def forward(self, x, t=None):
self.y = softmax(x)
self.t = t
if self.t is None: #단순 소프트맥스 처리
return self.y
# 약전파시 전치행렬에 대한 내적을 대비
if self.t.ndim == 1:
self.t = self.t[np.newaxis, :]
loss = cross_entropy_error(self.y, self.t)
return loss
# test
(train_x, train_t), (test_x, test_t) = load_mnist(normalize=True,
flatten=True,
one_hot_label=True)
print(train_x.shape) # 60,000 * 784
print(train_t.shape) # 60,000 * 10
train_size = len(train_x)
batch_size = 10
batch_mask = np.random.choice(train_size, batch_size)
print(batch_mask)
train_x_batch = train_x[batch_mask]
train_t_batch = train_t[batch_mask]
print(train_x_batch.shape) # 10 * 784
print(train_t_batch.shape) # 10 * 10
# test
# 만약에 batch_size가 3인 경우
t = np.array([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])
y1 = np.array([0.1, 0.05, 0.7, 0., 0.02, 0.03, 0.1, 0., 0., 0.])
y2 = np.array([0.1, 0.05, 0., 0.6, 0.02, 0.03, 0.1, 0.1, 0., 0.])
y3 = np.array([0., 0.92, 0.02, 0., 0.03, 0.03, 0., 0., 0., 0.])
print(cross_entropy_error(y1, t))
print(cross_entropy_error(y2, t))
print(cross_entropy_error(y3, t))
def loss(x, t):
y = forward_propagation(x)
e = cross_entropy_error(y, t)
return e
def loss(w): #softmax
y = forward_progation(w)
e = cross_entropy_error(y, t)
return e
def loss():
y = forward_progation()
e = cross_entropy_error(y, t)
return e
def loss(w, x, t):
a = np.dot(x, w)
y = softmax(a) # softmax(x @ w)
e = cross_entropy_error(y, t)
return e
def forward(self, x, t):
self.t = t
self.y = softmax(x)
self.loss = cross_entropy_error(self.y, self.t)
return self.loss
# test1
(train_x, train_t), (test_x, test_t) = load_mnist(normalize=True,
flatten=True,
one_hot_label=True)
print(train_x.shape) # 60000 x 784
print(train_t.shape) # 60000 x 10
train_size = len(train_x)
batch_size = 10
batch_mask = np.random.choice(train_size, batch_size)
print(batch_mask)
train_x_batch = train_x[batch_mask]
train_t_batch = train_t[batch_mask]
print(train_x_batch.shape) # 10 x 784
print(train_t_batch.shape) # 10 x 10
# test3
# 만약에 batch-size가 3인 경우
t = np.array([[0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
y = np.array([[0.1, 0.05, 0.7, 0., 0.02, 0.03, 0.1, 0., 0., 0.],
[0.1, 0.05, 0., 0.4, 0.02, 0.03, 0.1, 0.3, 0., 0.],
[0., 0.92, 0.02, 0., 0.02, 0.03, 0.1, 0., 0., 0.]])
print(cross_entropy_error(y, t))
# 신경망학습: 교차 엔트로피 손실함수(Cross Entropy Error, CEE)
import os
import sys
import numpy as np
from pathlib import Path
try:
sys.path.append(os.path.join(Path(os.getcwd()).parent, 'lib'))
from common import cross_entropy_error
except ImportError:
print('Library Module Can Not Found')
t = np.array([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.])
y1 = np.array([0.1, 0.05, 0.7, 0., 0.02, 0.03, 0., 0.1, 0., 0.])
y2 = np.array([0.1, 0.05, 0.05, 0.6, 0.02, 0.03, 0.05, 0.1, 0., 0.])
y3 = np.array([0., 0., 0.95, 0.02, 0.01, 0.01, 0., 0.1, 0., 0.])
# test
print(cross_entropy_error(y1, t)) # vector가 들어감
print(cross_entropy_error(y2, t))
print(cross_entropy_error(y3, t))
def forward(self, a, t):
self.y = co.softmax(a)
self.t = t
self.loss = co.cross_entropy_error(self.y, self.t)
return self.loss
def loss(self, x, t):
z = self.predict(x)
y = softmax(z)
loss = cross_entropy_error(y, t)
return loss
def loss(self, x, t):
z = self.predict(x)
y = common.soft_max(z)
return common.cross_entropy_error(y, t)
def loss(self, x, t):
y = self.predict(x)
return common.cross_entropy_error(y, t)
def loss(w):
a = np.dot(x, w)
y = softmax(a)
e = cross_entropy_error(y, t)
return e
