[人工智能]PaddlePaddle实现一个简单的回归模型

PaddlePaddle实现一个简单的回归模型，用来拟合一个多项式函数

import paddle
import numpy as np
import os
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")

print(paddle.__version__)

#定义函数
def poly_function(datain):
    temp = np.zeros(len(datain))
    for i in range(len(datain)):
        x = datain[i]
        temp[i] = x * x * x * x + 2 * x * x * x - 1 * x * x + 0.5 * x

    return temp

#准备训练集
data = np.random.uniform(-1, 1, 800)
orderdata = np.sort(data)
label = poly_function(orderdata)
orderdata = orderdata.reshape(800, 1)
label = label.reshape(800, 1)
datas = np.concatenate((orderdata, label), axis=1)

#定义函数
class Regressor(paddle.nn.Layer):
    def __init__(self):
        super(Regressor, self).__init__()
        self.fc = paddle.nn.Linear(1, 20)
        self.ac1 = paddle.nn.Sigmoid()
        self.fc2 = paddle.nn.Linear(20, 20)
        self.ac2 = paddle.nn.LeakyReLU()
        self.fc3 = paddle.nn.Linear(20, 1)

    def forward(self, inputs):
        x = self.fc(inputs)
        x = self.ac1(x)
        x = self.fc2(x)
        x = self.ac2(x)
        pred = self.fc3(x)
        return pred


train_nums = []
loss_seq = []
import paddle.nn.functional as F

#代码框架参考PaddlePaddle官方教程，波士顿房价预测
#训练
def train(model):
    model.train()
    BATCH_SIZE = 40
    EPOCH_NUM = 1000
    train_num = 0
    optimizer = paddle.optimizer.Adam(learning_rate=0.0002, parameters=model.parameters())
    for epoch_id in range(EPOCH_NUM):

        np.random.shuffle(datas)
        mini_batches = [datas[k: k + BATCH_SIZE] for k in range(0, len(datas), BATCH_SIZE)]
        for batch_id, data in enumerate(mini_batches):
            features_np = np.array(datas[:, 0], np.float32).reshape(800, 1)
            labels_np = np.array(datas[:, 1], np.float32).reshape(800, 1)
            features = paddle.to_tensor(features_np)

            labels = paddle.to_tensor(labels_np)
            # 前向计算
            y_pred = model(features)

            loss = F.mse_loss(y_pred, label=labels)
            loss_np = loss.detach().numpy()

            loss.backward()
            optimizer.step()
            optimizer.clear_grad()
            if batch_id % 30 == 0 and epoch_id % 50 == 0:
                print("Pass:%d,Cost:%0.5f" % (epoch_id, loss_np))
            train_num = train_num + BATCH_SIZE
            train_nums.append(train_num)
            loss_seq.append(loss_np)



model = Regressor()
train(model)
#准备测试集
test_data = np.random.uniform(-1, 1, 200)
test_data = np.sort(test_data)


def test(data, model):
    datanp = np.array(data, np.float32).reshape(len(data), 1)

    features = paddle.to_tensor(datanp)
    pred = model(features)
    # plt.plot(datanp, pred.detach().numpy(), color='red', label='training cost')
    return pred


def showresult(test_data):
    test_label = poly_function(test_data)
    pred = test(test_data, model)
    plt.clf()
    plt.plot(test_data, test_label, color='red', label='ground truth')
    plt.plot(test_data, pred.detach().numpy(), color='blue', label='predict')
    plt.legend()
    plt.savefig('result')
    plt.show()

if __name__ == '__main__':
    showresult(test_data)