--- /dev/null
+from caffe import layers as L, params as P, to_proto
+from caffe.proto import caffe_pb2
+
+# helper function for common structures
+
+def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
+ conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+ num_output=nout, pad=pad, group=group)
+ return conv, L.ReLU(conv, in_place=True)
+
+def fc_relu(bottom, nout):
+ fc = L.InnerProduct(bottom, num_output=nout)
+ return fc, L.ReLU(fc, in_place=True)
+
+def max_pool(bottom, ks, stride=1):
+ return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def caffenet(lmdb, batch_size=256, include_acc=False):
+ data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
+ transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
+
+ # the net itself
+ conv1, relu1 = conv_relu(data, 11, 96, stride=4)
+ pool1 = max_pool(relu1, 3, stride=2)
+ norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
+ conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
+ pool2 = max_pool(relu2, 3, stride=2)
+ norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
+ conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
+ conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
+ conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
+ pool5 = max_pool(relu5, 3, stride=2)
+ fc6, relu6 = fc_relu(pool5, 4096)
+ drop6 = L.Dropout(relu6, in_place=True)
+ fc7, relu7 = fc_relu(drop6, 4096)
+ drop7 = L.Dropout(relu7, in_place=True)
+ fc8 = L.InnerProduct(drop7, num_output=1000)
+ loss = L.SoftmaxWithLoss(fc8, label)
+
+ if include_acc:
+ acc = L.Accuracy(fc8, label)
+ return to_proto(loss, acc)
+ else:
+ return to_proto(loss)
+
+def make_net():
+ with open('train.prototxt', 'w') as f:
+ print >>f, caffenet('/path/to/caffe-train-lmdb')
+
+ with open('test.prototxt', 'w') as f:
+ print >>f, caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True)
+
+if __name__ == '__main__':
+ make_net()