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MindSpore 如何实现一个线性回归 —— Demo示例

有态度的萌狮子 2022-05-19 阅读 41
MindSpore（深度学习计算框架）python.net随机数生成虚拟化云计算

如何使用 MindSpore  实现一个简单的  线性回归呢？？？






根据前面的mindspore的基本操作的学习写出了下面的 一个简单的线性回归算法。


import mindspore
import numpy as np #引入numpy科学计算库
import matplotlib.pyplot as plt #引入绘图库
np.random.seed(123) #随机数生成种子
#from sklearn.model_selection import train_test_split#从sklearn里面引出训练与测试集划分

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


# 训练数据集
def creat_dataset():
    n_x=2*np.random.rand(500,1)#随机生成一个0-2之间的，大小为（500，1）的向量
    n_y=5+3*n_x+np.random.randn(500,1)#随机生成一个线性方程的，大小为（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):
    # 构建加和操作
    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))#确定画布大小
    plt.title("Dataset")#标题名
    plt.xlabel("First feature")#x轴的标题
    plt.ylabel("Second feature")#y轴的标题
    plt.scatter(x.asnumpy(), y.asnumpy())#设置为散点图
    plt.scatter(x.asnumpy(), y_hat.asnumpy())#设置为散点图
    plt.show()#绘制出来


if __name__ == '__main__':
    """ 设置运行的背景context """
    from mindspore import context
    # 为mindspore设置运行背景context
    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
    import time
    a = time.time()
    main()
    b = time.time()
    print(b-a)



最终结果：

MindSpore 如何实现一个线性回归 —— Demo示例_随机数生成




多次运行后，平均运行时间：

 41秒


 

MindSpore 如何实现一个线性回归 —— Demo示例_python_02





运行环境：

Ubuntu18.04系统

i7-9700HQ

笔记本显卡 1660ti



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




发现一个神奇的事情，如果我们把context的模式设置为  GRAPH_MODE

也就是：

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



那么运行过程中会不停的提示警告：


[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}





MindSpore 如何实现一个线性回归 —— Demo示例_随机数生成_03




具体代码：

MindSpore 如何实现一个线性回归 —— Demo示例_随机数生成_04MindSpore 如何实现一个线性回归 —— Demo示例_MindSpore（深度学习计算框架）_05

import mindspore
import numpy as np #引入numpy科学计算库
import matplotlib.pyplot as plt #引入绘图库
np.random.seed(123) #随机数生成种子
#from sklearn.model_selection import train_test_split#从sklearn里面引出训练与测试集划分

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


# 训练数据集
def creat_dataset():
    n_x=2*np.random.rand(500,1)#随机生成一个0-2之间的，大小为（500，1）的向量
    n_y=5+3*n_x+np.random.randn(500,1)#随机生成一个线性方程的，大小为（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):
    # 构建加和操作
    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))#确定画布大小
    plt.title("Dataset")#标题名
    plt.xlabel("First feature")#x轴的标题
    plt.ylabel("Second feature")#y轴的标题
    plt.scatter(x.asnumpy(), y.asnumpy())#设置为散点图
    plt.scatter(x.asnumpy(), y_hat.asnumpy())#设置为散点图
    plt.show()#绘制出来


if __name__ == '__main__':
    """ 设置运行的背景context """
    from mindspore import context
    # 为mindspore设置运行背景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


最终结果：

MindSpore 如何实现一个线性回归 —— Demo示例_MindSpore（深度学习计算框架）_06



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




可以看到不同 context 模式的设置，运行时间相差20倍左右。


# 为mindspore设置运行背景context
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
具体原因是什么，这里也是搞不太清楚？？？

本文作为尝试使用mindspore功能，具体原因也就不深究了。


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