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# coding: utf-8
from pylab import *
caffe_root = '/Users/wang/soft/caffe/' # this file should be run from {caffe_root}/examples (otherwise change this line)
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
from caffe import layers as L, params as P
def lenet(lmdb, batch_size):
n = caffe.NetSpec()
n.data, n.label = L.Data(
batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb,
transform_param=dict(scale=1. / 256),
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)
return n.to_proto()
my_lenet = lenet(caffe_root + 'examples/mnist/mnist_train_lmdb', 64)
print my_lenet
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