 0, 1]}
cnn.add_layer('max_pooling', pooling2_params)
conv3_params = {
'HF': 5,
'WF': 5,
'DF': 32,
'NF': 64,
'stride': 1,
'pad': 2,
'var': 0.03
}
cnn.add_layer('conv', conv3_params)
cnn.add_layer('relu', {})
pooling3_params = {'HF': 3, 'WF': 3, 'stride': 2, 'pad': [0, 1, 0, 1]}
cnn.add_layer('max_pooling', pooling3_params)
conv4_params = {
'HF': 4,
'WF': 4,
'DF': 64,
'NF': 64,
'stride': 1,
'pad': 0,
'var': 0.04
}
cnn.add_layer('conv', conv4_params)
cnn.add_layer('relu', {})
conv5_params = {
'HF': 1,
'WF': 1,
'DF': 64,
'NF': 10,
'stride': 1,
'pad': 0,
'var': 0.05
}
cnn.add_layer('conv', conv5_params)
cnn.add_layer('softmax-loss', {})
return cnn
def initial_cifar():
# initial cifar net
cnn = ConvNet()
conv1_params = {'HF': 5, 'WF': 5, 'DF': 3, 'NF': 32, 'stride': 1, 'pad': 2, 'var': 0.01}
cnn.add_layer('conv', conv1_params)
pooling1_params = {'HF': 3, 'WF': 3, 'stride': 2, 'pad': [0, 1, 0, 1]}
cnn.add_layer('max_pooling', pooling1_params)
cnn.add_layer('relu', {})
conv2_params = {'HF': 5, 'WF': 5, 'DF': 32, 'NF': 32, 'stride': 1, 'pad': 2, 'var': 0.02}
cnn.add_layer('conv', conv2_params)
cnn.add_layer('relu', {})
pooling2_params = {'HF': 3, 'WF': 3, 'stride': 2, 'pad': [0, 1, 0, 1]}
cnn.add_layer('max_pooling', pooling2_params)
conv3_params = {'HF': 5, 'WF': 5, 'DF': 32, 'NF': 64, 'stride': 1, 'pad': 2, 'var': 0.02}
cnn.add_layer('conv', conv3_params)
cnn.add_layer('relu', {})
pooling3_params = {'HF': 3, 'WF': 3, 'stride': 2, 'pad': [0, 1, 0, 1]}
cnn.add_layer('max_pooling', pooling3_params)
conv4_params = {'HF': 4, 'WF': 4, 'DF': 64, 'NF': 64, 'stride': 1, 'pad': 0, 'var': 0.02}
cnn.add_layer('conv', conv4_params)
cnn.add_layer('relu', {})
conv5_params = {'HF': 1, 'WF': 1, 'DF': 64, 'NF': 10, 'stride': 1, 'pad': 0, 'var': 0.02}
cnn.add_layer('conv', conv5_params)
cnn.add_layer('softmax-loss', {})
return cnn
def initial_LeNet():
# initial LeNet
cnn = ConvNet()
conv1_params = {
'HF': 5,
'WF': 5,
'DF': 1,
'NF': 20,
'stride': 1,
'pad': 0,
'var': 0.01
}
cnn.add_layer('conv', conv1_params)
pooling1_params = {'HF': 2, 'WF': 2, 'stride': 2, 'pad': 0}
cnn.add_layer('max_pooling', pooling1_params)
conv2_params = {
'HF': 5,
'WF': 5,
'DF': 20,
'NF': 50,
'stride': 1,
'pad': 0,
'var': 0.01
}
cnn.add_layer('conv', conv2_params)
pooling2_params = {'HF': 2, 'WF': 2, 'stride': 2, 'pad': 0}
cnn.add_layer('max_pooling', pooling2_params)
conv3_params = {
'HF': 4,
'WF': 4,
'DF': 50,
'NF': 500,
'stride': 1,
'pad': 0,
'var': 0.01
}
cnn.add_layer('conv', conv3_params)
cnn.add_layer('relu', {})
conv4_params = {
'HF': 1,
'WF': 1,
'DF': 500,
'NF': 10,
'stride': 1,
'pad': 0,
'var': 0.01
}
cnn.add_layer('conv', conv4_params)
cnn.add_layer('softmax-loss', {})
return cnn
def initial_LeNet():
# initial LeNet
cnn = ConvNet()
conv1_params = {'HF': 5, 'WF': 5, 'DF': 1, 'NF': 20, 'stride': 1, 'pad': 0, 'var': 0.01}
cnn.add_layer('conv', conv1_params)
pooling1_params = {'HF': 2, 'WF': 2, 'stride': 2, 'pad': 0}
cnn.add_layer('max_pooling', pooling1_params)
conv2_params = {'HF': 5, 'WF': 5, 'DF': 20, 'NF': 50, 'stride': 1, 'pad': 0, 'var': 0.01}
cnn.add_layer('conv', conv2_params)
pooling2_params = {'HF': 2, 'WF': 2, 'stride': 2, 'pad': 0}
cnn.add_layer('max_pooling', pooling2_params)
conv3_params = {'HF': 4, 'WF': 4, 'DF': 50, 'NF': 500, 'stride': 1, 'pad': 0, 'var': 0.01}
cnn.add_layer('conv', conv3_params)
cnn.add_layer('relu', {})
conv4_params = {'HF': 1, 'WF': 1, 'DF': 500, 'NF': 10, 'stride': 1, 'pad': 0, 'var': 0.01}
cnn.add_layer('conv', conv4_params)
cnn.add_layer('softmax-loss', {})
return cnn
