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