human
I am working on the file "DL0110EN/5.1.2.dropoutAssignment.ipynb".
The first task is:
<a id="ref0"></a>
<h2 align=center>Get Some Data </h2>

Create a nonlinearly separable dataset:
Can you write Python code for it?

Can you provide a the Python code you requested?
assistant
#Here is the Python code you requested.

from torch.utils.data import Dataset, DataLoader

class Data(Dataset):
    def __init__(self,N_SAMPLES = 1000,noise_std=0.1,train=True):
      
        a=np.matrix([-1,1,2,1,1,-3,1]).T
        
        self.x = np.matrix(np.random.rand(N_SAMPLES,2))

        self.f=np.array(a[0]+(self.x)*a[1:3]+np.multiply(self.x[:,0], self.x[:,1])*a[4]+np.multiply(self.x, self.x)*a[5:7]).flatten()
        self.a=a
       
        self.y=np.zeros(N_SAMPLES)
        self.y[self.f> 0]=1
        self.y=torch.from_numpy(self.y).type(torch.LongTensor)
        self.x=torch.from_numpy(self.x).type(torch.FloatTensor)
        self.x = self.x+noise_std*torch.randn(self.x.size())
        self.f=torch.from_numpy(self.f)
        self.a=a
        if train==True:
            torch.manual_seed(1)
         
            self.x = self.x+noise_std*torch.randn(self.x.size())
            torch.manual_seed(0)
        
            
    def __getitem__(self,index):    
        return self.x[index],self.y[index]
    def __len__(self):
        return self.len
    def plot(self):
        X=data_set.x.numpy()
        y=data_set.y.numpy()
        h = .02
        x_min, x_max = X[:, 0].min() , X[:, 0].max()
        y_min, y_max = X[:, 1].min(), X[:, 1].max() 
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h),np.arange(y_min, y_max, h))
        Z=data_set.fun(np.c_[xx.ravel(), yy.ravel()]).flatten()
        f=np.zeros(Z.shape)
        f[Z>0]=1
        f=f.reshape(xx.shape)
        plt.title('True decision boundary  and sample points with noise ')
      

        plt.plot(self.x[self.y==0,0].numpy(),self.x[self.y==0,1].numpy(),'bo',label='y=0' ) 
        plt.plot(self.x[self.y==1,0].numpy(), self.x[self.y==1,1].numpy(),'ro',label='y=1' )
        plt.contour(xx, yy, f, cmap=plt.cm.Paired)
        plt.xlim(0,1)
        plt.ylim(0,1)
        plt.legend()
    def fun(self,x):
        
        x=np.matrix(x)

        out=np.array(self.a[0]+(x)*self.a[1:3]+np.multiply(x[:,0], x[:,1])*self.a[4]+np.multiply(x, x)*self.a[5:7])
        out=np.array(out)
       ...