update

README.md

@@ -1,3 +1,5 @@
 # SES2020spring
 
 ## Another day
+
+## The third day

pytorch_cancer_torch.py

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+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+import pandas as pd
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn import preprocessing
+
+#利用sklean自带的cancer数据集
+X,T= datasets.load_breast_cancer(return_X_y=True)
+#数据预处理，标准化
+for i in range(X.shape[1]):
+    mean = np.mean(X[:,i])
+    std = np.std(X[:,i])
+    X[:,i] = (X[:,i] - mean)/std
+#划分训练集和测试集，比例为7:3，交叉验证
+X_train,X_test,T_train,T_test = train_test_split(X,T,train_size=0.7,test_size=0.3)
+x_data = Variable(torch.Tensor(X_train))
+y_data = Variable(torch.Tensor(T_train))
+x_test = Variable(torch.Tensor(X_test))
+t_test = Variable(torch.Tensor(T_test))
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.linear = torch.nn.Linear(x_data.shape[1], 1) # 30 in and 1 out
+
+    def forward(self, x):
+        #y_pred = torch.sigmoid(self.linear(x))
+        y_pred = self.linear(x).sigmoid()
+        return y_pred
+
+# Our model    
+model = Model()
+
+criterion = torch.nn.BCELoss(reduction="mean")
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+f = open("result.txt", 'w')
+# Training loop
+for epoch in range(1000):
+    # Forward pass: Compute predicted y by passing x to the model
+    y_pred = model(x_data)
+    pre=y_pred.ge(0.5).float()
+    correct = 0
+    for i in range(y_data.size(0)):
+        if pre[i] == y_data[i]:
+            correct += 1
+    acc = correct / y_data.size(0)  #计算Accuracy
+    print(epoch)
+    print('accuracy is',acc)
+
+    # 计算损失
+    loss = criterion(y_pred, y_data)
+    print(' loss is', loss.data.item())
+
+    # Zero gradients, perform a backward pass, and update the weights.
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+for f in model.parameters():
+    print('data is')
+    print(f.data)
+    print(f.grad)
+
+w = list(model.parameters())
+w0 = w[0].data.numpy()
+w1 = w[1].data.numpy()
+
+import matplotlib.pyplot as plt
+
+print("Final gradient descend:", w)
+# plot the data and separating line
+plt.scatter(x_data[:,0], x_data[:,1], c=y_data.reshape(len(x_data)), s=100, alpha=0.7)
+x_axis = np.linspace(-6, 6, 100)
+y_axis = -(w1[0] + x_axis*w0[0][0]) / w0[0][1]
+line_up, = plt.plot(x_axis, y_axis,'r--', label='gradient descent')
+plt.legend(handles=[line_up])
+plt.xlabel('X(1)')
+plt.ylabel('X(2)')
+plt.show()
+f.close() 

pytorch_cancer_torch_update.py

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+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+import pandas as pd
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn import preprocessing
+
+#利用sklean自带的cancer数据集
+X,T= datasets.load_breast_cancer(return_X_y=True)
+#数据预处理，标准化
+for i in range(X.shape[1]):
+    mean = np.mean(X[:,i])
+    std = np.std(X[:,i])
+    X[:,i] = (X[:,i] - mean)/std
+#划分训练集和测试集，比例为7:3，交叉验证
+X_train,X_test,T_train,T_test = train_test_split(X,T,train_size=0.7,test_size=0.3)
+x_data = Variable(torch.Tensor(X_train))
+y_data = Variable(torch.Tensor(T_train))
+x_test = Variable(torch.Tensor(X_test))
+t_test = Variable(torch.Tensor(T_test))
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        #self.hidden = torch.nn.Linear(x_data.shape[1])
+        self.linear = torch.nn.Linear(x_data.shape[1], 1) # 30 in and 1 out
+
+    def forward(self, x):
+        #y_pred = torch.sigmoid(self.linear(x))
+        y_pred = self.linear(x).sigmoid()
+        return y_pred
+
+# Our model    
+model = Model()
+
+criterion = torch.nn.BCELoss(reduction="mean")
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+f = open("result.txt", 'w')
+# Training loop
+for epoch in range(1000):
+    # Forward pass: Compute predicted y by passing x to the model
+    y_pred = model(x_data)
+    pre=y_pred.ge(0.5).float()
+    correct = 0
+    for i in range(y_data.size(0)):
+        if pre[i] == y_data[i]:
+            correct += 1
+    acc = correct / y_data.size(0)  #计算Accuracy
+    print(epoch,file = f)
+    print('accuracy is',acc,file = f)
+
+    # 计算损失
+    loss = criterion(y_pred, y_data)
+    print(' loss is', loss.data.item(),file = f)
+
+    # Zero gradients, perform a backward pass, and update the weights.
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+for f in model.parameters():
+    print('data is')
+    print(f.data)
+    print(f.grad)
+
+w = list(model.parameters())
+w0 = w[0].data.numpy()
+w1 = w[1].data.numpy()
+
+y_pred1 = model(x_test)#预测测试集
+pre1=y_pred1.ge(0.5).float()
+correct1 = 0
+for i in range(t_test.size(0)):
+    if pre1[i] == t_test[i]:
+        correct1 += 1
+acc1 = correct1 / t_test.size(0)  #计算测试集的Accuracy
+print('test accuracy is',acc)
+
+import matplotlib.pyplot as plt
+
+print("Final gradient descend:", w)
+# plot the data and separating line
+plt.scatter(x_data[:,0], x_data[:,1], c=y_data.reshape(len(x_data)), s=100, alpha=0.7)
+x_axis = np.linspace(-6, 6, 100)
+y_axis = -(w1[0] + x_axis*w0[0][0]) / w0[0][1]
+line_up, = plt.plot(x_axis, y_axis,'r--', label='gradient descent')
+plt.legend(handles=[line_up])
+plt.xlabel('X(1)')
+plt.ylabel('X(2)')
+plt.show()
+
+