Replace the ReLU function with sigmoidal units (This is code for Python)

import numpy as np import matplotlib.pyplot as plt import torch from torch.autograd import Variable

N = 100 # number of points per class D = 2 # dimensionality K = 3 # number of classes X = np.zeros((N*K,D)) # data matrix (each row = single example) y = np.zeros(N*K, dtype='uint8') # class labels for j in range(K): ix = range(N*j,N*(j+1)) r = np.linspace(0.0,1,N) # radius t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta X[ix] = np.c_[r*np.sin(t), r*np.cos(t)] y[ix] = j # lets visualize the data: plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral) plt.show()