diff --git a/train/code/make_spherenet.py b/train/code/make_spherenet.py
new file mode 100644
index 0000000..fd6fdca
--- /dev/null
+++ b/train/code/make_spherenet.py
@@ -0,0 +1,106 @@
+from __future__ import print_function
+from caffe import layers as L, params as P, to_proto
+from caffe.proto import caffe_pb2
+import caffe
+
+# The initialization used
+xavier_constant = dict(type='xavier')
+gaussian_constant = dict(type='gaussian', std=0.01)
+
+
+class Spherenet(object):
+    def __init__(self):
+        pass
+
+    def conv_prelu(self, bottom, num_out, kernel_size=3, stride=1, pad=1, is_bias=False, 
+        wf=gaussian_constant):
+    
+        if is_bias:
+            learn_param = [dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)]
+        else:
+            learn_param = [dict(lr_mult=1, decay_mult=1), dict(lr_mult=0, decay_mult=0)]
+        conv = L.Convolution(bottom,
+                             kernel_size=kernel_size,
+                             stride=stride,
+                             num_output=num_out,
+                             pad=pad,
+                             param=learn_param,
+                             weight_filler=wf,
+                             bias_filler=dict(type='constant', value=0))
+        prelu = L.PReLU(conv, in_place=True)
+        return prelu
+
+    def add_block(self, bottom, num_output):
+        layer1 = self.conv_prelu(bottom, num_output)
+        layer2 = self.conv_prelu(layer1, num_output)
+        output = L.Eltwise(bottom, layer2, eltwise_param=dict(operation=1))
+        return output
+
+    def build_convolution(self, bottom, num_output, num_block):
+        model = self.conv_prelu(bottom, num_output, 3, 2, 1, True, xavier_constant)
+        for i in range(num_block):
+            model = self.add_block(model, num_output)
+        return model
+
+    def make_net(self, data_file, block_nums,  batch_size=256, feature_dim=512, class_num=10572):
+        assert len(block_nums) == 4, print('cause there four convolutions')
+        data, label = L.ImageData(image_data_param=dict(source=data_file,
+                                                   batch_size=batch_size,
+                                                   shuffle=True),
+
+                             transform_param=dict(mean_value=[127.5, 127.5, 127.5],
+                                                  scale=0.0078125,
+                                                  mirror=True),
+                             name='data',
+                             ntop=2)
+
+        conv1 = self.build_convolution(data, 64, block_nums[0])
+        conv2 = self.build_convolution(conv1, 128, block_nums[1])
+        conv3 = self.build_convolution(conv2, 256, block_nums[2])
+        conv4 = self.build_convolution(conv3, 512, block_nums[3])
+
+        fc5 = L.InnerProduct(conv4,
+                             num_output=feature_dim,
+                             bias_term=True,
+                             param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
+                             weight_filler=dict(type='xavier'),
+                             bias_filler=dict(type='constant'),
+                             name='fc5')
+
+        fc6 = L.MarginInnerProduct(fc5,
+                                   label,
+                                   num_output=class_num,
+                                   type=3,
+                                   param=dict(lr_mult=1, decay_mult=1),
+                                   weight_filler=dict(type='xavier'),
+                                   base=1000,
+                                   gamma=0.12,
+                                   power=1,
+                                   lambda_min=5,
+                                   iteration=0,
+                                   top='lambda')
+
+        loss = L.SoftmaxWithLoss(fc6, label)
+        return to_proto(loss)
+
+
+def make_net():
+    # make spherenet20
+    model = Spherenet()
+    with open('spherenet_model20.prototxt', 'w') as f:
+        block_nums = [1, 2, 4, 1]
+        print(str(model.make_net('data/CASIA-WebFace-112X96.txt', block_nums)), file=f)
+
+    # make spherenet36
+    with open('spherenet_model36.prototxt', 'w') as f:
+        block_nums = [2, 4, 8, 2]
+        print(str(model.make_net('data/CASIA-WebFace-112X96.txt', block_nums)), file=f)
+
+    # make spherenet64
+    with open('spherenet_model64.prototxt', 'w') as f:
+        block_nums = [3, 8, 16, 3]
+        print(str(model.make_net('data/CASIA-WebFace-112X96.txt', block_nums)), file=f)
+
+
+if __name__ == '__main__':
+    make_net()