sw1 = swish1.forward(z1)
sw2 = dense2.forward(sw1)
y_pre = swish2.forward(sw2)
# loss = loss_fn.loss(y_true, y_pred)
# print("loss: ", loss)
# print("loss's mean: ",np.mean(loss))
# sigloss = loss_fn.loss(y_true, y_pre)
# print("SIG loss: ", sigloss)
# print("sig loss's mean: ",np.mean(sigloss))
# dldy_pred = loss_fn.gradient(y_true , y_pred)
# print("lldy: ",dldy_pred)
# dldz2 = activation2.backward(dldy_pred)
# print("dldz2: ",dldz2)
# dLda1 = dense2.backward(dldz2)
# print("dLda1: ",dLda1)
# dLz1 = sigmoid.backward(dLda1)
# dLdw = dense.backward(dLz1)
d1 = loss_fn.gradient(y_true, y_pre)
# a = swish2.gradient(d1)
# print(d1)
d2 = swish2.backward(d1)
d3 = dense2.backward(d2)
d4 = swish1.backward(d3)
d5 = dense.backward(d4)
print(d2)
# Dense -> Activation -> Dense -> Activation -> y_pred
z1 = dense.forward(x)
a1 = activation1.forward(z1)
print("Activation Value:", a1)
z2 = dense2.forward(a1)
a2 = activation2.forward(z2)
y_pred = a2
loss = loss_func.loss(y_true, y_pred)
print("Individual Loss:", loss)
total_loss = np.mean(loss)
print("Total Loss:", total_loss)
#Backward Propagation
dLdy_pred = loss_func.gradient(y_true, y_pred)
print("dLdy:", dLdy_pred)
'''
dydz=activation2.gradient(z2)
dLdz2-dLdy_pred*dydz
'''
dLdz2 = activation2.backward(dLdy_pred)
dLda1 = dense2.backward(dLdz2)
dLdz1 = sigmoid.backward(dLda1)
dLdw = dense.backward(dLdz1)