exercise1-wbx

exercise1/testml.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon May 18 11:38:02 2020
+
+@author: bxw
+"""
+import torch
+import torch.nn.functional as F
+import torch.nn.init as init
+import math
+import numpy as np
+import pandas as pd
+from torch.autograd import Variable
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+#从本地读取数据
+xy = pd.read_csv('./data2.csv').values
+x = Variable(torch.from_numpy(xy[:,0:-1]))
+y = Variable(torch.from_numpy(xy[:,0]))
+#划分训练数据和测试数据
+x_train, x_test,y_train,y_test= train_test_split(x.numpy(),y.numpy(), test_size=0.3,random_state=2018)   
+ss = StandardScaler()  
+#特征归一化
+x_train = Variable(torch.tensor(ss.fit_transform(x_train)))
+x_test = Variable(torch.tensor(ss.fit_transform(x_test)))
+y_train = Variable(torch.tensor(y_train))
+y_test = Variable(torch.tensor(y_test))
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.l1 = torch.nn.Linear(31, 33)  
+        self.l2 = torch.nn.Linear(33, 33)
+        self.l3 = torch.nn.Linear(33, 33)
+        self.l4 = torch.nn.Linear(33, 1)
+    def forward(self, x):
+        x = F.tanh(self.l1(x.float()))
+        x = F.tanh(self.l2(x))
+        x = F.tanh(self.l3(x))
+        x = self.l4(x)
+        return x
+model = Model()
+#model.apply(weights_init)
+criterion = torch.nn.BCEWithLogitsLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-1, momentum=0.9)
+Loss = []
+Acc = []
+EPOCHS = 1000
+for epoch in range(EPOCHS):
+    y_pred = model(x_train)
+    loss = criterion(y_pred, y_train.float().view(-1,1))
+    preds = torch.tensor(y_pred >= 0)
+    corrects = torch.sum(preds.byte() == y_train.view(-1,1).byte())
+    acc = corrects.item()/len(x_train)
+    if epoch%100 == 0:
+        print("corrects:",corrects)
+        print("epoch = {0}, loss = {1}, acc = {2}".format(epoch, loss, acc))
+        Loss.append(loss)
+        Acc.append(acc)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    plt.plot(range(len(Loss)), Loss)
+w = list(model.parameters())
+w0 = w[0].data.numpy()
+w1 = w[1].data.numpy()
+
+import matplotlib.pyplot as plt
+
+print("Final gradient descend:", w)
+
+plt.ylabel('loss')
+plt.xlabel('epochs')
+plt.show()
+plt.plot(range(len(Acc)), Acc)
+plt.ylabel('acc')
+plt.xlabel('epochs')
+plt.show()