diff --git a/exercise1/data_normalized.csv b/exercise1/data_normalized.csv
new file mode 100644
index 0000000..8117fe1
--- /dev/null
+++ b/exercise1/data_normalized.csv
@@ -0,0 +1,569 @@
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+0.002682116,0.002039941,0.002713529,0.003969064,0.002024476,0.001952164,0.00482714,0.004990121,0.00167441,0.001573667,0.005101003,0.001814008,0.004705088,0.006914491,0.00257094,0.001994197,0.002864302,0.003656137,0.000953068,0.001963136,0.002749221,0.001806939,0.00272154,0.00404549,0.001872069,0.001460497,0.002651812,0.003398203,0.001248084,0.001489579
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+0.00206508,0.002558353,0.002069544,0.002302809,0.001542067,0.001723092,0.001830909,0.001904797,0.001542475,0.001580663,0.001979675,0.001552594,0.002100212,0.002115303,0.001473423,0.002573625,0.002606416,0.002319725,0.001127597,0.001802436,0.002050303,0.002335332,0.002075973,0.002243281,0.00151226,0.00213856,0.002197252,0.002174482,0.001343811,0.001637176
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+0.000965363,0.002235826,0.00091572,0.000485734,0.000959893,0.000734714,0,0,0.001539564,0.001646711,0.001673007,0.002062423,0.001562435,0.000834357,0.001794416,0.000321444,0,0,0.002289416,0.001288843,0.001021479,0.002078665,0.000969333,0.000536072,0.001194407,0.000445407,0,0,0.001739441,0.001473668
diff --git a/exercise1/lr.py b/exercise1/lr.py
new file mode 100644
index 0000000..f7ff308
--- /dev/null
+++ b/exercise1/lr.py
@@ -0,0 +1,73 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+N = 100
+D = 2
+
+X = np.random.randn(N,D)*2
+
+# center the first N/2 points at (-2,-2)
+X[:N//2,:] = X[:N//2,:] - 2*np.ones((N//2,D))
+
+# center the last N/2 points at (2, 2)
+X[N//2:,:] = X[N//2:,:] + 2*np.ones((N//2,D))
+
+# labels: first N/2 are 0, last N/2 are 1
+T = np.array([0]*(N//2) + [1]*(N//2)).reshape(100,1)
+
+x_data = Variable(torch.Tensor(X))
+y_data = Variable(torch.Tensor(T))
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.linear = torch.nn.Linear(2, 1) # 2 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.RMSprop(model.parameters(), lr=100)
+
+# Training loop
+for epoch in range(1000):
+    # Forward pass: Compute predicted y by passing x to the model
+    y_pred = model(x_data)
+    
+    # Compute and print loss
+    loss = criterion(y_pred, y_data)
+    print(epoch, 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[:,0], X[:,1], c=T.reshape(N), s=100, alpha=0.5)
+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()
\ No newline at end of file
diff --git a/exercise1/test.py b/exercise1/test.py
new file mode 100644
index 0000000..4c4c3ab
--- /dev/null
+++ b/exercise1/test.py
@@ -0,0 +1,57 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+X = np.loadtxt(open("C:/Users/ThinkPad/PycharmProjects/opticalsimulator/untitled/data_normalized.csv", "rb"), delimiter=",", skiprows=0)
+D = X.shape[1]
+N = X.shape[0]
+
+
+# labels: first N/2 are 0, last N/2 are 1
+T = np.array([0] * (N // 2) + [1] * (N // 2 + 1)).reshape(N, 1)
+
+x_data = Variable(torch.Tensor(X))
+y_data = Variable(torch.Tensor(T))
+
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.hidden = torch.nn.Linear(D,10) # hidden layer
+        self.linear = torch.nn.Linear(10, 1)  # D in and 1 out
+
+    def forward(self, x):
+        # y_pred = torch.sigmoid(self.linear(x))
+        x = self.hidden(x).sigmoid()
+        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=0.1)
+
+# Training loop
+for epoch in range(1000):
+    # Forward pass: Compute predicted y by passing x to the model
+    y_pred = model(x_data)
+
+    # Compute and print loss
+    loss = criterion(y_pred, y_data)
+    print(epoch, 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())
+print(w)
\ No newline at end of file
diff --git "a/exercise1/\351\231\210\345\255\220\345\272\267_U201713689_\346\234\272\345\231\250\345\255\246\344\271\240ex1.pdf" "b/exercise1/\351\231\210\345\255\220\345\272\267_U201713689_\346\234\272\345\231\250\345\255\246\344\271\240ex1.pdf"
new file mode 100644
index 0000000..aa3857e
Binary files /dev/null and "b/exercise1/\351\231\210\345\255\220\345\272\267_U201713689_\346\234\272\345\231\250\345\255\246\344\271\240ex1.pdf" differ