為您找到相關(guān)結(jié)果5個
Pytorch模型定義與深度學習自查手冊_python_腳本之家
1 2 def Sigmoid(x): return 1/(np.exp(-x)+1) torch.nn.LogSigmoid 1 2 def LogSigmoid(x): return np.log(1/(np.exp(-x)+1)) torch.nn.Tanh 1 2 def Tanh(x): return (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x)) torch.nn.Tanhshrink 1 2 def Tanhshrink(x): return...
www.dbjr.com.cn/article/2502...htm 2025-5-25
pytorch1.60 torch.nn在pycharm中無法自動智能提示的解決_python_腳本之...
Hardtanh as Hardtanh, LeakyReLU as LeakyReLU, LogSigmoid as LogSigmoid, LogSoftmax as LogSoftmax, PReLU as PReLU, \ RReLU as RReLU, ReLU as ReLU, ReLU6 as ReLU6, SELU as SELU, Sigmoid as Sigmoid, Softmax as Softmax, \ Softmax2d as Softmax2d, Softmin as Softmin, Softplus as Soft...
www.dbjr.com.cn/python/316373c...htm 2025-5-27
純numpy數(shù)值微分法實現(xiàn)手寫數(shù)字識別_python_腳本之家
z1=sigmoid(a1) a2=np.dot(z1,W2)+b2 y=softmax(a2) return y #損失函數(shù) def loss(self,x,t): y=self.predict(x) return cross_entropy_error(y,t) #數(shù)值微分法 def numerical_gradient(self,x,t): loss_W=lambda W:self.loss(x,t) grads={} grads['W1']=numerical_gradient(loss_W,self...
www.dbjr.com.cn/article/2578...htm 2025-5-13
keras實現(xiàn)多種分類網(wǎng)絡(luò)的方式_python_腳本之家
predictions = layers.Dense(1, activation='sigmoid')(merged) # ---構(gòu)建模型,并擬合訓練--- model = Model([left_input, right_input], predictions) model.fit([left_data, right_data], targets) 以上這篇keras實現(xiàn)多種分類網(wǎng)絡(luò)的方式就是小編分享給大家的全部內(nèi)容了,希望能給大家一個參考,也希望大家多...
www.dbjr.com.cn/article/1885...htm 2025-6-8