!687 delete lenet and create_dataset api description

Merge pull request !687 from jinyaohui/master

tutorials/source_zh_cn/advanced_use/gradient_accumulation.md

@@ -28,7 +28,7 @@
   
 最终目的是为了达到跟直接用N*Mini-batch数据训练几乎同样的效果。
 
-> 本教程用于GPU、Ascend 910 AI处理器, 你可以在这里下载完整的样例代码：<https://gitee.com/mindspore/docs/tree/master/tutorials/tutorial_code/gradient_accumulation>
+> 本教程用于GPU、Ascend 910 AI处理器, 你可以在这里下载主要的训练样例代码：<https://gitee.com/mindspore/docs/tree/master/tutorials/tutorial_code/gradient_accumulation>
 
 ## 创建梯度累积模型
 
@@ -57,111 +57,11 @@ from model_zoo.official.cv.lenet.src.lenet import LeNet5
 
 ### 加载数据集
 
-利用MindSpore的dataset提供的`MnistDataset`接口加载MNIST数据集。
-
-```python
-def create_dataset(data_path, batch_size=32, repeat_size=1,
-                   num_parallel_workers=1):
-    """
-    create dataset for train or test
-    """
-    # define dataset
-    mnist_ds = ds.MnistDataset(data_path)
-
-    resize_height, resize_width = 32, 32
-    rescale = 1.0 / 255.0
-    shift = 0.0
-    rescale_nml = 1 / 0.3081
-    shift_nml = -1 * 0.1307 / 0.3081
-
-    # define map operations
-    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
-    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
-    rescale_op = CV.Rescale(rescale, shift)
-    hwc2chw_op = CV.HWC2CHW()
-    type_cast_op = C.TypeCast(mstype.int32)
-
-    # apply map operations on images
-    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
-
-    # apply DatasetOps
-    buffer_size = 10000
-    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
-    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
-    mnist_ds = mnist_ds.repeat(repeat_size)
-
-    return mnist_ds
-```
+利用MindSpore的dataset提供的`MnistDataset`接口加载MNIST数据集，此部分代码由model_zoo中lenet目录下的[dataset.py](<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet/src/dataset.py>)导入。
 
 ### 定义网络
 
-这里以LeNet网络为例进行介绍，当然也可以使用其它的网络，如ResNet-50、BERT等。
-
-```python
-def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
-    """weight initial for conv layer"""
-    weight = weight_variable()
-    return nn.Conv2d(in_channels, out_channels,
-                     kernel_size=kernel_size, stride=stride, padding=padding,
-                     weight_init=weight, has_bias=False, pad_mode="valid")
-
-
-def fc_with_initialize(input_channels, out_channels):
-    """weight initial for fc layer"""
-    weight = weight_variable()
-    bias = weight_variable()
-    return nn.Dense(input_channels, out_channels, weight, bias)
-
-
-def weight_variable():
-    """weight initial"""
-    return TruncatedNormal(0.02)
-
-
-class LeNet5(nn.Cell):
-    """
-    Lenet network
-
-    Args:
-        num_class (int): Number classes. Default: 10.
-
-    Returns:
-        Tensor, output tensor
-    Examples:
-        >>> LeNet(num_class=10)
-
-    """
-    def __init__(self, num_class=10, channel=1):
-        super(LeNet5, self).__init__()
-        self.num_class = num_class
-        self.conv1 = conv(channel, 6, 5)
-        self.conv2 = conv(6, 16, 5)
-        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
-        self.fc2 = fc_with_initialize(120, 84)
-        self.fc3 = fc_with_initialize(84, self.num_class)
-        self.relu = nn.ReLU()
-        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.flatten = nn.Flatten()
-
-    def construct(self, x):
-        x = self.conv1(x)
-        x = self.relu(x)
-        x = self.max_pool2d(x)
-        x = self.conv2(x)
-        x = self.relu(x)
-        x = self.max_pool2d(x)
-        x = self.flatten(x)
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        x = self.relu(x)
-        x = self.fc3(x)
-        return x
-```
+这里以LeNet网络为例进行介绍，当然也可以使用其它的网络，如ResNet-50、BERT等, 此部分代码由model_zoo中lenet目录下的[lenet.py](<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet/src/lenet.py>)导入。
 
 ### 定义训练模型
 将训练流程拆分为正向反向训练、参数更新和累积梯度清理三个部分：
@@ -350,7 +250,7 @@ if __name__ == "__main__":
 
 **验证模型**
 
-通过model_zoo下lenet网络的[eval.py](<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet/train.py>)，使用保存的CheckPoint文件，加载验证数据集，进行验证。
+通过model_zoo中lenet目录下的[eval.py](<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet/train.py>)，使用保存的CheckPoint文件，加载验证数据集，进行验证。
 
 ```shell
 $ python eval.py --data_path=./MNIST_Data --ckpt_path=./gradient_accumulation.ckpt