import ANN import matplotlib.pyplot as plt from sklearn.datasets import load_iris data = load_iris() input = data.data #.T[:2].T target = data.target numLayers = 3 iterations = 20000 # input = [[0,0],[0,1],[1,0],[1,1]] # target = [0,1,1,0] nn1 = ANN.FNN(numLayers, input, target, eta=0.005) #output, error = nn1.train(iterations) target = nn1.__target__ error = [] output = [] out, e = nn1.train() error.append(e) output.append(out) plt.ion() f, ax = plt.subplots(1,2) im = ax[0].imshow(target, interpolation = 'none', cmap='viridis', origin='lower', aspect='auto', vmin= 0., vmax = 1.) ax[0].set_yticks([0,1,2]) ax[0].set_yticklabels(['0', '1', '2'])
