用mindspore实现mnist感知器

MindSpore是华为公司最新开源的深度学习框架，具有高效、灵活、跨平台等特点，适合实现各类深度学习模型。Mnist感知器是入门级别的机器学习模型之一，该模型可以通过MindSpore来实现。

首先，需要导入必要的包和库：

import mindspore.nn as nn
import mindspore.context as context
from mindspore import Tensor
from mindspore.common.initializer import Normal
from mindspore.train.serialization import save_checkpoint, load_checkpoint
from mindspore import Parameter

import matplotlib.pyplot as plt
from sklearn.datasets import make_classification

其次，需要定义Mnist感知器的类：

class MnistPerceptron(nn.Cell):
    def __init__(self, weight_init='normal', bias_init='zeros'):
        super(MnistPerceptron, self).__init__()
        self.weight = Parameter(
            weight_init(1, 784), name='weight')
        self.bias = Parameter(
            bias_init(1), name='bias')
        self.matmul = nn.MatMul()
        self.add = nn.TensorAdd()
        self.relu = nn.ReLU()

    def construct(self, x):
        output = self.matmul(x, self.weight)
        output = self.add(output, self.bias)
        output = self.relu(output)
        return output

其中weight和bias参数分别是特征和偏差的系数，matmul、add和relu是Mnist感知器中的三个基本操作。

接着，需要训练数据和测试数据进行训练：

train_X, train_y = make_classification(
    n_samples=500, n_features=784, n_classes=10)
train_data = Tensor(train_X, mindspore.float32)
train_label = Tensor(train_y, mindspore.int32)
test_X, test_y = make_classification(
    n_samples=200, n_features=784, n_classes=10)
test_data = Tensor(test_X, mindspore.float32)
test_label = Tensor(test_y, mindspore.int32)

数据加载完毕后，需要定义优化器和损失函数：

weight_init = Normal(0.02)
bias_init = Normal(0.02)
net = MnistPerceptron(weight_init, bias_init)
lr = 0.01
momentum = 0.9
optimizer = nn.Momentum(net.trainable_params(), lr, momentum)
loss_fn = nn.SoftmaxCrossEntropyWithLogits()

然后，可以开始模型的训练了：

context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
loss_list = []

net.set_train()
for i in range(500):
    optimizer.clear_grad()
    output = net(train_data)
    loss = loss_fn(output, train_label)
    loss_list.append(loss.asnumpy())
    loss.backward()
    optimizer.step()

最后，可以使用测试数据对模型进行验证：

net.set_eval()
correct = 0
total = test_data.shape[0]
output = net(test_data)
output = nn.Softmax()(output)
pred_value = output.argmax(axis=1).asnumpy()
for i in range(total):
    if pred_value[i] == test_y[i]:
        correct += 1
print("Accuracy: {}".format(correct / total))

Mnist感知器的实现过程就是这样，我们使用MindSpore框架对其进行了实现和训练。