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)
