diff --git a/dygraph/mnist/README_cn.md b/dygraph/mnist/README_cn.md
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+# MNIST
+当我们学习编程的时候，编写的第一个程序一般是实现打印"Hello World"。而机器学习（或深度学习）的入门教程，一般都是 MNIST 数据库上的手写识别问题。原因是手写识别属于典型的图像分类问题，比较简单，同时MNIST数据集也很完备。
+本页将介绍如何使用PaddlePaddle在DyGraph模式下实现MNIST，包括[安装](#installation)、[训练](#training-a-model)、[输出](#log)。
+
+---
+## 内容
+- [安装](#installation)
+- [训练](#training-a-model)
+- [输出](#log)
+
+## 安装
+
+在当前目录下运行样例代码需要PadddlePaddle Fluid的v1.4.0或以上的版本。如果你的运行环境中的PaddlePaddle低于此版本，请根据安装文档中的说明来更新PaddlePaddle。
+
+## 训练
+教程中使用`paddle.dataset.mnist`数据集作为训练数据，可以通过如下的方式启动训练：
+```
+env CUDA_VISIBLE_DEVICES=0 python mnist_dygraph.py
+```
+
+## 输出
+执行训练开始后，将得到类似如下的输出。
+```
+batch_id 0,loss 2.1786134243
+batch_id 10,loss 0.898496925831
+batch_id 20,loss 1.32524681091
+...
+```
diff --git a/dygraph/mnist/mnist_dygraph.py b/dygraph/mnist/mnist_dygraph.py
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+++ b/dygraph/mnist/mnist_dygraph.py
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+from __future__ import print_function
+
+import contextlib
+import numpy as np
+import six
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.optimizer import SGDOptimizer
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
+from paddle.fluid.dygraph.base import to_variable
+
+
+class SimpleImgConvPool(fluid.dygraph.Layer):
+    """
+    Conv Pool Layer
+    """
+    def __init__(self,
+                 name_scope,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 pool_size,
+                 pool_stride,
+                 pool_padding=0,
+                 pool_type='max',
+                 global_pooling=False,
+                 conv_stride=1,
+                 conv_padding=0,
+                 conv_dilation=1,
+                 conv_groups=1,
+                 act=None,
+                 use_cudnn=False,
+                 param_attr=None,
+                 bias_attr=None):
+        super(SimpleImgConvPool, self).__init__(name_scope)
+
+        self._conv2d = Conv2D(
+            self.full_name(),
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=conv_stride,
+            padding=conv_padding,
+            dilation=conv_dilation,
+            groups=conv_groups,
+            param_attr=None,
+            bias_attr=None,
+            use_cudnn=use_cudnn)
+
+        self._pool2d = Pool2D(
+            self.full_name(),
+            pool_size=pool_size,
+            pool_type=pool_type,
+            pool_stride=pool_stride,
+            pool_padding=pool_padding,
+            global_pooling=global_pooling,
+            use_cudnn=use_cudnn)
+
+    def forward(self, inputs):
+        x = self._conv2d(inputs)
+        x = self._pool2d(x)
+        return x
+
+
+class MNIST(fluid.dygraph.Layer):
+    """
+    MNIST model
+    """
+    def __init__(self, name_scope):
+        super(MNIST, self).__init__(name_scope)
+
+        self._simple_img_conv_pool_1 = SimpleImgConvPool(
+            self.full_name(), 1, 20, 5, 2, 2, act="relu")
+
+        self._simple_img_conv_pool_2 = SimpleImgConvPool(
+            self.full_name(), 20, 50, 5, 2, 2, act="relu")
+
+        pool_2_shape = 50 * 4 * 4
+        SIZE = 10
+        scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
+        self._fc = FC(self.full_name(),
+                      10,
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.NormalInitializer(
+                              loc=0.0, scale=scale)),
+                      act="softmax")
+
+    def forward(self, inputs):
+        x = self._simple_img_conv_pool_1(inputs)
+        x = self._simple_img_conv_pool_2(x)
+        x = self._fc(x)
+        return x
+
+
+def train_mnist():
+    seed = 90
+    epoch_num = 10
+
+    with fluid.dygraph.guard():
+        fluid.default_startup_program().random_seed = seed
+        fluid.default_main_program().random_seed = seed
+
+        mnist = MNIST("mnist")
+        sgd = SGDOptimizer(learning_rate=1e-3)
+        train_reader = paddle.batch(
+            paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
+
+        for epoch in range(epoch_num):
+            for batch_id, data in enumerate(train_reader()):
+                dy_x_data = np.array(
+                    [x[0].reshape(1, 28, 28)
+                     for x in data]).astype('float32')
+                y_data = np.array(
+                    [x[1] for x in data]).astype('int64').reshape(128, 1)
+
+                img = to_variable(dy_x_data)
+                label = to_variable(y_data)
+                label.stop_gradient = True
+
+                cost = mnist(img)
+                loss = fluid.layers.cross_entropy(cost, label)
+                avg_loss = fluid.layers.mean(loss)
+                avg_loss.backward()
+                sgd.minimize(avg_loss)
+                mnist.clear_gradients()
+
+                dy_out = avg_loss.numpy()
+                print("batch id %d, loss %f" % (batch_id, dy_out))
+
+
+if __name__ == '__main__':
+    train_mnist()
+