人工智能开发框架 04.网络构建

目录

步骤一 、全连接层

步骤二 、卷积层

步骤三 、ReLU层

步骤四 、池化层

步骤五 、Flatten层

步骤六 、定义模型类并查看参数

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MindSpore将构建网络层的接口封装在nn模块中，我们将通过调用来构建不同类型的神经网络层。

步骤一 、全连接层

全连接层：mindspore.nn.Dense

1. in_channels：输入通道
2. out_channels：输出通道
3. weight_init：权重初始化，Default 'normal'.

import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
import numpy as np# 构造输入张量
input_a = Tensor(np.array([[1, 1, 1], [2, 2, 2]]), ms.float32)
print(input_a)
# 构造全连接网络，输入通道为3，输出通道为3
net = nn.Dense(in_channels=3, out_channels=3, weight_init=1)
output = net(input_a)
print(output)

 

步骤二 、卷积层

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

 

步骤三 、ReLU层

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

 

步骤四 、池化层

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

步骤五 、Flatten层

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

步骤六 、定义模型类并查看参数

MindSpore的Cell类是构建所有网络的基类，也是网络的基本单元。当用户需要神经网络时，需要继承Cell类，并重写__init__方法和construct方法。

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 * 4 * 4, 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
#实例化模型，利用parameters_and_names方法查看模型的参数
modelle = LeNet5()
for m in modelle.parameters_and_names():print(m)