关于深度学习:课程作业经验基于MIndSpore疫苗接种数据预测

基于 mindspore 实现疫苗接种数据预测基于机器学习实际课程实现的相干应用 mindspore 深度学习框架实现的工作，写一些分享心得。数据导入与筹备数据下载链接：https://pan.baidu.com/s/10npL… 提取码：23vb 疫苗接种数据集蕴含 1983 年 -2016 年疫苗接种数据，其模式如下图所示：

读取数据并进行训练集、测试集宰割：df = pd.read_csv(“vaccine.csv”)
features = df[“Year”]
target = df[“Values”]
split_num = int(len(features)*0.7)

X_train = features[:split_num]
y_train = target[:split_num]

X_test = features[split_num:]
y_test = target[split_num:]

创立数据

X_train,X_test = poly_transform(X_train,X_test,degree=1)
ds_train=create_dataset(X_train,y_train,batch_number,repeat_number)
复制构建多项式特色：def poly_transform(X_train,X_test,degree=2):

'''多项式特色'''
poly_features_2 = PolynomialFeatures(degree = degree, include_bias=False)
poly_X_train_2 = poly_features_2.fit_transform(X_train.values.reshape(len(X_train),1))
poly_X_test_2 = poly_features_2.fit_transform(X_test.values.reshape(len(X_test),1))

return poly_X_train_2,poly_X_test_2

复制全连贯网络模型建设构建一层 1 ->1 的网络，比较简单：class LinearNet(nn.Cell):

def __init__(self,n=1):
    super(LinearNet,self).__init__()
    # 定义一个线形层，同时初始化权重和偏置
    self.fc=nn.Dense(n,1,Normal(0.02),Normal(0.02),has_bias=True) 

def construct(self,x):
    x=self.fc(x)
    return x

复制设定优化器以及其余参数在这里咱们采纳 MSEloss 函数，以及 Momentum 优化器，感兴趣的话能够探索其余参数。# 初始化超参数
batch_number=1
repeat_number=1
epoch = 1000

创立模型

net=LinearNet(n=1)
net_loss=nn.loss.MSELoss()
opt=nn.Momentum(net.trainable_params(),learning_rate=1e-7,momentum=0.01)
复制模型训练 Mindspore 实现的模型训练较为简单，封装的很好。model=Model(net,net_loss,opt)
model.train(epoch, ds_train, dataset_sink_mode=False)
复制测试后果

残缺代码 import numpy as np
import pandas as pd
from mindspore import Model, Tensor
from mindspore import dataset as ds
from mindspore import nn
from mindspore.common.initializer import Normal
from sklearn.preprocessing import PolynomialFeatures

def get_train(X,y):

'''
获取训练数据
param: 
    X: 特色 (pandas 读取类型)
    y: 标签 (pandas 读取类型)
'''
X,y=np.array(X).astype(np.float32),np.array(y).astype(np.float32)
for i in range(len(X)):
    yield [X<i>],[y<i>]
    

def get_test(X,y):

'''获取测试数据'''
X,y=np.array(X).astype(np.float32),np.array(y).astype(np.float32)

return X,y
    

def create_dataset(X_train,y_train,batch_size=16,repeat_size=1):

'''创立数据迭代器'''
a = list(get_train(X_train,y_train))
input_data=ds.GeneratorDataset(a,column_names=['data','label'])
input_data=input_data.batch(batch_size) # 设置数据批次
input_data=input_data.repeat(repeat_size) # 设置数据反复次数
return input_data

def mse(y_predict,y_test):

error = 0
for i in range(y_predict.shape[0]):
    error+=(y_predict<i>-y_test<i>)**2
error /= y_predict.shape[0]
error = error**0.5
print(" 测试集的 mse 为：",error)
return error

def test_all(net,X_test,y_test):

'''测试函数，输入测试集的 mse'''
weight = net.trainable_params()[0]
bias = net.trainable_params()[1]
x_test,y_test = get_test(X_test,y_test)
a= Tensor(weight).asnumpy()[0]
a = np.expand_dims(a, 1)
x_test = np.expand_dims(x_test, 1)
b = np.matmul(x_test, a)
y_predict =  b + Tensor(bias).asnumpy()[0]
mse(y_predict,y_test)

def poly_transform(X_train,X_test,degree=2):

'''多项式特色'''
poly_features_2 = PolynomialFeatures(degree = degree, include_bias=False)
poly_X_train_2 = poly_features_2.fit_transform(X_train.values.reshape(len(X_train),1))
poly_X_test_2 = poly_features_2.fit_transform(X_test.values.reshape(len(X_test),1))

return poly_X_train_2,poly_X_test_2

class LinearNet(nn.Cell):

def __init__(self,n=1):
    super(LinearNet,self).__init__()
    # 定义一个线形层，同时初始化权重和偏置
    self.fc=nn.Dense(n,1,Normal(0.02),Normal(0.02),has_bias=True) 

def construct(self,x):
    x=self.fc(x)
    return x

def main():

# ===================================================
# 读入数据 -> 绝对路径
df = pd.read_csv("vaccine.csv")
features = df["Year"]
target = df["Values"]
split_num = int(len(features)*0.7)

X_train = features[:split_num]
y_train = target[:split_num]    

X_test = features[split_num:]
y_test = target[split_num:]
# ===================================================

# ===================================================
# 初始化超参数
batch_number=1
repeat_number=1
epoch = 1000
# ===================================================

# 创立数据
X_train,X_test = poly_transform(X_train,X_test,degree=1)
ds_train=create_dataset(X_train,y_train,batch_number,repeat_number)
print(ds_train)

# ===================================================
# 创立模型
net=LinearNet(n=1)
net_loss=nn.loss.MSELoss()
opt=nn.Momentum(net.trainable_params(),learning_rate=1e-7,momentum=0.01)

model=Model(net,net_loss,opt)
# ===================================================

# ===================================================
# 训练 + 测试
model.train(epoch, ds_train, dataset_sink_mode=False)

test_all(net,X_test,y_test)
# ===================================================

# 打印线性回归参数
for net_param in net.trainable_params():
    print(net_param, net_param.asnumpy())

if name == “__main__”:

main()