def test_IFNNSR():
net = caffe.NetSpec()
net.data = L.Input(shape=dict(dim=[1, 1, 2 * depth + 1, 2 * depth + 1]),
ntop=1)
net.model = net.data
net.model = weight(net.model, share) # element-wise product
net.model = smooth(net.model, channel, group, kernel,
dilate) # convolution
net.model = L.TanH(net.model) # tanh actiavtion function
net.model = smooth(net.model, channel, group, kernel,
dilate) # convolution
net.model = L.TanH(net.model) # tanh actiavtion function
net.model = smooth(net.model, share, group, kernel, dilate) # convolution
net.model = L.TanH(net.model) # tanh actiavtion function
net.sum = net.model
for j in range(depth - 1):
net.model = weight(net.model, share)
net.model = smooth(net.model, channel, group, kernel, dilate)
net.model = L.TanH(net.model)
net.model = smooth(net.model, channel, group, kernel, dilate)
net.model = L.TanH(net.model)
net.model = smooth(net.model, share, group, kernel, dilate)
net.model = L.TanH(net.model)
net.sum = L.Eltwise(net.sum, net.model)
net.predict = weight(net.sum, share)
# net.loss = L.WeightL2Loss(net.predict, net.label)
# net.loss = L.EuclideanLoss(net.predict, net.label)
return net.to_proto()
def train_IFNNSR():
net = caffe.NetSpec()
net.data, net.label = L.HDF5Data(hdf5_data_param={
'source': train_data_path,
'batch_size': batch_size_train
},
include={'phase': caffe.TRAIN},
ntop=2)
train_data_layer = str(net.to_proto())
net.data, net.label = L.HDF5Data(hdf5_data_param={
'source': test_data_path,
'batch_size': batch_size_test
},
include={'phase': caffe.TEST},
ntop=2)
net.model = net.data
net.model = weight(net.model, share) # element-wise product
net.model = smooth(net.model, channel, group, kernel,
dilate) # convolution
net.model = L.TanH(net.model) # tanh actiavtion function
net.model = smooth(net.model, channel, group, kernel,
dilate) # convolution
net.model = L.TanH(net.model) # tanh actiavtion function
net.model = smooth(net.model, share, group, kernel, dilate) # convolution
net.model = L.TanH(net.model) # tanh actiavtion function
net.sum = net.model
for j in range(depth - 1):
net.model = weight(net.model, share)
net.model = smooth(net.model, channel, group, kernel, dilate)
net.model = L.TanH(net.model)
net.model = smooth(net.model, channel, group, kernel, dilate)
net.model = L.TanH(net.model)
net.model = smooth(net.model, share, group, kernel, dilate)
net.model = L.TanH(net.model)
net.sum = L.Eltwise(net.sum, net.model)
net.predict = weight(net.sum, share)
net.loss = L.WeightL2Loss(net.predict, net.label)
# net.loss = L.EuclideanLoss(net.predict, net.label)
return train_data_layer + str(net.to_proto())