model_def2.py 文件源码

def LeNet(lmdb, batch_size):
  # our version of LeNet: a series of linear and simple nonlinear transformations
  n = caffe.NetSpec()

  n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,
                           transform_param=dict(scale=1./255), ntop=2)

  n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, 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=5, num_output=50, weight_filler=dict(type='xavier'))
  n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
  n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))
  n.relu1 = L.ReLU(n.fc1, in_place=True)
  n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))
  n.loss =  L.SoftmaxWithLoss(n.score, n.label)

  proto = n.to_proto()
  proto.name = 'LeNet'
  return proto