update

Author: L1aoXingyu

cahpter3_MLP/linear_model.py renamed to cahpter3_MLP/linear_model/linear_model.py

@@ -0,0 +1,100 @@
+34.62365962451697,78.0246928153624,0
+30.28671076822607,43.89499752400101,0
+35.84740876993872,72.90219802708364,0
+60.18259938620976,86.30855209546826,1
+79.0327360507101,75.3443764369103,1
+45.08327747668339,56.3163717815305,0
+61.10666453684766,96.51142588489624,1
+75.02474556738889,46.55401354116538,1
+76.09878670226257,87.42056971926803,1
+84.43281996120035,43.53339331072109,1
+95.86155507093572,38.22527805795094,0
+75.01365838958247,30.60326323428011,0
+82.30705337399482,76.48196330235604,1
+69.36458875970939,97.71869196188608,1
+39.53833914367223,76.03681085115882,0
+53.9710521485623,89.20735013750205,1
+69.07014406283025,52.74046973016765,1
+67.94685547711617,46.67857410673128,0
+70.66150955499435,92.92713789364831,1
+76.97878372747498,47.57596364975532,1
+67.37202754570876,42.83843832029179,0
+89.67677575072079,65.79936592745237,1
+50.534788289883,48.85581152764205,0
+34.21206097786789,44.20952859866288,0
+77.9240914545704,68.9723599933059,1
+62.27101367004632,69.95445795447587,1
+80.1901807509566,44.82162893218353,1
+93.114388797442,38.80067033713209,0
+61.83020602312595,50.25610789244621,0
+38.78580379679423,64.99568095539578,0
+61.379289447425,72.80788731317097,1
+85.40451939411645,57.05198397627122,1
+52.10797973193984,63.12762376881715,0
+52.04540476831827,69.43286012045222,1
+40.23689373545111,71.16774802184875,0
+54.63510555424817,52.21388588061123,0
+33.91550010906887,98.86943574220611,0
+64.17698887494485,80.90806058670817,1
+74.78925295941542,41.57341522824434,0
+34.1836400264419,75.2377203360134,0
+83.90239366249155,56.30804621605327,1
+51.54772026906181,46.85629026349976,0
+94.44336776917852,65.56892160559052,1
+82.36875375713919,40.61825515970618,0
+51.04775177128865,45.82270145776001,0
+62.22267576120188,52.06099194836679,0
+77.19303492601364,70.45820000180959,1
+97.77159928000232,86.7278223300282,1
+62.07306379667647,96.76882412413983,1
+91.56497449807442,88.69629254546599,1
+79.94481794066932,74.16311935043758,1
+99.2725269292572,60.99903099844988,1
+90.54671411399852,43.39060180650027,1
+34.52451385320009,60.39634245837173,0
+50.2864961189907,49.80453881323059,0
+49.58667721632031,59.80895099453265,0
+97.64563396007767,68.86157272420604,1
+32.57720016809309,95.59854761387875,0
+74.24869136721598,69.82457122657193,1
+71.79646205863379,78.45356224515052,1
+75.3956114656803,85.75993667331619,1
+35.28611281526193,47.02051394723416,0
+56.25381749711624,39.26147251058019,0
+30.05882244669796,49.59297386723685,0
+44.66826172480893,66.45008614558913,0
+66.56089447242954,41.09209807936973,0
+40.45755098375164,97.53518548909936,1
+49.07256321908844,51.88321182073966,0
+80.27957401466998,92.11606081344084,1
+66.74671856944039,60.99139402740988,1
+32.72283304060323,43.30717306430063,0
+64.0393204150601,78.03168802018232,1
+72.34649422579923,96.22759296761404,1
+60.45788573918959,73.09499809758037,1
+58.84095621726802,75.85844831279042,1
+99.82785779692128,72.36925193383885,1
+47.26426910848174,88.47586499559782,1
+50.45815980285988,75.80985952982456,1
+60.45555629271532,42.50840943572217,0
+82.22666157785568,42.71987853716458,0
+88.9138964166533,69.80378889835472,1
+94.83450672430196,45.69430680250754,1
+67.31925746917527,66.58935317747915,1
+57.23870631569862,59.51428198012956,1
+80.36675600171273,90.96014789746954,1
+68.46852178591112,85.59430710452014,1
+42.0754545384731,78.84478600148043,0
+75.47770200533905,90.42453899753964,1
+78.63542434898018,96.64742716885644,1
+52.34800398794107,60.76950525602592,0
+94.09433112516793,77.15910509073893,1
+90.44855097096364,87.50879176484702,1
+55.48216114069585,35.57070347228866,0
+74.49269241843041,84.84513684930135,1
+89.84580670720979,45.35828361091658,1
+83.48916274498238,48.38028579728175,1
+42.2617008099817,87.10385094025457,1
+99.31500880510394,68.77540947206617,1
+55.34001756003703,64.9319380069486,1
+74.77589300092767,89.52981289513276,1

cahpter3_MLP/poly_model.py renamed to cahpter3_MLP/linear_model/poly_model.py

@@ -0,0 +1,84 @@
+__author__ = 'sherlock'
+
+import torch
+from torch import nn
+from torch.autograd import Variable
+import numpy as np
+import matplotlib.pyplot as plt
+
+torch.manual_seed(2017)
+# get the data and preprocessing
+with open('data.txt', 'r') as f:
+    data_list = f.readlines()
+    data_list = [i.split('\n')[0] for i in data_list]
+    data_list = [i.split(',') for i in data_list]
+    data = [(float(i[0]), float(i[1]), float(i[2])) for i in data_list]
+
+x0 = list(filter(lambda x: x[-1] == 0.0, data))
+x1 = list(filter(lambda x: x[-1] == 1.0, data))
+plot_x0_0 = [i[0] for i in x0]
+plot_x0_1 = [i[1] for i in x0]
+plot_x1_0 = [i[0] for i in x1]
+plot_x1_1 = [i[1] for i in x1]
+
+plt.plot(plot_x0_0, plot_x0_1, 'ro', label='x_0')
+plt.plot(plot_x1_0, plot_x1_1, 'bo', label='x_1')
+plt.legend(loc='best')
+
+# transform to tensor
+np_data = np.array(data, dtype=np.float32)
+x_data = torch.from_numpy(np_data[:, 0:2])
+y_data = torch.from_numpy(np_data[:, -1])
+
+
+# define logistic regression
+class LogisticRegression(nn.Module):
+    def __init__(self):
+        super(LogisticRegression, self).__init__()
+        self.lr = nn.Linear(2, 1)
+        self.sm = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.lr(x)
+        x = self.sm(x)
+        return x
+
+
+logistic_model = LogisticRegression()
+if torch.cuda.is_available():
+    logistic_model.cuda()
+
+criterion = nn.BCELoss()
+optimizer = torch.optim.SGD(logistic_model.parameters(), lr=1e-3,
+                            momentum=0.9)
+
+for epoch in range(50000):
+    if torch.cuda.is_available():
+        x = Variable(x_data).cuda()
+        y = Variable(y_data).cuda()
+    else:
+        x = Variable(x_data)
+        y = Variable(y_data)
+    # ==================forward==================
+    out = logistic_model(x)
+    loss = criterion(out, y)
+    print_loss = loss.data[0]
+
+    # ===================backward=================
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if (epoch+1) % 1000 == 0:
+        print('epoch {}'.format(epoch+1))
+        print('loss is {:.4f}'.format(print_loss))
+
+torch.save(logistic_model.state_dict, './logistic_regression.pth')
+# ====================plot classification=================
+w0, w1 = logistic_model.lr.weight[0]
+w0 = w0.data[0]
+w1 = w1.data[0]
+b = logistic_model.lr.bias.data[0]
+plot_x = np.arange(30, 100, 0.1)
+plot_y = (-w0 * plot_x - b) / w1
+plt.plot(plot_x, plot_y)
+plt.show()

cahpter3_MLP/logistic_regression/data.txt

@@ -1,6 +1,9 @@
 import torch
 import numpy as np
 from torch.autograd import Variable
+from torch.utils.data import Dataset, DataLoader
+from torchvision.datasets import ImageFolder
+import pandas as pd
 
 # =============================Tensor================================
 # Define 3x2 matrix with given values
@@ -58,3 +61,40 @@
 
 y.backward(torch.FloatTensor([1, 0.1, 0.01]))
 print(x.grad)
+
+
+# ==============================nn.Module=================================
+class net_name(nn.Module):
+    def __init__(self, other_arguments):
+        super(net_name, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size)
+        # other network layer
+
+    def forward(self, x):
+        x = self.conv1(x)
+        return x
+
+# ============================Dataset====================================
+
+
+class myDataset(Dataset):
+    def __init__(self, csv_file, txt_file, root_dir, other_file):
+        self.csv_data = pd.read_csv(csv_file)
+        with open(txt_file, 'r') as f:
+            data_list = f.readlines()
+        self.txt_data = data_list
+        self.root_dir = root_dir
+
+    def __len__(self):
+        return len(self.csv_data)
+
+    def __getitem__(self, idx):
+        data = (self.csv_data[idx], self.txt_data[idx])
+        return data
+
+
+dataiter = DataLoader(myDataset, batch_size=32, shuffle=True,
+                      collate_fn=default_collate)
+
+dset = ImageFolder(root='root_path', transform=None,
+                   loader=default_loader)