def lenet(lmdbData, lmdbLabel, batch_size):
n = NetSpec()
n.data = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdbData,
transform_param=dict(scale=1./255), ntop=1)
n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdbLabel,
transform_param=dict(scale=1./255), ntop=1)
n.conv1 = L.Convolution(n.data, kernel_size=4, num_output=200, weight_filler=dict(type='xavier'))
n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
n.conv2 = L.Convolution(n.pool1, kernel_size=3, num_output=50, weight_filler=dict(type='xavier'))
n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=1, pool=P.Pooling.MAX)
n.fc1 = L.InnerProduct(n.pool2, num_output=200, weight_filler=dict(type='xavier'))
n.relu1 = L.ReLU(n.fc1, in_place=True)
n.score = L.InnerProduct(n.relu1, num_output=1200, weight_filler=dict(type='xavier'))
n.loss = L.Python(n.score, n.label, module='pyloss', layer='EuclideanLossLayer')
return n.to_proto()
def deploy_tail(self, last_top):
n = NetSpec()
n.score = L.Softmax(bottom=last_top)
return n.to_proto()
