신경망 각 층의 구현(회귀)
import numpy as npimport matplotlib.pyplot as pltX = np.arange(-1.0, 1.0, 0.2)Y = np.arange(-1.0, 1.0, 0.2)Z = np.zeros((10,10))w_im = np.array([[4.0,4.0],[4.0,4.0]])w_mo = np.array([[1.0],[-1.0]])b_im = np.array([3.0,-3.0])b_mo = np.array([0.1])def middle_layer(x,w,b): u = np.dot(x,w)+b return 1/(1+np.exp(-u))def output_layer(x,w,b): u = np.dot(x,w)+b return ufor i in range(10): ..
3층 신경망(3 layer neural network)
#3 layer neural networkimport numpy as npdef sigmoid(x): return 1 / (1 + np.exp(-x))def identity_function(x): return xdef init_network(): network = {} network['W1'] = np.array([[0.1,0.3,0.5],[0.2,0.4,0.6]]) network['b1'] = np.array([0.1,0.2,0.3]) network['W2'] = np.array([[0.1,0.4],[0.2,0.5],[0.3,0.6]]) network['b2'] = np.array([0.1,0.2]) network['W3'] = np.array([[0.1,0...
