def numerical_gradient_net(): gradient = { 'w1': numerical_gradient2(loss, params['w1']), 'b1': numerical_gradient2(loss, params['b1']) } return gradient
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib')) from mnist import load_mnist from common import softmax, cross_entropy_error, numerical_gradient2 except ImportError: print('Library Module Can Not Fount') x = np.array([0.6, 0.9]) # 입력(x) 2 vector t = np.array([0., 0., 1.]) # label(one-hot) 3 vector def forward_progation(w): a = np.dot(x, w) y = softmax(a) return y #softmax(x @ w) def loss(w): #softmax y = forward_progation(w) e = cross_entropy_error(y, t) return e _w = np.array([[0.02, 0.224, 0.135], [0.01, 0.052, 0.345]]) # weight, 2*3 matrix g = numerical_gradient2(loss, _w) print(g)