def gru2(curr_s2, prev_s2):
curr_s2 = tensor.reshape(curr_s2, s_shape2)
curr_s2_ = InputLayer(s_shape2, curr_s2)
prev_s2_ = InputLayer(s_shape2, prev_s2)
t_x_s_update2_ = CConv3DLayer(prev_s2_, curr_s2_, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3),
params=t_x_s_update2.params)
t_x_s_reset2_ = CConv3DLayer(prev_s2_, curr_s2_, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3),
params=t_x_s_reset2.params)
update2_ = SigmoidLayer(t_x_s_update2_)
comp_udpate_gate2_ = ComplementLayer(update2_)
reset_gate2_ = SigmoidLayer(t_x_s_reset2_)
rs2_ = EltwiseMultiplyLayer(reset_gate2_, prev_s2_)
t_x_rs2_ = CConv3DLayer(rs2_, curr_s2_, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3),
params=t_x_rs2.params)
tanh_t_x_rs2_ = TanhLayer(t_x_rs2_)
gru_out2_ = AddLayer(
EltwiseMultiplyLayer(update2_, prev_s2_),
EltwiseMultiplyLayer(comp_udpate_gate2_, tanh_t_x_rs2_))
print("gru_out2: ", gru_out2_.output_shape)
return gru_out2_.output
def gru3(curr_s3, prev_s3):
curr_s3 = tensor.reshape(curr_s3, s_shape3)
curr_s3_ = InputLayer(s_shape3, curr_s3)
prev_s3_ = InputLayer(s_shape3, prev_s3)
t_x_s_update3_ = CConv3DLayer(prev_s3_, curr_s3_, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3),
params=t_x_s_update3.params)
t_x_s_reset3_ = CConv3DLayer(prev_s3_, curr_s3_, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3),
params=t_x_s_reset3.params)
update3_ = SigmoidLayer(t_x_s_update3_)
comp_udpate_gate3_ = ComplementLayer(update3_)
reset_gate3_ = SigmoidLayer(t_x_s_reset3_)
rs3_ = EltwiseMultiplyLayer(reset_gate3_, prev_s3_)
t_x_rs3_ = CConv3DLayer(rs3_, curr_s3_, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3),
params=t_x_rs3.params)
tanh_t_x_rs3_ = TanhLayer(t_x_rs3_)
gru_out3_ = AddLayer(
EltwiseMultiplyLayer(update3_, prev_s3_),
EltwiseMultiplyLayer(comp_udpate_gate3_, tanh_t_x_rs3_))
print("gru_out3: ", gru_out3_.output_shape)
return gru_out3_.output
def gru4(curr_s4, prev_s4):
curr_s4 = tensor.reshape(curr_s4, s_shape4)
curr_s4_ = InputLayer(s_shape4, curr_s4)
prev_s4_ = InputLayer(s_shape4, prev_s4)
t_x_s_update4_ = CConv3DLayer(prev_s4_, curr_s4_, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3),
params=t_x_s_update4.params)
t_x_s_reset4_ = CConv3DLayer(prev_s4_, curr_s4_, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3),
params=t_x_s_reset4.params)
update4_ = SigmoidLayer(t_x_s_update4_)
comp_udpate_gate4_ = ComplementLayer(update4_)
reset_gate4_ = SigmoidLayer(t_x_s_reset4_)
rs4_ = EltwiseMultiplyLayer(reset_gate4_, prev_s4_)
t_x_rs4_ = CConv3DLayer(rs4_, curr_s4_, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3),
params=t_x_rs4.params)
tanh_t_x_rs4_ = TanhLayer(t_x_rs4_)
gru_out4_ = AddLayer(
EltwiseMultiplyLayer(update4_, prev_s4_),
EltwiseMultiplyLayer(comp_udpate_gate4_, tanh_t_x_rs4_))
print("gru_out4: ", gru_out4_.output_shape)
return gru_out4_.output
def gru5(curr_s5, prev_s5):
curr_s5 = tensor.reshape(curr_s5, s_shape5)
curr_s5_ = InputLayer(s_shape5, curr_s5)
prev_s5_ = InputLayer(s_shape5, prev_s5)
print("curr_s5: ", curr_s5_.output_shape)
print("prev_s5: ", prev_s5_.output_shape)
t_x_s_update5_ = CConv3DLayer(prev_s5_, curr_s5_, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3),
params=t_x_s_update5.params)
t_x_s_reset5_ = CConv3DLayer(prev_s5_, curr_s5_, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3),
params=t_x_s_reset5.params)
update5_ = SigmoidLayer(t_x_s_update5_)
comp_udpate_gate5_ = ComplementLayer(update5_)
reset_gate5_ = SigmoidLayer(t_x_s_reset5_)
rs5_ = EltwiseMultiplyLayer(reset_gate5_, prev_s5_)
t_x_rs5_ = CConv3DLayer(rs5_, curr_s5_, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3),
params=t_x_rs5.params)
tanh_t_x_rs5_ = TanhLayer(t_x_rs5_)
print("t_x_s_update5: ", t_x_s_update5_.output_shape)
print("t_x_s_reset5: ", t_x_s_reset5_.output_shape)
gru_out5_ = AddLayer(
EltwiseMultiplyLayer(update5_, prev_s5_),
EltwiseMultiplyLayer(comp_udpate_gate5_, tanh_t_x_rs5_))
print("gru_out5: ", gru_out5_.output_shape)
return gru_out5_.output
def network_definition(self):
self.x = tensor5()
self.is_x_tensor4 = False
img_w = self.img_w
img_h = self.img_h
n_gru_vox = [32, 32, 16, 8, 4]
n_convfilter = [16, 32, 64, 128, 256, 512]
n_deconvfilter = [2, 2, 8, 32, 128]
input_shape = (self.batch_size, 3, img_w, img_h)
x = InputLayer(input_shape)
conv1a = ConvLayer(x, (n_convfilter[0], 7, 7))
conv1b = ConvLayer(conv1a, (n_convfilter[0], 3, 3))
pool1 = PoolLayer(conv1b, padding=(0, 0)) # H/2->64
conv2a = ConvLayer(pool1, (n_convfilter[1], 3, 3))
conv2b = ConvLayer(conv2a, (n_convfilter[1], 3, 3))
conv2c = ConvLayer(pool1, (n_convfilter[1], 1, 1))
pool2 = PoolLayer(conv2c) # H/4->32
conv3a = ConvLayer(pool2, (n_convfilter[2], 3, 3))
conv3b = ConvLayer(conv3a, (n_convfilter[2], 3, 3))
conv3c = ConvLayer(pool2, (n_convfilter[2], 1, 1))
pool3 = PoolLayer(conv3c, padding=(0, 0)) # H/8->16
conv4a = ConvLayer(pool3, (n_convfilter[3], 3, 3))
conv4b = ConvLayer(conv4a, (n_convfilter[3], 3, 3))
conv4c = ConvLayer(pool3, (n_convfilter[3], 1, 1))
pool4 = PoolLayer(conv4c, padding=(0, 0)) # H/16->8
conv5a = ConvLayer(pool4, (n_convfilter[4], 3, 3))
conv5b = ConvLayer(conv5a, (n_convfilter[4], 3, 3))
conv5c = ConvLayer(pool4, (n_convfilter[4], 1, 1)) # H/32->4
pool5 = PoolLayer(conv5c, padding=(0, 0))
conv6a = ConvLayer(pool5, (n_convfilter[5], 3, 3))
conv6b = ConvLayer(conv6a, (n_convfilter[5], 3, 3))
conv6c = ConvLayer(pool5, (n_convfilter[5], 1, 1)) # H/32->4
def encoder(x):
input_ = InputLayer(input_shape, x)
conv1a_ = ConvLayer(input_, (n_convfilter[0], 7, 7), params=conv1a.params)
rect1a_ = LeakyReLU(conv1a_)
conv1b_ = ConvLayer(rect1a_, (n_convfilter[0], 3, 3), params=conv1b.params)
rect1b_ = LeakyReLU(conv1b_)
pool1_ = PoolLayer(rect1b_, padding=(0, 0))
conv2a_ = ConvLayer(pool1_, (n_convfilter[1], 3, 3), params=conv2a.params)
rect2a_ = LeakyReLU(conv2a_)
conv2b_ = ConvLayer(rect2a_, (n_convfilter[1], 3, 3), params=conv2b.params)
rect2b_ = LeakyReLU(conv2b_)
conv2c_ = ConvLayer(pool1_, (n_convfilter[1], 1, 1), params=conv2c.params)
res2_ = AddLayer(conv2c_, rect2b_)
pool2_ = PoolLayer(res2_)
conv3a_ = ConvLayer(pool2_, (n_convfilter[2], 3, 3), params=conv3a.params)
rect3a_ = LeakyReLU(conv3a_)
conv3b_ = ConvLayer(rect3a_, (n_convfilter[2], 3, 3), params=conv3b.params)
rect3b_ = LeakyReLU(conv3b_)
conv3c_ = ConvLayer(pool2_, (n_convfilter[2], 1, 1), params=conv3c.params)
res3_ = AddLayer(conv3c_, rect3b_)
pool3_ = PoolLayer(res3_, padding=(0, 0))
conv4a_ = ConvLayer(pool3_, (n_convfilter[3], 3, 3), params=conv4a.params)
rect4a_ = LeakyReLU(conv4a_)
conv4b_ = ConvLayer(rect4a_, (n_convfilter[3], 3, 3), params=conv4b.params)
rect4b_ = LeakyReLU(conv4b_)
conv4c_ = ConvLayer(pool3_, (n_convfilter[3], 1, 1), params=conv4c.params)
res4_ = AddLayer(conv4c_, rect4b_)
pool4_ = PoolLayer(res4_, padding=(0, 0))
conv5a_ = ConvLayer(pool4_, (n_convfilter[4], 3, 3), params=conv5a.params)
rect5a_ = LeakyReLU(conv5a_)
conv5b_ = ConvLayer(rect5a_, (n_convfilter[4], 3, 3), params=conv5b.params)
rect5b_ = LeakyReLU(conv5b_)
conv5c_ = ConvLayer(pool4_, (n_convfilter[4], 1, 1), params=conv5c.params)
res5_ = AddLayer(conv5c_, rect5b_)
pool5_ = PoolLayer(res5_, padding=(0, 0))
conv6a_ = ConvLayer(pool5_, (n_convfilter[5], 3, 3), params=conv6a.params)
rect6a_ = LeakyReLU(conv6a_)
conv6b_ = ConvLayer(rect6a_, (n_convfilter[5], 3, 3), params=conv6b.params)
rect6b_ = LeakyReLU(conv6b_)
conv6c_ = ConvLayer(pool5_, (n_convfilter[5], 1, 1), params=conv6c.params)
res6_ = AddLayer(conv6c_, rect6b_)
flat3_ = FlattenLayer(res3_)
flat4_ = FlattenLayer(res4_)
flat5_ = FlattenLayer(res5_)
flat6_ = FlattenLayer(res6_)
print("res3: ", res3_.output_shape)
print("res4: ", res4_.output_shape)
print("res5: ", res5_.output_shape)
print("res6: ", res6_.output_shape)
# pool6_ = PoolLayer(res6_)
return flat3_.output, flat4_.output, flat5_.output, flat6_.output
# return flat6_.output
# Set the shape of each resolution
s_shape5 = (self.batch_size, n_gru_vox[4], n_deconvfilter[4], n_gru_vox[4], n_gru_vox[4])
s_shape4 = (self.batch_size, n_gru_vox[3], n_deconvfilter[3], n_gru_vox[3], n_gru_vox[3])
s_shape3 = (self.batch_size, n_gru_vox[2], n_deconvfilter[2], n_gru_vox[2], n_gru_vox[2])
s_shape2 = (self.batch_size, n_gru_vox[1], n_deconvfilter[1], n_gru_vox[1], n_gru_vox[1])
## resolution 5
prev_s5 = InputLayer(s_shape5)
curr_s5 = InputLayer(s_shape5)
t_x_s_update5 = CConv3DLayer(prev_s5, curr_s5, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3))
t_x_s_reset5 = CConv3DLayer(prev_s5, curr_s5, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3))
reset_gate5 = SigmoidLayer(t_x_s_reset5)
rs5 = EltwiseMultiplyLayer(reset_gate5, prev_s5)
t_x_rs5 = CConv3DLayer(rs5, curr_s5, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3))
## resolution 4
prev_s4 = InputLayer(s_shape4)
curr_s4 = InputLayer(s_shape4)
t_x_s_update4 = CConv3DLayer(prev_s4, curr_s4, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3))
t_x_s_reset4 = CConv3DLayer(prev_s4, curr_s4, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3))
reset_gate4 = SigmoidLayer(t_x_s_reset4)
rs4 = EltwiseMultiplyLayer(reset_gate4, prev_s4)
t_x_rs4 = CConv3DLayer(rs4, curr_s4, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3))
# resolution 3
prev_s3 = InputLayer(s_shape3)
curr_s3 = InputLayer(s_shape3)
t_x_s_update3 = CConv3DLayer(prev_s3, curr_s3, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3))
t_x_s_reset3 = CConv3DLayer(prev_s3, curr_s3, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3))
reset_gate3 = SigmoidLayer(t_x_s_reset3)
rs3 = EltwiseMultiplyLayer(reset_gate3, prev_s3)
t_x_rs3 = CConv3DLayer(rs3, curr_s3, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3))
# resolution 4
prev_s2 = InputLayer(s_shape2)
curr_s2 = InputLayer(s_shape2)
t_x_s_update2 = CConv3DLayer(prev_s2, curr_s2, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3))
t_x_s_reset2 = CConv3DLayer(prev_s2, curr_s2, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3))
reset_gate2 = SigmoidLayer(t_x_s_reset2)
rs2 = EltwiseMultiplyLayer(reset_gate2, prev_s2)
t_x_rs2 = CConv3DLayer(rs2, curr_s2, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3))
def gru5(curr_s5, prev_s5):
curr_s5 = tensor.reshape(curr_s5, s_shape5)
curr_s5_ = InputLayer(s_shape5, curr_s5)
prev_s5_ = InputLayer(s_shape5, prev_s5)
print("curr_s5: ", curr_s5_.output_shape)
print("prev_s5: ", prev_s5_.output_shape)
t_x_s_update5_ = CConv3DLayer(prev_s5_, curr_s5_, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3),
params=t_x_s_update5.params)
t_x_s_reset5_ = CConv3DLayer(prev_s5_, curr_s5_, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3),
params=t_x_s_reset5.params)
update5_ = SigmoidLayer(t_x_s_update5_)
comp_udpate_gate5_ = ComplementLayer(update5_)
reset_gate5_ = SigmoidLayer(t_x_s_reset5_)
rs5_ = EltwiseMultiplyLayer(reset_gate5_, prev_s5_)
t_x_rs5_ = CConv3DLayer(rs5_, curr_s5_, (n_deconvfilter[4], n_deconvfilter[4], 3, 3, 3),
params=t_x_rs5.params)
tanh_t_x_rs5_ = TanhLayer(t_x_rs5_)
print("t_x_s_update5: ", t_x_s_update5_.output_shape)
print("t_x_s_reset5: ", t_x_s_reset5_.output_shape)
gru_out5_ = AddLayer(
EltwiseMultiplyLayer(update5_, prev_s5_),
EltwiseMultiplyLayer(comp_udpate_gate5_, tanh_t_x_rs5_))
print("gru_out5: ", gru_out5_.output_shape)
return gru_out5_.output
def gru4(curr_s4, prev_s4):
curr_s4 = tensor.reshape(curr_s4, s_shape4)
curr_s4_ = InputLayer(s_shape4, curr_s4)
prev_s4_ = InputLayer(s_shape4, prev_s4)
t_x_s_update4_ = CConv3DLayer(prev_s4_, curr_s4_, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3),
params=t_x_s_update4.params)
t_x_s_reset4_ = CConv3DLayer(prev_s4_, curr_s4_, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3),
params=t_x_s_reset4.params)
update4_ = SigmoidLayer(t_x_s_update4_)
comp_udpate_gate4_ = ComplementLayer(update4_)
reset_gate4_ = SigmoidLayer(t_x_s_reset4_)
rs4_ = EltwiseMultiplyLayer(reset_gate4_, prev_s4_)
t_x_rs4_ = CConv3DLayer(rs4_, curr_s4_, (n_deconvfilter[3], n_deconvfilter[3], 3, 3, 3),
params=t_x_rs4.params)
tanh_t_x_rs4_ = TanhLayer(t_x_rs4_)
gru_out4_ = AddLayer(
EltwiseMultiplyLayer(update4_, prev_s4_),
EltwiseMultiplyLayer(comp_udpate_gate4_, tanh_t_x_rs4_))
print("gru_out4: ", gru_out4_.output_shape)
return gru_out4_.output
def gru3(curr_s3, prev_s3):
curr_s3 = tensor.reshape(curr_s3, s_shape3)
curr_s3_ = InputLayer(s_shape3, curr_s3)
prev_s3_ = InputLayer(s_shape3, prev_s3)
t_x_s_update3_ = CConv3DLayer(prev_s3_, curr_s3_, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3),
params=t_x_s_update3.params)
t_x_s_reset3_ = CConv3DLayer(prev_s3_, curr_s3_, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3),
params=t_x_s_reset3.params)
update3_ = SigmoidLayer(t_x_s_update3_)
comp_udpate_gate3_ = ComplementLayer(update3_)
reset_gate3_ = SigmoidLayer(t_x_s_reset3_)
rs3_ = EltwiseMultiplyLayer(reset_gate3_, prev_s3_)
t_x_rs3_ = CConv3DLayer(rs3_, curr_s3_, (n_deconvfilter[2], n_deconvfilter[2], 3, 3, 3),
params=t_x_rs3.params)
tanh_t_x_rs3_ = TanhLayer(t_x_rs3_)
gru_out3_ = AddLayer(
EltwiseMultiplyLayer(update3_, prev_s3_),
EltwiseMultiplyLayer(comp_udpate_gate3_, tanh_t_x_rs3_))
print("gru_out3: ", gru_out3_.output_shape)
return gru_out3_.output
def gru2(curr_s2, prev_s2):
curr_s2 = tensor.reshape(curr_s2, s_shape2)
curr_s2_ = InputLayer(s_shape2, curr_s2)
prev_s2_ = InputLayer(s_shape2, prev_s2)
t_x_s_update2_ = CConv3DLayer(prev_s2_, curr_s2_, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3),
params=t_x_s_update2.params)
t_x_s_reset2_ = CConv3DLayer(prev_s2_, curr_s2_, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3),
params=t_x_s_reset2.params)
update2_ = SigmoidLayer(t_x_s_update2_)
comp_udpate_gate2_ = ComplementLayer(update2_)
reset_gate2_ = SigmoidLayer(t_x_s_reset2_)
rs2_ = EltwiseMultiplyLayer(reset_gate2_, prev_s2_)
t_x_rs2_ = CConv3DLayer(rs2_, curr_s2_, (n_deconvfilter[1], n_deconvfilter[1], 3, 3, 3),
params=t_x_rs2.params)
tanh_t_x_rs2_ = TanhLayer(t_x_rs2_)
gru_out2_ = AddLayer(
EltwiseMultiplyLayer(update2_, prev_s2_),
EltwiseMultiplyLayer(comp_udpate_gate2_, tanh_t_x_rs2_))
print("gru_out2: ", gru_out2_.output_shape)
return gru_out2_.output
s_encoder, _ = theano.scan(encoder,
sequences=[self.x])
# print("self.x: ", self.x)
out_encoder5 = s_encoder[3]
out_encoder4 = s_encoder[2]
out_encoder3 = s_encoder[1]
out_encoder2 = s_encoder[0]
s_gru5, _ = theano.scan(gru5,
sequences=[out_encoder5],
outputs_info=[tensor.zeros_like(np.zeros(s_shape5),
dtype=theano.config.floatX)])
input_5 = InputLayer(s_shape5, s_gru5[-1])
# print("input_5: ", input_5.output_shape)
pred5 = Conv3DLayer(input_5, (2, 3, 3, 3))
unpool5 = Unpool3DLayer(input_5)
conv3d5 = Conv3DLayer(unpool5, (n_deconvfilter[3], 3, 3, 3))
rect3d5 = LeakyReLU(conv3d5)
print("rect3d5: ", rect3d5.output_shape)
print("recct3d5: ", rect3d5.output)
s_gru4, _ = theano.scan(gru4,
sequences=[out_encoder4],
outputs_info=[rect3d5.output]
)
input_4 = InputLayer(s_shape4, s_gru4[-1])
pred4 = Conv3DLayer(input_4, (2, 3, 3, 3))
unpool4 = Unpool3DLayer(input_4)
conv3d4 = Conv3DLayer(unpool4, (n_deconvfilter[2], 3, 3, 3))
rect3d4 = LeakyReLU(conv3d4)
print("rect3d4: ", rect3d4.output_shape)
print("recct3d4: ", rect3d4.output)
s_gru3, _ = theano.scan(gru3,
sequences=[out_encoder3],
outputs_info=[rect3d4.output])
input_3 = InputLayer(s_shape3, s_gru3[-1])
pred3 = Conv3DLayer(input_3, (2, 3, 3, 3))
unpool3 = Unpool3DLayer(input_3)
conv3d3 = Conv3DLayer(unpool3, (n_deconvfilter[1], 3, 3, 3))
rect3d3 = LeakyReLU(conv3d3)
print("rect3d3: ", rect3d3.output_shape)
print("recct3d3: ", rect3d3.output)
s_gru2, _ = theano.scan(gru2,
sequences=[out_encoder2],
outputs_info=[rect3d3.output])
input_2 = InputLayer(s_shape2, s_gru2[-1])
pred2 = Conv3DLayer(input_2, (2, 3, 3, 3))
labele_shape = self.y.shape
label3 = self.y[:, 0:labele_shape[1]:2, :, 0:labele_shape[3]:2, 0:labele_shape[4]:2]
label4 = self.y[:, 0:labele_shape[1]:4, :, 0:labele_shape[3]:4, 0:labele_shape[4]:4]
label5 = self.y[:, 0:labele_shape[1]:8, :, 0:labele_shape[3]:8, 0:labele_shape[4]:8]
# print("pred5: ", pred5.output_shape)
# print("pred4: ", pred4.output_shape)
# print("pred3: ", pred3.output_shape)
print("pred2: ", pred2.output_shape)
softmax_loss5 = SoftmaxWithLoss3D(pred5.output)
softmax_loss4 = SoftmaxWithLoss3D(pred4.output)
softmax_loss3 = SoftmaxWithLoss3D(pred3.output)
softmax_loss2 = SoftmaxWithLoss3D(pred2.output)
# self.loss = softmax_loss2.loss(self.y)
self.loss = (softmax_loss5.loss(label5) + softmax_loss4.loss(label4) +
softmax_loss3.loss(label3) + softmax_loss2.loss(self.y)) / 4.
# self.loss = self.loss /
self.error = softmax_loss2.error(self.y)
self.output = softmax_loss2.prediction()
self.params = get_trainable_params()
self.activations = []