def forward(self, x, t): self.t = t self.y = 1 / (1 + np.exp(-x)) self.loss = cross_entropy_error(np.c_[1 - self.y, self.y], self.t) return self.loss
def estimate_E_out(f, w, n_rounds=1000): ''' Estima o erro fora da amostra de h[w](x), gerando uma nova amostra e calculando o erro de entropia cruzada sobre ela. ''' dataset = generate_dataset(f, n_rounds) return myround(cross_entropy_error(w, dataset))
def loss(self, x, t): z = self.predict(x) #y = softmax(z) y = functions.relu(z) loss = functions.cross_entropy_error(y, t) return loss
def forward(self, x: np.ndarray, t: np.ndarray) -> float: self.t = t self.y = softmax(x) if self.t.size == self.y.size: self.t = self.t.argmax(axis=1) loss = cross_entropy_error(self.y, self.t) return loss
def forward(self, x, t): self.t = t self.y = softmax(x) if self.t.size == self.y.size: self.t = self.t.argmax(axis=1) loss = cross_entropy_error(self.y , self.t) return loss
def forward(self, x, t): self.t = t self.y = softmax(x) # 教師ラベルがone-hotベクトルの場合、正解のインデックスに変換 if self.t.size == self.y.size: self.t = self.t.argmax(axis=1) loss = cross_entropy_error(self.y, self.t) return loss
def forward(self, x, t): self.t = t self.y = softmax(x) # 정답 레이블이 원핫 벡터일 경우 정답의 인덱스로 변환 if self.t.size == self.y.size: self.t = self.t.argmax(axis=1) loss = cross_entropy_error(self.y, self.t) return loss
def loss(self, x, t): """ x : array-like input t : array-like true label """ z = self.predict(x) y = softmax(z) loss = cross_entropy_error(y, t) return loss
def loss(self, x, t): """Loss function using cross entropy error Args: x (numpy.ndarray): image data which mean input to NN t (numpy.ndarray): labels Returns: float: result of cross entropy error """ y = self.predict(x) return cross_entropy_error(y, t)
def forward(self, x, t): """forward propagation Args: x (numpy.ndarray): input t (numpy.ndarray): train data Returns: float: cross entropy error """ self.t = t self.y = softmax(x) self.loss = cross_entropy_error(self.y, self.t) return self.loss
def forward(self, x, t): """順伝播 SoftmaxWithLossレイヤの順伝播の結果を返す Args: x (ndarray): 入力 t (ndarray): 教師ラベル """ self.t = t self.y = softmax(x) # 教師ラベルがone-hotベクトルの場合、正解のインデックスに変換 if self.t.size == self.y.size: self.t = self.t.argmax(axis=1) loss = cross_entropy_error(self.y, self.t) return loss
def loss(self, x, t): y = self.predict(x) return f.cross_entropy_error(y, t)
def forward(self, x, true): self.true = true self.pred = f.softmax(x) self.loss = f.cross_entropy_error(self.pred, self.true) return self.loss
def forward(self, x, t): self.t = t self.y = softmax(x) self.loss = 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): #lossの算出 by cross_entropy_error y = self.predict(x) return cross_entropy_error(y,t)
import numpy as np """データ""" x = np.array([[0.1, 0.8]]) w1 = np.array([[10, 7], [0.8, 6]]) b1 = np.array([[1, 1]]) w2 = np.array([[0.4, 30], [0.8, 0.2]]) b2 = np.array([[1, 1]]) t = np.array([[1, 0]]) learning_rate = 0.02 for i in range(1): a1 = np.dot(x, w1) + b1 z1 = relu(a1) a2 = np.dot(z1, w2) + b2 y = softmax(a2) loss = cross_entropy_error(y, t) """共通の偏微分""" dEdY = -(t / y) # [dEdy1, dEdy2] S = np.sum(np.exp(a2)) dYdS = np.exp(a2) / np.square(S) # [dY1dS, dY2dS] print("dw2_11: ", (-(t[0][0] / y[0][0]) * (np.exp(a2[0][1]) / np.square(S))) * np.exp(a2[0][0]) * z1[0][0]) print("dw2_12: ", (-(t[0][0] / y[0][0]) * (np.exp(a2[0][1]) / np.square(S))) * np.exp(a2[0][1]) * z1[0][0]) print("dw2_21: ", (-(t[0][0] / y[0][0]) * (np.exp(a2[0][1]) / np.square(S))) * np.exp(a2[0][0]) * z1[0][1]) print("dw2_22: ",
def evaluate_E_in(w, dataset): ''' Calcula o erro dentro da amostra (rotulada) dataset, de uma hipótese parametrizada por w. ''' return myround(cross_entropy_error(w, dataset))
def loss(self, x, t): #xは入力データ,tは正解ラベル z = self.predict(x) y = softmax(z) loss = cross_entropy_error(y, t) return loss
def forward(self, x: np.ndarray, t: np.ndarray) -> np.ndarray: self.t = t self.y = 1 / (1 + np.exp(-x)) self.loss = cross_entropy_error(np.c_[1 - self.y, self.y], self.t) return self.loss
def loss(self, t): self.t = t self.error = cross_entropy_error(self.y, self.t) return self.error
import numpy as np import matplotlib.pylab as plt import sys,os sys.path.append("../lib") sys.path.append(os.pardir) import functions from dataset.mnist import load_mnist t = [0,0,1,0,0,0,0,0,0,0] y_good = [0.1, 0.05, 0.6, 0.0, 0.05, 0.1, 0.0, 0.1, 0.0, 0.0] print(functions.mean_squared_error(np.array(y_good), np.array(t))) y_bad = [0.1, 0.05, 0.2, 0.0, 0.05, 0.1, 0.0, 0.1, 0.8, 0.0] print(functions.mean_squared_error(np.array(y_bad), np.array(t))) print(functions.cross_entropy_error(np.array(y_good), np.array(t))) print(functions.cross_entropy_error(np.array(y_bad), np.array(t)))
def loss(self, y, t): # loss를 계산 error = cross_entropy_error(y, t) return error