def numerical_gradient(self, x, t):
loss_W = lambda W: self.loss(x, t)
grads = {}
grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
return grads
def numerical_gradient(self, x, t):
loss_W = lambda W: self.loss(x, t)
grads = {}
grads["W1"] = numerical_gradient(loss_W, self.params["W1"])
grads["b1"] = numerical_gradient(loss_W, self.params["b1"])
grads["W2"] = numerical_gradient(loss_W, self.params["W2"])
grads["b2"] = numerical_gradient(loss_W, self.params["b2"])
return grads
def batch_gradient(self, Gs, ts):
loss_f = lambda f: self.batch_loss(Gs, ts)
# 損失関数を計算
grad_W = numerical_gradient(loss_f, self.params["W"])
grad_A = numerical_gradient(loss_f, self.params["A"])
grad_b = numerical_gradient(loss_f, self.params["b"])
grads = {"W": grad_W, "A": grad_A, "b": grad_b}
return grads
def numerical_gradient(self, G, x, t):
loss_f = lambda f : self.loss(G, x, t)
# 損失関数を計算
grad_W = numerical_gradient(loss_f, self.params["W"])
grad_A = numerical_gradient(loss_f, self.params["A"])
grad_b = numerical_gradient(loss_f, self.params["b"])
grads = {
"W" : grad_W,
"A" : grad_A,
"b" : grad_b
}
return grads
def numerical_gradient(self, x, t):
"""勾配を求める（数値微分）
Parameters
----------
x : 入力データ
t : 教師ラベル
Returns
-------
各層の勾配を持ったディクショナリ変数
grads['W1']、grads['W2']、...は各層の重み
grads['b1']、grads['b2']、...は各層のバイアス
"""
loss_W = lambda W: self.loss(x, t)
grads = {}
for idx in range(1, self.hidden_layer_num+2):
grads['W' + str(idx)] = numerical_gradient(loss_W, self.params['W' + str(idx)])
grads['b' + str(idx)] = numerical_gradient(loss_W, self.params['b' + str(idx)])
return grads
import sys
import os
import numpy as np
from pathlib import Path
try:
print(
os.path.join(Path(os.getcwd()).parent, 'lib')
) #이렇게 출력-> C:\Users\GX701GXR\PycharmProjects\linear-algebra-basic\lib 그런데 바로 넣으면 안됨. /나 \\로 해야함
sys.path.append(
'C:/Users/GX701GXR/PycharmProjects/linear-algebra-basic/lib')
#sys.path.append('..\lib')
#sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
import common as cm
except ImportError:
print('lib ??')
def f(v):
return np.sum(v**2, axis=0)
print(cm.numerical_gradient(f, np.array([3., 4.])))
# 기울기(gradient)
import os
import sys
from pathlib import Path
import numpy as np
try:
# sys.path.append('D:/deep-learning/PycharmProjects/linear-algebra-basics/lib')
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from common import numerical_gradient
except ImportError:
print('Library Module Can Not Found')
def f(x):
return np.sum(x**2, axis=0)
# 함수 테스트
# print(f(np.array([3., 4.])))
gra1 = numerical_gradient(f, np.array([3., 4.]))
gra2 = numerical_gradient(f, np.array([-1., -1.5]))
gra3 = numerical_gradient(f, np.array([-0.25, -0.25]))
print(gra1, gra2, gra3)
import numpy as np
try:
#print(os.getcwd())
#sys.path.append('F:\deep-learning\dowork\PycharmProjects\linear-algebra-basics\lib')
#p1 = Path(os.getcwd()).parent + '\\lib'
#p2 = os.path.join(Path(os.getcwd()).parent, 'lib')
#print(p2)
#sys.path.append(p2)
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
#import common as cm
from common import numerical_gradient
except ImportError:
print('Library Module Can Not Found')
def f(x):
return np.sum(x**2, axis=0)
#함수테스트
gra1 = numerical_gradient(f, np.array([3., 4.]))
gra2 = numerical_gradient(f, np.array([0., 2.]))
gra3 = numerical_gradient(f, np.array([3., 0.]))
print(gra1, gra2, gra3)
class simpleNet:
def __init__(self):
self.W = np.random.randn(2, 3)
def predict(self, x):
return np.dot(x, self.W)
def loss(self, x, t):
z = self.predict(x)
y = softmax(z)
loss = cross_entropy_error(y, t)
return loss
net = simpleNet()
print(net.W)
x = np.array([0.6, 0.9])
p = net.predict(x)
print(p)
print(np.argmax(p))
t = np.array([0, 0, 1])
print(net.loss(x, t))
def f(W):
return net.loss(x, t)
print(numerical_gradient(f, net.W))
self.W = np.random.randn(2, 3)
def predict(self, x):
return np.dot(x, self.W)
def loss(self, x, t):
z = self.predict(x)
y = softmax(z)
loss = cross_entropy_error(y, t)
return loss
net = SimpleNet()
x = np.array([0.6, 0.9])
p = net.predict(x)
print(p)
print(np.argmax(p))
t = np.array([0, 0, 1])
print(net.loss(x, t))
def f(W):
return net.loss(x, t)
dW = numerical_gradient(f, net.W)
print(dW)