add lenet&alexnet readme in example

example/alexnet_cifar10/README.md

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+# AlexNet Example
+
+## Description
+
+Training AlexNet with CIFAR-10 dataset in MindSpore.
+
+This is the simple tutorial for training AlexNet in MindSpore.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the CIFAR-10 dataset at <http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz>. The directory structure is as follows:
+
+```
+├─cifar-10-batches-bin
+│
+└─cifar-10-verify-bin
+```
+
+## Running the example
+
+```python
+# train AlexNet, hyperparameter setting in config.py
+python train.py --data_path cifar-10-batches-bin
+```
+
+You can get loss with each step similar to this:
+
+```bash
+epoch: 1 step: 1, loss is 2.2791853
+...
+epoch: 1 step: 1536, loss is 1.9366643
+epoch: 1 step: 1537, loss is 1.6983616
+epoch: 1 step: 1538, loss is 1.0221305
+...
+```
+
+Then, test AlexNet according to network model
+```python
+# test AlexNet, 1 epoch training accuracy is up to 51.1%; 10 epoch training accuracy is up to 81.2%
+python eval.py --data_path cifar-10-verify-bin --mode test --ckpt_path checkpoint_alexnet-1_1562.ckpt
+```
+
+## Note
+There are some optional arguments:
+
+```bash
+-h, --help           show this help message and exit
+--device_target {Ascend,GPU}
+                     device where the code will be implemented (default: Ascend)
+--data_path DATA_PATH
+                     path where the dataset is saved
+--dataset_sink_mode DATASET_SINK_MODE
+                     dataset_sink_mode is False or True
+```
+
+You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.

example/lenet_mnist/README.md

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+# LeNet Example
+
+## Description
+
+Training LeNet with MNIST dataset in MindSpore.
+
+This is the simple and basic tutorial for constructing a network in MindSpore.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the MNIST dataset at <http://yann.lecun.com/exdb/mnist/>. The directory structure is as follows:
+
+```
+└─MNIST_Data
+    ├─test
+    │      t10k-images.idx3-ubyte
+    │      t10k-labels.idx1-ubyte
+    │
+    └─train
+            train-images.idx3-ubyte
+            train-labels.idx1-ubyte
+```
+
+## Running the example
+
+```python
+# train LeNet, hyperparameter setting in config.py
+python train.py --data_path MNIST_Data
+```
+
+You can get loss with each step similar to this:
+
+```bash
+epoch: 1 step: 1, loss is 2.3040335
+...
+epoch: 1 step: 1739, loss is 0.06952668
+epoch: 1 step: 1740, loss is 0.05038793
+epoch: 1 step: 1741, loss is 0.05018193
+...
+```
+
+Then, test LeNet according to network model
+```python
+# test LeNet, after 1 epoch training, the accuracy is up to 96.5%
+python eval.py --data_path MNIST_Data --mode test --ckpt_path checkpoint_lenet-1_1875.ckpt
+```
+
+## Note
+There are some optional arguments:
+
+```bash
+-h, --help           show this help message and exit
+--device_target {Ascend,GPU,CPU}
+                     device where the code will be implemented (default: Ascend)
+--data_path DATA_PATH
+                     path where the dataset is saved
+--dataset_sink_mode DATASET_SINK_MODE
+                     dataset_sink_mode is False or True
+```
+
+You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.