diff --git a/docs/onnx.md b/docs/onnx.md
index ee616d1..225c2c8 100644
--- a/docs/onnx.md
+++ b/docs/onnx.md
@@ -5,4 +5,420 @@
 
 <!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.  See the NOTICE file distributed with this work for additional information regarding copyright ownership.  The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.  You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  See the License for the specific language governing permissions and limitations under the License.  -->
 
-(under construction)
+ONNX is an open format built to represent machine learning models, which enables an ability to transfer trained models between different deep learning frameworks. We have integrated the main functionality of ONNX into Singa, and several basic operators have been supported. More operators are being developing.
+
+## Example: ONNX mnist on singa
+
+We will introduce the onnx of singa by using the mnist example. In this section, the examples of how to export, load, inference, re-training, and transfer-learning the minist model will be displayed.
+
+### Load dataset
+
+Firstly, we import some necessary libraries and define some auxiliary functions for downloading and preprocessing the dataset:
+
+```python
+import os
+import urllib.request
+import gzip
+import numpy as np
+import codecs
+
+from singa import device
+from singa import tensor
+from singa import opt
+from singa import autograd
+from singa import sonnx
+import onnx
+
+
+def load_dataset():
+    train_x_url = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
+    train_y_url = 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz'
+    valid_x_url = 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz'
+    valid_y_url = 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
+    train_x = read_image_file(check_exist_or_download(train_x_url)).astype(
+        np.float32)
+    train_y = read_label_file(check_exist_or_download(train_y_url)).astype(
+        np.float32)
+    valid_x = read_image_file(check_exist_or_download(valid_x_url)).astype(
+        np.float32)
+    valid_y = read_label_file(check_exist_or_download(valid_y_url)).astype(
+        np.float32)
+    return train_x, train_y, valid_x, valid_y
+
+
+def check_exist_or_download(url):
+
+    download_dir = '/tmp/'
+
+    name = url.rsplit('/', 1)[-1]
+    filename = os.path.join(download_dir, name)
+    if not os.path.isfile(filename):
+        print("Downloading %s" % url)
+        urllib.request.urlretrieve(url, filename)
+    return filename
+
+
+def read_label_file(path):
+    with gzip.open(path, 'rb') as f:
+        data = f.read()
+        assert get_int(data[:4]) == 2049
+        length = get_int(data[4:8])
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=8).reshape(
+            (length))
+        return parsed
+
+
+def get_int(b):
+    return int(codecs.encode(b, 'hex'), 16)
+
+
+def read_image_file(path):
+    with gzip.open(path, 'rb') as f:
+        data = f.read()
+        assert get_int(data[:4]) == 2051
+        length = get_int(data[4:8])
+        num_rows = get_int(data[8:12])
+        num_cols = get_int(data[12:16])
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=16).reshape(
+            (length, 1, num_rows, num_cols))
+        return parsed
+
+
+def to_categorical(y, num_classes):
+    y = np.array(y, dtype="int")
+    n = y.shape[0]
+    categorical = np.zeros((n, num_classes))
+    categorical[np.arange(n), y] = 1
+    categorical = categorical.astype(np.float32)
+    return categorical
+```
+
+### MNIST model
+
+We define a class called **CNN** to construct the mnist model which consists of several convolution, pooling, fully connection and relu layers. We also define a function to calculate the **accuracy** of our result. Finally, we define a **train** and a **test** function to handle the training and prediction process.
+
+```python
+class CNN:
+    def __init__(self):
+        self.conv1 = autograd.Conv2d(1, 20, 5, padding=0)
+        self.conv2 = autograd.Conv2d(20, 50, 5, padding=0)
+        self.linear1 = autograd.Linear(4 * 4 * 50, 500, bias=False)
+        self.linear2 = autograd.Linear(500, 10, bias=False)
+        self.pooling1 = autograd.MaxPool2d(2, 2, padding=0)
+        self.pooling2 = autograd.MaxPool2d(2, 2, padding=0)
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = autograd.relu(y)
+        y = self.pooling1(y)
+        y = self.conv2(y)
+        y = autograd.relu(y)
+        y = self.pooling2(y)
+        y = autograd.flatten(y)
+        y = self.linear1(y)
+        y = autograd.relu(y)
+        y = self.linear2(y)
+        return y
+
+
+def accuracy(pred, target):
+    y = np.argmax(pred, axis=1)
+    t = np.argmax(target, axis=1)
+    a = y == t
+    return np.array(a, "int").sum() / float(len(t))
+
+
+def train(model,
+          x,
+          y,
+          epochs=1,
+          batch_size=64,
+          dev=device.get_default_device()):
+    batch_number = x.shape[0] // batch_size
+
+    for i in range(epochs):
+        for b in range(batch_number):
+            l_idx = b * batch_size
+            r_idx = (b + 1) * batch_size
+
+            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
+            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
+
+            output_batch = model.forward(x_batch)
+            # onnx_model = sonnx.to_onnx([x_batch], [y])
+            # print('The model is:\n{}'.format(onnx_model))
+
+            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
+            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
+                                     tensor.to_numpy(target_batch))
+
+            sgd = opt.SGD(lr=0.001)
+            for p, gp in autograd.backward(loss):
+                sgd.update(p, gp)
+            sgd.step()
+
+            if b % 1e2 == 0:
+                print("acc %6.2f loss, %6.2f" %
+                      (accuracy_rate, tensor.to_numpy(loss)[0]))
+    print("training completed")
+    return x_batch, output_batch
+
+def test(model, x, y, batch_size=64, dev=device.get_default_device()):
+    batch_number = x.shape[0] // batch_size
+
+    result = 0
+    for b in range(batch_number):
+        l_idx = b * batch_size
+        r_idx = (b + 1) * batch_size
+
+        x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
+        target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
+
+        output_batch = model.forward(x_batch)
+        result += accuracy(tensor.to_numpy(output_batch),
+                           tensor.to_numpy(target_batch))
+
+    print("testing acc %6.2f" % (result / batch_number))
+```
+
+### Train mnist model and export it to onnx
+
+Now, we can train the mnist model and export its onnx model by calling the **soonx.to_onnx** function.
+
+```python
+def make_onnx(x, y):
+    return sonnx.to_onnx([x], [y])
+
+# create device
+dev = device.create_cuda_gpu()
+#dev = device.get_default_device()
+# create model
+model = CNN()
+# load data
+train_x, train_y, valid_x, valid_y = load_dataset()
+# normalization
+train_x = train_x / 255
+valid_x = valid_x / 255
+train_y = to_categorical(train_y, 10)
+valid_y = to_categorical(valid_y, 10)
+# do training
+autograd.training = True
+x, y = train(model, train_x, train_y, dev=dev)
+onnx_model = make_onnx(x, y)
+# print('The model is:\n{}'.format(onnx_model))
+
+# Save the ONNX model
+model_path = os.path.join('/', 'tmp', 'mnist.onnx')
+onnx.save(onnx_model, model_path)
+print('The model is saved.')
+```
+
+### Inference
+
+After we export the onnx model, we can find a file called **mnist.onnx** in the '/tmp' directory, this model, therefore, can be imported by other libraries. Now, if we want to import this onnx model into singa again and do the inference using the validation dataset, we can define a class called **Infer**, the forward function of Infer will be called by the test function to do inference for validation dataset. By the way, we should set the label of training to **False** to fix the gradient of autograd operators.
+
+When import the onnx model, we firstly call **onnx.load** to load the onnx model. Then the onnx model will be fed into the **soonx.prepare** to parse and initiate to a singa model(**sg_ir** in the code). The sg_ir contains a singa graph within it, and we can run an step of inference by feeding input to its run function.
+
+```python
+class Infer:
+    def __init__(self, sg_ir):
+        self.sg_ir = sg_ir
+        for idx, tens in sg_ir.tensor_map.items():
+            # allow the tensors to be updated
+            tens.requires_grad = True
+            tens.stores_grad= True
+            sg_ir.tensor_map[idx] = tens
+
+    def forward(self, x):
+        return sg_ir.run([x])[0] # we can run one step of inference by feeding input
+
+# load the ONNX model
+onnx_model = onnx.load(model_path)
+sg_ir = sonnx.prepare(onnx_model, device=dev) # parse and initiate to a singa model
+
+# inference
+autograd.training = False
+print('The inference result is:')
+test(Infer(sg_ir), valid_x, valid_y, dev=dev)
+```
+
+### Re-training
+
+Assume after import the model, we want to re-train the model again, we can define a function called **re_train**. Before we call this re_train function, we should set the label of training to **True** to make the autograde operators update their gradient. And after we finish the training, we set it as **False** again to call the test function doing inference.
+
+```python
+def re_train(sg_ir,
+             x,
+             y,
+             epochs=1,
+             batch_size=64,
+             dev=device.get_default_device()):
+    batch_number = x.shape[0] // batch_size
+
+    new_model = Infer(sg_ir)
+
+    for i in range(epochs):
+        for b in range(batch_number):
+            l_idx = b * batch_size
+            r_idx = (b + 1) * batch_size
+
+            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
+            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
+
+            output_batch = new_model.forward(x_batch)
+
+            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
+            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
+                                     tensor.to_numpy(target_batch))
+
+            sgd = opt.SGD(lr=0.01)
+            for p, gp in autograd.backward(loss):
+                sgd.update(p, gp)
+            sgd.step()
+
+            if b % 1e2 == 0:
+                print("acc %6.2f loss, %6.2f" %
+                      (accuracy_rate, tensor.to_numpy(loss)[0]))
+    print("re-training completed")
+    return new_model
+
+# load the ONNX model
+onnx_model = onnx.load(model_path)
+sg_ir = sonnx.prepare(onnx_model, device=dev)
+
+# re-training
+autograd.training = True
+new_model = re_train(sg_ir, train_x, train_y, dev=dev)
+autograd.training = False
+test(new_model, valid_x, valid_y, dev=dev)
+```
+
+### Transfer learning
+
+Finally, if we want to do transfer-learning, we can define a function called **Trans** to append some layers after the onnx model. For demonstration, we only append several linear(fully connection) and relu after the onnx model. We also define a transfer_learning function to handle the training process of the transfer-learning model. And the label of training is the same as the previous one.
+
+```python
+class Trans:
+    def __init__(self, sg_ir, last_layers):
+        self.sg_ir = sg_ir
+        self.last_layers = last_layers
+        self.append_linear1 = autograd.Linear(500, 128, bias=False)
+        self.append_linear2 = autograd.Linear(128, 32, bias=False)
+        self.append_linear3 = autograd.Linear(32, 10, bias=False)
+
+    def forward(self, x):
+        y = sg_ir.run([x], last_layers=self.last_layers)[0]
+        y = self.append_linear1(y)
+        y = autograd.relu(y)
+        y = self.append_linear2(y)
+        y = autograd.relu(y)
+        y = self.append_linear3(y)
+        y = autograd.relu(y)
+        return y
+
+def transfer_learning(sg_ir,
+             x,
+             y,
+             epochs=1,
+             batch_size=64,
+             dev=device.get_default_device()):
+    batch_number = x.shape[0] // batch_size
+
+    trans_model = Trans(sg_ir, -1)
+
+    for i in range(epochs):
+        for b in range(batch_number):
+            l_idx = b * batch_size
+            r_idx = (b + 1) * batch_size
+
+            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
+            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
+            output_batch = trans_model.forward(x_batch)
+
+            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
+            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
+                                     tensor.to_numpy(target_batch))
+
+            sgd = opt.SGD(lr=0.07)
+            for p, gp in autograd.backward(loss):
+                sgd.update(p, gp)
+            sgd.step()
+
+            if b % 1e2 == 0:
+                print("acc %6.2f loss, %6.2f" %
+                      (accuracy_rate, tensor.to_numpy(loss)[0]))
+    print("transfer-learning completed")
+    return trans_mode
+
+# load the ONNX model
+onnx_model = onnx.load(model_path)
+sg_ir = sonnx.prepare(onnx_model, device=dev)
+
+# transfer-learning
+autograd.training = True
+new_model = transfer_learning(sg_ir, train_x, train_y, dev=dev)
+autograd.training = False
+test(new_model, valid_x, valid_y, dev=dev)
+```
+
+## Supported operators
+
+The following operators are supported:
+
+- Conv
+- Relu
+- Constant
+- MaxPool
+- AveragePool
+- Softmax
+- Sigmoid
+- Add
+- MatMul
+- BatchNormalization
+- Concat
+- Flatten
+- Add
+- Gemm
+- Reshape
+- Sum
+- Cos
+- Cosh
+- Sin
+- Sinh
+- Tan
+- Tanh
+- Acos
+- Acosh
+- Asin
+- Asinh
+- Atan
+- Atanh
+- Selu
+- Elu
+- Equal
+- Less
+- Sign
+- Div
+- Sub
+- Sqrt
+- Log
+- Greater
+- HardSigmoid
+- Identity
+- Softplus
+- Softsign
+- Mean
+- Pow
+- Clip
+- PRelu
+- Mul
+- Transpose
+- Max
+- Min
+- Shape
+- And
+- Or
+- Xor
+- Not
+- Neg
+- Reciprocal
diff --git a/website/versioned_docs/version-2.0.0/onnx.md b/website/versioned_docs/version-2.0.0/onnx.md
index 0cc1378..6909648 100644
--- a/website/versioned_docs/version-2.0.0/onnx.md
+++ b/website/versioned_docs/version-2.0.0/onnx.md
@@ -6,4 +6,420 @@
 
 <!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.  See the NOTICE file distributed with this work for additional information regarding copyright ownership.  The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.  You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  See the License for the specific language governing permissions and limitations under the License.  -->
 
-(under construction)
+ONNX is an open format built to represent machine learning models, which enables an ability to transfer trained models between different deep learning frameworks. We have integrated the main functionality of ONNX into Singa, and several basic operators have been supported. More operators are being developing.
+
+## Example: ONNX mnist on singa
+
+We will introduce the onnx of singa by using the mnist example. In this section, the examples of how to export, load, inference, re-training, and transfer-learning the minist model will be displayed.
+
+### Load dataset
+
+Firstly, we import some necessary libraries and define some auxiliary functions for downloading and preprocessing the dataset:
+
+```python
+import os
+import urllib.request
+import gzip
+import numpy as np
+import codecs
+
+from singa import device
+from singa import tensor
+from singa import opt
+from singa import autograd
+from singa import sonnx
+import onnx
+
+
+def load_dataset():
+    train_x_url = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
+    train_y_url = 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz'
+    valid_x_url = 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz'
+    valid_y_url = 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
+    train_x = read_image_file(check_exist_or_download(train_x_url)).astype(
+        np.float32)
+    train_y = read_label_file(check_exist_or_download(train_y_url)).astype(
+        np.float32)
+    valid_x = read_image_file(check_exist_or_download(valid_x_url)).astype(
+        np.float32)
+    valid_y = read_label_file(check_exist_or_download(valid_y_url)).astype(
+        np.float32)
+    return train_x, train_y, valid_x, valid_y
+
+
+def check_exist_or_download(url):
+
+    download_dir = '/tmp/'
+
+    name = url.rsplit('/', 1)[-1]
+    filename = os.path.join(download_dir, name)
+    if not os.path.isfile(filename):
+        print("Downloading %s" % url)
+        urllib.request.urlretrieve(url, filename)
+    return filename
+
+
+def read_label_file(path):
+    with gzip.open(path, 'rb') as f:
+        data = f.read()
+        assert get_int(data[:4]) == 2049
+        length = get_int(data[4:8])
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=8).reshape(
+            (length))
+        return parsed
+
+
+def get_int(b):
+    return int(codecs.encode(b, 'hex'), 16)
+
+
+def read_image_file(path):
+    with gzip.open(path, 'rb') as f:
+        data = f.read()
+        assert get_int(data[:4]) == 2051
+        length = get_int(data[4:8])
+        num_rows = get_int(data[8:12])
+        num_cols = get_int(data[12:16])
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=16).reshape(
+            (length, 1, num_rows, num_cols))
+        return parsed
+
+
+def to_categorical(y, num_classes):
+    y = np.array(y, dtype="int")
+    n = y.shape[0]
+    categorical = np.zeros((n, num_classes))
+    categorical[np.arange(n), y] = 1
+    categorical = categorical.astype(np.float32)
+    return categorical
+```
+
+### MNIST model
+
+We define a class called **CNN** to construct the mnist model which consists of several convolution, pooling, fully connection and relu layers. We also define a function to calculate the **accuracy** of our result. Finally, we define a **train** and a **test** function to handle the training and prediction process.
+
+```python
+class CNN:
+    def __init__(self):
+        self.conv1 = autograd.Conv2d(1, 20, 5, padding=0)
+        self.conv2 = autograd.Conv2d(20, 50, 5, padding=0)
+        self.linear1 = autograd.Linear(4 * 4 * 50, 500, bias=False)
+        self.linear2 = autograd.Linear(500, 10, bias=False)
+        self.pooling1 = autograd.MaxPool2d(2, 2, padding=0)
+        self.pooling2 = autograd.MaxPool2d(2, 2, padding=0)
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = autograd.relu(y)
+        y = self.pooling1(y)
+        y = self.conv2(y)
+        y = autograd.relu(y)
+        y = self.pooling2(y)
+        y = autograd.flatten(y)
+        y = self.linear1(y)
+        y = autograd.relu(y)
+        y = self.linear2(y)
+        return y
+
+
+def accuracy(pred, target):
+    y = np.argmax(pred, axis=1)
+    t = np.argmax(target, axis=1)
+    a = y == t
+    return np.array(a, "int").sum() / float(len(t))
+
+
+def train(model,
+          x,
+          y,
+          epochs=1,
+          batch_size=64,
+          dev=device.get_default_device()):
+    batch_number = x.shape[0] // batch_size
+
+    for i in range(epochs):
+        for b in range(batch_number):
+            l_idx = b * batch_size
+            r_idx = (b + 1) * batch_size
+
+            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
+            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
+
+            output_batch = model.forward(x_batch)
+            # onnx_model = sonnx.to_onnx([x_batch], [y])
+            # print('The model is:\n{}'.format(onnx_model))
+
+            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
+            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
+                                     tensor.to_numpy(target_batch))
+
+            sgd = opt.SGD(lr=0.001)
+            for p, gp in autograd.backward(loss):
+                sgd.update(p, gp)
+            sgd.step()
+
+            if b % 1e2 == 0:
+                print("acc %6.2f loss, %6.2f" %
+                      (accuracy_rate, tensor.to_numpy(loss)[0]))
+    print("training completed")
+    return x_batch, output_batch
+
+def test(model, x, y, batch_size=64, dev=device.get_default_device()):
+    batch_number = x.shape[0] // batch_size
+
+    result = 0
+    for b in range(batch_number):
+        l_idx = b * batch_size
+        r_idx = (b + 1) * batch_size
+
+        x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
+        target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
+
+        output_batch = model.forward(x_batch)
+        result += accuracy(tensor.to_numpy(output_batch),
+                           tensor.to_numpy(target_batch))
+
+    print("testing acc %6.2f" % (result / batch_number))
+```
+
+### Train mnist model and export it to onnx
+
+Now, we can train the mnist model and export its onnx model by calling the **soonx.to_onnx** function.
+
+```python
+def make_onnx(x, y):
+    return sonnx.to_onnx([x], [y])
+
+# create device
+dev = device.create_cuda_gpu()
+#dev = device.get_default_device()
+# create model
+model = CNN()
+# load data
+train_x, train_y, valid_x, valid_y = load_dataset()
+# normalization
+train_x = train_x / 255
+valid_x = valid_x / 255
+train_y = to_categorical(train_y, 10)
+valid_y = to_categorical(valid_y, 10)
+# do training
+autograd.training = True
+x, y = train(model, train_x, train_y, dev=dev)
+onnx_model = make_onnx(x, y)
+# print('The model is:\n{}'.format(onnx_model))
+
+# Save the ONNX model
+model_path = os.path.join('/', 'tmp', 'mnist.onnx')
+onnx.save(onnx_model, model_path)
+print('The model is saved.')
+```
+
+### Inference
+
+After we export the onnx model, we can find a file called **mnist.onnx** in the '/tmp' directory, this model, therefore, can be imported by other libraries. Now, if we want to import this onnx model into singa again and do the inference using the validation dataset, we can define a class called **Infer**, the forward function of Infer will be called by the test function to do inference for validation dataset. By the way, we should set the label of training to **False** to fix the gradient of autograd operators.
+
+When import the onnx model, we firstly call **onnx.load** to load the onnx model. Then the onnx model will be fed into the **soonx.prepare** to parse and initiate to a singa model(**sg_ir** in the code). The sg_ir contains a singa graph within it, and we can run an step of inference by feeding input to its run function.
+
+```python
+class Infer:
+    def __init__(self, sg_ir):
+        self.sg_ir = sg_ir
+        for idx, tens in sg_ir.tensor_map.items():
+            # allow the tensors to be updated
+            tens.requires_grad = True
+            tens.stores_grad= True
+            sg_ir.tensor_map[idx] = tens
+
+    def forward(self, x):
+        return sg_ir.run([x])[0] # we can run one step of inference by feeding input
+
+# load the ONNX model
+onnx_model = onnx.load(model_path)
+sg_ir = sonnx.prepare(onnx_model, device=dev) # parse and initiate to a singa model
+
+# inference
+autograd.training = False
+print('The inference result is:')
+test(Infer(sg_ir), valid_x, valid_y, dev=dev)
+```
+
+### Re-training
+
+Assume after import the model, we want to re-train the model again, we can define a function called **re_train**. Before we call this re_train function, we should set the label of training to **True** to make the autograde operators update their gradient. And after we finish the training, we set it as **False** again to call the test function doing inference.
+
+```python
+def re_train(sg_ir,
+             x,
+             y,
+             epochs=1,
+             batch_size=64,
+             dev=device.get_default_device()):
+    batch_number = x.shape[0] // batch_size
+
+    new_model = Infer(sg_ir)
+
+    for i in range(epochs):
+        for b in range(batch_number):
+            l_idx = b * batch_size
+            r_idx = (b + 1) * batch_size
+
+            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
+            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
+
+            output_batch = new_model.forward(x_batch)
+
+            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
+            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
+                                     tensor.to_numpy(target_batch))
+
+            sgd = opt.SGD(lr=0.01)
+            for p, gp in autograd.backward(loss):
+                sgd.update(p, gp)
+            sgd.step()
+
+            if b % 1e2 == 0:
+                print("acc %6.2f loss, %6.2f" %
+                      (accuracy_rate, tensor.to_numpy(loss)[0]))
+    print("re-training completed")
+    return new_model
+
+# load the ONNX model
+onnx_model = onnx.load(model_path)
+sg_ir = sonnx.prepare(onnx_model, device=dev)
+
+# re-training
+autograd.training = True
+new_model = re_train(sg_ir, train_x, train_y, dev=dev)
+autograd.training = False
+test(new_model, valid_x, valid_y, dev=dev)
+```
+
+### Transfer learning
+
+Finally, if we want to do transfer-learning, we can define a function called **Trans** to append some layers after the onnx model. For demonstration, we only append several linear(fully connection) and relu after the onnx model. We also define a transfer_learning function to handle the training process of the transfer-learning model. And the label of training is the same as the previous one.
+
+```python
+class Trans:
+    def __init__(self, sg_ir, last_layers):
+        self.sg_ir = sg_ir
+        self.last_layers = last_layers
+        self.append_linear1 = autograd.Linear(500, 128, bias=False)
+        self.append_linear2 = autograd.Linear(128, 32, bias=False)
+        self.append_linear3 = autograd.Linear(32, 10, bias=False)
+
+    def forward(self, x):
+        y = sg_ir.run([x], last_layers=self.last_layers)[0]
+        y = self.append_linear1(y)
+        y = autograd.relu(y)
+        y = self.append_linear2(y)
+        y = autograd.relu(y)
+        y = self.append_linear3(y)
+        y = autograd.relu(y)
+        return y
+
+def transfer_learning(sg_ir,
+             x,
+             y,
+             epochs=1,
+             batch_size=64,
+             dev=device.get_default_device()):
+    batch_number = x.shape[0] // batch_size
+
+    trans_model = Trans(sg_ir, -1)
+
+    for i in range(epochs):
+        for b in range(batch_number):
+            l_idx = b * batch_size
+            r_idx = (b + 1) * batch_size
+
+            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
+            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
+            output_batch = trans_model.forward(x_batch)
+
+            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
+            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
+                                     tensor.to_numpy(target_batch))
+
+            sgd = opt.SGD(lr=0.07)
+            for p, gp in autograd.backward(loss):
+                sgd.update(p, gp)
+            sgd.step()
+
+            if b % 1e2 == 0:
+                print("acc %6.2f loss, %6.2f" %
+                      (accuracy_rate, tensor.to_numpy(loss)[0]))
+    print("transfer-learning completed")
+    return trans_mode
+
+# load the ONNX model
+onnx_model = onnx.load(model_path)
+sg_ir = sonnx.prepare(onnx_model, device=dev)
+
+# transfer-learning
+autograd.training = True
+new_model = transfer_learning(sg_ir, train_x, train_y, dev=dev)
+autograd.training = False
+test(new_model, valid_x, valid_y, dev=dev)
+```
+
+## Supported operators
+
+The following operators are supported:
+
+- Conv
+- Relu
+- Constant
+- MaxPool
+- AveragePool
+- Softmax
+- Sigmoid
+- Add
+- MatMul
+- BatchNormalization
+- Concat
+- Flatten
+- Add
+- Gemm
+- Reshape
+- Sum
+- Cos
+- Cosh
+- Sin
+- Sinh
+- Tan
+- Tanh
+- Acos
+- Acosh
+- Asin
+- Asinh
+- Atan
+- Atanh
+- Selu
+- Elu
+- Equal
+- Less
+- Sign
+- Div
+- Sub
+- Sqrt
+- Log
+- Greater
+- HardSigmoid
+- Identity
+- Softplus
+- Softsign
+- Mean
+- Pow
+- Clip
+- PRelu
+- Mul
+- Transpose
+- Max
+- Min
+- Shape
+- And
+- Or
+- Xor
+- Not
+- Neg
+- Reciprocal