MindSpore 建立神经网络

 代码原地址：

https://www.mindspore.cn/tutorial/zh-CN/r1.2/model.html

 

 

 

 

建立神经网络：

 

import mindspore.nn as nn

class LeNet5(nn.Cell):
    """
    Lenet网络结构
    """
    def __init__(self, num_class=10, num_channel=1):
        super(LeNet5, self).__init__()
        # 定义所需要的运算
        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
        self.fc1 = nn.Dense(16 * 5 * 5, 120)
        self.fc2 = nn.Dense(120, 84)
        self.fc3 = nn.Dense(84, num_class)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()

    def construct(self, x):
        # 使用定义好的运算构建前向网络
        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

model = LeNet5()

for m in model.parameters_and_names():
    print(m)

 

 

 

 

 

 

 

 

 

import mindspore
from mindspore import Tensor
import mindspore.nn as nn

import numpy as np

conv2d = nn.Conv2d(1, 6, 5, has_bias=False, weight_init='normal', pad_mode='valid')
input_x = Tensor(np.ones([1, 1, 32, 32]), mindspore.float32)

print(conv2d(input_x).shape)

 

 

 

 

 

 

 

 

 

import mindspore
from mindspore import Tensor
import mindspore.nn as nn

import numpy as np

relu = nn.ReLU()
input_x = Tensor(np.array([-1, 2, -3, 2, -1]), mindspore.float16)
output = relu(input_x)

print(output)

 

 

 

 

 

 

 

 

 

 

 

 

import mindspore
from mindspore import Tensor
import mindspore.nn as nn

import numpy as np

max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
input_x = Tensor(np.ones([1, 6, 28, 28]), mindspore.float32)

print(max_pool2d(input_x).shape)

 

 

 

 

 

 

 

import mindspore
from mindspore import Tensor
import mindspore.nn as nn

import numpy as np

flatten = nn.Flatten()
input_x = Tensor(np.ones([1, 16, 5, 5]), mindspore.float32)
output = flatten(input_x)

print(output.shape)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

import mindspore
from mindspore import Tensor
import mindspore.nn as nn

import numpy as np

dense = nn.Dense(400, 120, weight_init='normal')
input_x = Tensor(np.ones([1, 400]), mindspore.float32)
output = dense(input_x)

print(output.shape)