def build_net3(X1,X2,X3, num_classes, keep_prob_fc, is_train, arch_model):
arch = net_arch()
if arch_model == "arch_multi_alexnet_v2":
net, net_vis = arch.arch_multi_alexnet_v2(X1,X2,X3, num_classes, keep_prob_fc, is_train)
elif arch_model == "arch_multi_vgg16":
net, net_vis = arch.arch_multi_vgg16(X1,X2,X3, num_classes, keep_prob_fc, is_train)
elif arch_model == "arch_multi_vgg16_conv":
net, net_vis = arch.arch_multi_vgg16_conv(X1,X2,X3, num_classes, keep_prob_fc, is_train)
else:
print ('{} is error!', arch_model)
return net, net_vis
def build_net(X, num_classes, keep_prob_fc, is_train, arch_model):
arch = net_arch()
if arch_model == "arch_inception_v4":
net, net_vis = arch.arch_inception_v4(X, num_classes, keep_prob_fc, is_train)
elif arch_model == "arch_inception_v4_rnn":
net, net_vis = arch.arch_inception_v4_rnn(X, num_classes, keep_prob_fc, is_train)
elif arch_model == "arch_inception_v4_rnn_attention":
net, net_vis = arch.arch_inception_v4_rnn_attention(X, num_classes, keep_prob_fc, is_train)
elif arch_model == "arch_alexnet_v2":
net, net_vis = arch.arch_alexnet_v2(X, num_classes, keep_prob_fc, is_train)
else:
print ('{} is error!', arch_model)
return net, net_vis