def backwardada(self,D,lr,mu,nest,l1,l2):
derivterm=D*self.Act_derivative(self.Z)
Dout=np.empty((D.shape[0],self.A.shape[1]))
padrows=int(np.ceil(((self.weights.shape[0]-1)/2)))
padder=np.vstack((np.zeros((padrows,derivterm.shape[1])),derivterm,np.zeros((padrows,derivterm.shape[1]))))
for k in range(self.weights[0,:,0].shape[0]):
Dout[:,k]=SC(padder,self.weights[::-1,k,:],mode='valid').reshape(-1)
for l in range(self.weights[0,0,:].shape[0]):
self.weights[:,k,l]-=lr*SC(self.A[:,k],derivterm[:,l], mode="valid")
self.biases-=lr*(np.sum(derivterm, axis=0))
return Dout
def forwardnest(self,A,mu,p=1):
self.A=A
self.weights=np.random.randn(self.filtersize,self.inputs,self.filters)*np.sqrt(2/(self.inputs+self.filters))
self.biases=np.random.randn(1,self.filters)*np.sqrt(2/(self.inputs+self.filters))
padrows=int(np.ceil(((self.weights.shape[0]-1)/2)))
padA=np.vstack((np.zeros((padrows,self.A.shape[1])),self.A,np.zeros((padrows,self.A.shape[1]))))
self.H=np.empty((self.A.shape[0],self.filters))
for k in range(self.filters):
self.H[:,k]=SC(padA,self.weights[:,:,k], mode="valid").reshape(-1)
self.H+=self.biases
self.Z=ReLU(self.H)
return self.Z
def forward(self,A,p=1):
self.A=A
if not self.weights.size:
self.weightinit()
padrows=int((self.weights.shape[0]-1)/2)
padA=np.vstack((np.zeros((padrows,self.A.shape[1])),self.A,np.zeros((padrows,self.A.shape[1]))))
self.H=np.empty((self.A.shape[0],self.filters))
counter=0
for k in range(self.filters):
# print(self.weights[:,:,k].shape)
self.H[:,k]=SC(padA,self.weights[:,:,k], mode="valid").reshape(-1)
self.H+=self.biases
self.Z=ReLU(self.H)
return self.Z