fprintf(stderr,"calculate activation function:\n"); fprintf(stderr,"type: tanh result: \n"); fbc::activation_function_tanh(src.data(),dst.data(),length); fbc::print_matrix(dst); fprintf(stderr,"type: tanh derivative result: \n"); fbc::activation_function_tanh_derivative(dst.data(),...
x):#return self.tools.sigmoid(x)returnself.tools.tanh(x)defactivationFunctionDerivative(self, x):#return self.tools.sigmoidDerivative(x)returnself.tools.tanhDerivative(x)defrandomWeight(self):returnrandom.uniform
The positive line is steeper than the negative one, and both lines start at two learnable thresholds, and their slopes are Tanh’s derivative values at these thresholds. These thresholds can be trained and updated along the gradient descent direction of cost function. So ReLTanh can improve ...
classTanh(Activation): def_build_activation_vars(self,input_act_vars): returntf.math.tanh(input_act_vars) def_get_gradient_at_activation(self,activation_vars): #derivative: https://blogs.cuit.columbia.edu/zp2130/derivative_of_tanh_function/ ...
#plt.legend(['Sigmoid', 'Sigmoid derivative', 'Relu', 'Relu derivative', 'Tanh', 'Tanh derivative']) # y3 y33 # plt.legend(loc='upper left') # 将图例放在左上角 # save pic # plt.savefig('plot_test.png', dpi=100) plt.savefig(r"./") ...
Controller层负责具体的业务模块流程的控制，在此层里面要调用Serice层的接口来控制业务流程，控制的配置也...
Differentiable Activation Function –The activation function must be discrete. The network can learn by back-propagating loss from the output nodes to hidden units during the training stage. The backpropagation technique adjusts the weights of neurons in hidden layers using the derivative of their act...