MindSpore 如何實作一個線性回歸 —— Demo示例

如何使用 MindSpore 實作一個簡單的線性回歸呢？？？

根據前面的mindspore的基本操作的學習寫出了下面的一個簡單的線性回歸算法。

import mindspore
import numpy as np #引入numpy科學計算庫
import matplotlib.pyplot as plt #引入繪圖庫
np.random.seed(123) #随機數生成種子
#from sklearn.model_selection import train_test_split#從sklearn裡面引出訓練與測試集劃分

import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Tensor
from mindspore import ParameterTuple, Parameter
from mindspore import dtype as mstype


# 訓練資料集
def creat_dataset():
    n_x=2*np.random.rand(500,1)#随機生成一個0-2之間的，大小為（500，1）的向量
    n_y=5+3*n_x+np.random.randn(500,1)#随機生成一個線性方程的，大小為（500，1）的向量
    x = Tensor(n_x, dtype=mindspore.float32)
    y = Tensor(n_y, dtype=mindspore.float32)
    return x, y


class Net(nn.Cell):
    def __init__(self, input_dims, output_dims):
        super(Net, self).__init__()
        self.matmul = ops.MatMul()

        self.weight_1 = Parameter(Tensor(np.random.randn(input_dims, 128), dtype=mstype.float32), name='weight_1')
        self.bias_1 = Parameter(Tensor(np.zeros(128), dtype=mstype.float32), name='bias_1')
        self.weight_2 = Parameter(Tensor(np.random.randn(128, 64), dtype=mstype.float32), name='weight_2')
        self.bias_2 = Parameter(Tensor(np.zeros(64), dtype=mstype.float32), name='bias_2')
        self.weight_3 = Parameter(Tensor(np.random.randn(64, output_dims), dtype=mstype.float32), name='weight_3')
        self.bias_3 = Parameter(Tensor(np.zeros(output_dims), dtype=mstype.float32), name='bias_3')

    def construct(self, x):
        x = self.matmul(x, self.weight_1)+self.bias_1
        x = self.matmul(x, self.weight_2)+self.bias_2
        x = self.matmul(x, self.weight_3)+self.bias_3
        return x


class LossNet(nn.Cell):
    def __init__(self, net):
        super(LossNet, self).__init__()
        self.net = net
        self.pow = ops.Pow()
        self.mean = ops.ReduceMean()

    def construct(self, x, y):
        _x = self.net(x)
        loss = self.mean(self.pow(_x - y, 2))
        return loss


class GradNetWrtX(nn.Cell):
    def __init__(self, net):
        super(GradNetWrtX, self).__init__()
        self.net = net
        self.params = ParameterTuple(net.trainable_params())
        self.grad_op = ops.GradOperation(get_by_list=True)

    def construct(self, x, y):
        gradient_function = self.grad_op(self.net, self.params)
        return gradient_function(x, y)


def train(epochs, loss_net, x, y, print_flag=False):
    # 建構加和操作
    ass_add = ops.AssignAdd()
    para_list = loss_net.trainable_params()

    for epoch in range(epochs):
        grad_net = GradNetWrtX(loss_net)
        grad_list = grad_net(x, y)

        for para, grad in zip(para_list, grad_list):
            ass_add(para, -0.000001*grad)

        if print_flag and (epoch%100 == 0):
            print("epoch: %s, loss: %s"%(epoch, loss_net(x, y)))


def main():
    epochs = 10000
    x, y = creat_dataset()

    net = Net(x.shape[-1], y.shape[-1])
    loss_net = LossNet(net)
    train(epochs, loss_net, x, y, False)

    y_hat = net(x)

    fig=plt.figure(figsize=(8,6))#确定畫布大小
    plt.title("Dataset")#标題名
    plt.xlabel("First feature")#x軸的标題
    plt.ylabel("Second feature")#y軸的标題
    plt.scatter(x.asnumpy(), y.asnumpy())#設定為散點圖
    plt.scatter(x.asnumpy(), y_hat.asnumpy())#設定為散點圖
    plt.show()#繪制出來


if __name__ == '__main__':
    """ 設定運作的背景context """
    from mindspore import context
    # 為mindspore設定運作背景context
    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
    import time
    a = time.time()
    main()
    b = time.time()
    print(b-a)

最終結果：

多次運作後，平均運作時間：

41秒

運作環境：

Ubuntu18.04系統

i7-9700HQ

筆記本顯示卡 1660ti

======================================================================================

發現一個神奇的事情，如果我們把context的模式設定為 GRAPH_MODE

也就是：

context.set_context(mode=context.GRAPH_MODE, device_target='GPU')

那麼運作過程中會不停的提示警告：

[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:07.536.303 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1116905_construct_wrapper, J user: 1116905_construct_wrapper:construct{[0]: 7496, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:07.664.157 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1117054_construct_wrapper, J user: 1117054_construct_wrapper:construct{[0]: 7497, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:07.787.667 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1117203_construct_wrapper, J user: 1117203_construct_wrapper:construct{[0]: 7498, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:07.906.649 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1117352_construct_wrapper, J user: 1117352_construct_wrapper:construct{[0]: 7499, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:08.021.086 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1117501_construct_wrapper, J user: 1117501_construct_wrapper:construct{[0]: 7500, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:08.136.975 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1117650_construct_wrapper, J user: 1117650_construct_wrapper:construct{[0]: 7501, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:08.271.804 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1117799_construct_wrapper, J user: 1117799_construct_wrapper:construct{[0]: 7502, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:08.380.832 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1117948_construct_wrapper, J user: 1117948_construct_wrapper:construct{[0]: 7503, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:08.489.950 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1118097_construct_wrapper, J user: 1118097_construct_wrapper:construct{[0]: 7504, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:08.599.613 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1118246_construct_wrapper, J user: 1118246_construct_wrapper:construct{[0]: 7505, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:08.707.115 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1118395_construct_wrapper, J user: 1118395_construct_wrapper:construct{[0]: 7506, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}
[WARNING] OPTIMIZER(4150,python):2021-07-06-23:07:08.812.025 [mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc:860] FindPrimalJPair] J operation has no relevant primal call in the same graph. Func graph: 1118544_construct_wrapper, J user: 1118544_construct_wrapper:construct{[0]: 7507, [1]: x, [2]: y, [3]: ValueNode<UMonad> U}

具體代碼：

MindSpore 如何實作一個線性回歸 —— Demo示例

import mindspore
import numpy as np #引入numpy科學計算庫
import matplotlib.pyplot as plt #引入繪圖庫
np.random.seed(123) #随機數生成種子
#from sklearn.model_selection import train_test_split#從sklearn裡面引出訓練與測試集劃分

import mindspore.nn as nn
import mindspore.ops as ops
from mindspore import Tensor
from mindspore import ParameterTuple, Parameter
from mindspore import dtype as mstype


# 訓練資料集
def creat_dataset():
    n_x=2*np.random.rand(500,1)#随機生成一個0-2之間的，大小為（500，1）的向量
    n_y=5+3*n_x+np.random.randn(500,1)#随機生成一個線性方程的，大小為（500，1）的向量
    x = Tensor(n_x, dtype=mindspore.float32)
    y = Tensor(n_y, dtype=mindspore.float32)
    return x, y


class Net(nn.Cell):
    def __init__(self, input_dims, output_dims):
        super(Net, self).__init__()
        self.matmul = ops.MatMul()

        self.weight_1 = Parameter(Tensor(np.random.randn(input_dims, 128), dtype=mstype.float32), name='weight_1')
        self.bias_1 = Parameter(Tensor(np.zeros(128), dtype=mstype.float32), name='bias_1')
        self.weight_2 = Parameter(Tensor(np.random.randn(128, 64), dtype=mstype.float32), name='weight_2')
        self.bias_2 = Parameter(Tensor(np.zeros(64), dtype=mstype.float32), name='bias_2')
        self.weight_3 = Parameter(Tensor(np.random.randn(64, output_dims), dtype=mstype.float32), name='weight_3')
        self.bias_3 = Parameter(Tensor(np.zeros(output_dims), dtype=mstype.float32), name='bias_3')

    def construct(self, x):
        x = self.matmul(x, self.weight_1)+self.bias_1
        x = self.matmul(x, self.weight_2)+self.bias_2
        x = self.matmul(x, self.weight_3)+self.bias_3
        return x


class LossNet(nn.Cell):
    def __init__(self, net):
        super(LossNet, self).__init__()
        self.net = net
        self.pow = ops.Pow()
        self.mean = ops.ReduceMean()

    def construct(self, x, y):
        _x = self.net(x)
        loss = self.mean(self.pow(_x - y, 2))
        return loss


class GradNetWrtX(nn.Cell):
    def __init__(self, net):
        super(GradNetWrtX, self).__init__()
        self.net = net
        self.params = ParameterTuple(net.trainable_params())
        self.grad_op = ops.GradOperation(get_by_list=True)

    def construct(self, x, y):
        gradient_function = self.grad_op(self.net, self.params)
        return gradient_function(x, y)


def train(epochs, loss_net, x, y, print_flag=False):
    # 建構加和操作
    ass_add = ops.AssignAdd()
    para_list = loss_net.trainable_params()

    for epoch in range(epochs):
        grad_net = GradNetWrtX(loss_net)
        grad_list = grad_net(x, y)

        for para, grad in zip(para_list, grad_list):
            ass_add(para, -0.000001*grad)

        if print_flag and (epoch%100 == 0):
            print("epoch: %s, loss: %s"%(epoch, loss_net(x, y)))


def main():
    epochs = 10000
    x, y = creat_dataset()

    net = Net(x.shape[-1], y.shape[-1])
    loss_net = LossNet(net)
    train(epochs, loss_net, x, y, False)

    y_hat = net(x)

    fig=plt.figure(figsize=(8,6))#确定畫布大小
    plt.title("Dataset")#标題名
    plt.xlabel("First feature")#x軸的标題
    plt.ylabel("Second feature")#y軸的标題
    plt.scatter(x.asnumpy(), y.asnumpy())#設定為散點圖
    plt.scatter(x.asnumpy(), y_hat.asnumpy())#設定為散點圖
    plt.show()#繪制出來


if __name__ == '__main__':
    """ 設定運作的背景context """
    from mindspore import context
    # 為mindspore設定運作背景context
    #context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')

    import time
    a = time.time()
    main()
    b = time.time()
    print(b-a)

View Code

最終結果：

============================================================

# 為mindspore設定運作背景context

context.set_context(mode=context.GRAPH_MODE, device_target='GPU')

context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')

具體原因是什麼，這裡也是搞不太清楚？？？

本文作為嘗試使用mindspore功能，具體原因也就不深究了。

MindSpore 如何實作一個線性回歸 —— Demo示例

繼續閱讀

來自python的【條件控制/語句循環/break/continue/else/pass】一、條件控制二、語句循環

無法解析的外部符号 wmain，該符号在函數 "void cdecl mainCRTStartupHelper(struct HINSTANCE *,unsigned short con......

TestLink導出用例轉換工具(XML2Excel)

YAML簡介和PyYAML安全操作YAML支援的類型YAML的優點：yaml的基本文法python操作

Small tricks

libsvm for python 安裝

學習軟體測試基礎測試第七天

Zeppelin 配置通路 REST APIApache Zeppelin Configuration REST API

【Torch】最簡潔logging使用指南

27. Remove Element(清單)題目代碼

Cloud Studio初體驗

使用 ctypes 進行 Python 和 C 的混合程式設計

【python】【資料處理】畫多元資料分布圖

【python】netconf協定對接管理裝置

「Python 網絡自動化」NETCONF —— Python 使用 NETCONF 管理配置 H3C 網絡裝置

在python中建立excel并寫入