def demoLinear():
eaData = eaDataLinear([2, -3.4], [4.2], m=1000)
net = eaNetLinear(eaData, L=1, n=4)
nnInitWb(net)
nnFit(eaData, net, learn_rate=0.02)
Ea.show(net, 3)
plotCost("Linear Costs", net.costs, netInfo=eaNetInfo(net))
def demoLogistics():
eaData = eaDataTFRing()
net = eaNetLogistics(eaData)
nnInitWb(net)
nnFit(eaData, net, learn_rate=0.1)
Ea.show(net, 3)
plotPredict(net, eaData.X, eaData.Y, cmap=['#0099CC', '#FF6666','#6622FF'])
plotCost("Logistics Costs", net.costs,netInfo=eaNetInfo(net))
def debug(self):
print(help(self.keypoints[0]))
pts = []
for pt in self.keypoints:
ePt = Ea()
ePt.angle = pt.angle
ePt.class_id = pt.class_id
ePt.octave = pt.octave
ePt.pt = (pt.pt[0], pt.pt[1])
ePt.response = pt.response
ePt.size = pt.size
pts.append(ePt)
Ea.show(pts, 500)
pass
return 1
eaActivFun = Ea()
eaActivFun.linear.g = linear
eaActivFun.linear.dg = dLinear
eaActivFun.linear.name = 'linear'
eaActivFun.sigmoid.g = sigmoid
eaActivFun.sigmoid.dg = dSigmoid
eaActivFun.sigmoid.name = 'sigmoid'
eaActivFun.tanh.g = tanh
eaActivFun.tanh.dg = dTanh
eaActivFun.tanh.name = 'tanh'
eaActivFun.relu.g = relu
eaActivFun.relu.dg = dRelu
eaActivFun.relu.name = 'relu'
eaActivFun.leakyRelu.g = leakyRelu
eaActivFun.leakyRelu.dg = dLeakyRelu
eaActivFun.leakyRelu.name = 'leakyRelu'
eaActivFun.softmax.g = softmax
eaActivFun.softmax.dg = dSoftmax
eaActivFun.softmax.name = 'softmax'
if __name__ == '__main__':
Ea.show(eaActivFun)
loss = -1 / m * np.sum(
Y * np.log(A) + (1 - Y) * np.log(1 - A), axis=1, keepdims=False)
loss = np.sum(loss)
return loss
# softmax+交叉熵 结果的交叉熵导函数（对应多分类问题）
def dCrossSoftmax(A, Y):
return A - Y
eaCostFun = Ea()
eaCostFun.L1.J = bias
eaCostFun.L1.dJ = dBias
eaCostFun.L1.name = 'bias'
eaCostFun.L2.J = variance
eaCostFun.L2.dJ = dVariance
eaCostFun.L2.name = 'variance'
eaCostFun.L3.J = cross
eaCostFun.L3.dJ = dCross
eaCostFun.L3.name = 'cross'
eaCostFun.L4.J = crossSoftmax
eaCostFun.L4.dJ = dCrossSoftmax
eaCostFun.L4.name = 'cross'
if __name__ == '__main__':
Ea.show(eaCostFun)