COMPMID-997: Add support for node's name in GraphAPI.

Change-Id: I0ca02e42807c1ad9afeffb7202a3556feb11442f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129701
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

6 files changed, 175 insertions, 106 deletions

diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 6a3c14b7bb..d654f9ece9 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -103,9 +103,10 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
                   PadStrideInfo(4, 4, 0, 0))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu1")
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               // Layer 2
               << convolution_5x5_hint
               << ConvolutionLayer(
@@ -113,9 +114,10 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
                   PadStrideInfo(1, 1, 2, 2), 2)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu2")
+              << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               << convolution_3x3_hint
               // Layer 3
               << ConvolutionLayer(
@@ -123,41 +125,47 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv3")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu3")
               // Layer 4
               << ConvolutionLayer(
                   3U, 3U, 384U,
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
                   PadStrideInfo(1, 1, 1, 1), 2)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv4")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu4")
               // Layer 5
               << ConvolutionLayer(
                   3U, 3U, 256U,
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
                   PadStrideInfo(1, 1, 1, 1), 2)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv5")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu5")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
               // Layer 6
               << FullyConnectedLayer(
                   4096U,
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("fc6")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu6")
               // Layer 7
               << FullyConnectedLayer(
                   4096U,
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("fc7")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu7")
               // Layer 8
               << FullyConnectedLayer(
                   1000U,
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
+              .set_name("fc8")
               // Softmax
-              << SoftmaxLayer()
+              << SoftmaxLayer().set_name("prob")
               << OutputLayer(get_output_accessor(label, 5));
 
         // Finalize graph
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 8aca0fa45b..895d9aad4e 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -84,23 +84,27 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               << ConvolutionLayer(
                   5U, 5U, 50U,
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               << FullyConnectedLayer(
                   500U,
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("ip1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu")
               << FullyConnectedLayer(
                   10U,
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
-              << SoftmaxLayer()
+              .set_name("ip2")
+              << SoftmaxLayer().set_name("prob")
               << OutputLayer(get_output_accessor(""));
 
         // Finalize graph
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index 6e2921a8a6..870e67daa5 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -132,13 +132,15 @@ public:
                   get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW),
                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                   PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
+              .set_name("Conv2d_0")
               << BatchNormalizationLayer(
                   get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"),
                   get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"),
                   get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"),
                   get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"),
                   0.001f)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
+              .set_name("Conv2d_0/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6");
         graph << get_dwsc_node(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
         graph << get_dwsc_node(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
         graph << get_dwsc_node(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
@@ -152,14 +154,15 @@ public:
         graph << get_dwsc_node(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
         graph << get_dwsc_node(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
         graph << get_dwsc_node(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a")
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
                   get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << ReshapeLayer(TensorShape(1001U))
-              << SoftmaxLayer()
+              .set_name("Logits/Conv2d_1c_1x1")
+              << ReshapeLayer(TensorShape(1001U)).set_name("Reshape")
+              << SoftmaxLayer().set_name("Softmax")
               << OutputLayer(get_output_accessor(label, 5));
 
         // Finalize graph
@@ -188,25 +191,29 @@ private:
                get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW),
                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                dwc_pad_stride_info)
+           .set_name(total_path + "depthwise/depthwise")
            << BatchNormalizationLayer(
                get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
                get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
                get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
                get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
                0.001f)
-           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+           .set_name(total_path + "depthwise/BatchNorm")
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6")
            << ConvolutionLayer(
                1U, 1U, conv_filt,
                get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW),
                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                conv_pad_stride_info)
+           .set_name(total_path + "pointwise/Conv2D")
            << BatchNormalizationLayer(
                get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
                get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
                get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
                get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
                0.001f)
-           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
+           .set_name(total_path + "pointwise/BatchNorm")
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6");
 
         return BranchLayer(std::move(sg));
     }
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index eb74a1aa11..6fc652edbe 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -97,6 +97,7 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"),
                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                   PadStrideInfo(2, 2, 3, 3))
+              .set_name("conv1/convolution")
               << convolution_hint
               << BatchNormalizationLayer(
                   get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"),
@@ -104,22 +105,24 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"),
                   get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"),
                   0.0000100099996416f)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)));
+              .set_name("conv1/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
 
         add_residual_block(data_path, "block1", 64, 3, 2);
         add_residual_block(data_path, "block2", 128, 4, 2);
         add_residual_block(data_path, "block3", 256, 6, 2);
         add_residual_block(data_path, "block4", 512, 3, 1);
 
-        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5")
               << ConvolutionLayer(
                   1U, 1U, 1000U,
                   get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"),
                   get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"),
                   PadStrideInfo(1, 1, 0, 0))
-              << FlattenLayer()
-              << SoftmaxLayer()
+              .set_name("logits/convolution")
+              << FlattenLayer().set_name("predictions/Reshape")
+              << SoftmaxLayer().set_name("predictions/Softmax")
               << OutputLayer(get_output_accessor(label, 5));
 
         // Finalize graph
@@ -142,9 +145,13 @@ private:
     {
         for(unsigned int i = 0; i < num_units; ++i)
         {
-            std::stringstream unit;
-            unit << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_";
-            std::string unit_name = unit.str();
+            std::stringstream unit_path_ss;
+            unit_path_ss << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_";
+            std::stringstream unit_name_ss;
+            unit_name_ss << name << "/unit" << (i + 1) << "/bottleneck_v1/";
+
+            std::string unit_path = unit_path_ss.str();
+            std::string unit_name = unit_name_ss.str();
 
             unsigned int middle_stride = 1;
 
@@ -156,73 +163,81 @@ private:
             SubStream right(graph);
             right << ConvolutionLayer(
                       1U, 1U, base_depth,
-                      get_weights_accessor(data_path, unit_name + "conv1_weights.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv1_weights.npy"),
                       std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                       PadStrideInfo(1, 1, 0, 0))
+                  .set_name(unit_name + "conv1/convolution")
                   << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_beta.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"),
                       0.0000100099996416f)
-                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+                  .set_name(unit_name + "conv1/BatchNorm")
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
 
                   << ConvolutionLayer(
                       3U, 3U, base_depth,
-                      get_weights_accessor(data_path, unit_name + "conv2_weights.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv2_weights.npy"),
                       std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                       PadStrideInfo(middle_stride, middle_stride, 1, 1))
+                  .set_name(unit_name + "conv2/convolution")
                   << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_beta.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"),
                       0.0000100099996416f)
-                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+                  .set_name(unit_name + "conv2/BatchNorm")
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
 
                   << ConvolutionLayer(
                       1U, 1U, base_depth * 4,
-                      get_weights_accessor(data_path, unit_name + "conv3_weights.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv3_weights.npy"),
                       std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                       PadStrideInfo(1, 1, 0, 0))
+                  .set_name(unit_name + "conv3/convolution")
                   << BatchNormalizationLayer(
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_mean.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_variance.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_gamma.npy"),
-                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_beta.npy"),
-                      0.0000100099996416f);
+                      get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_moving_mean.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_moving_variance.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_gamma.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_beta.npy"),
+                      0.0000100099996416f)
+                  .set_name(unit_name + "conv2/BatchNorm");
 
             if(i == 0)
             {
                 SubStream left(graph);
                 left << ConvolutionLayer(
                          1U, 1U, base_depth * 4,
-                         get_weights_accessor(data_path, unit_name + "shortcut_weights.npy"),
+                         get_weights_accessor(data_path, unit_path + "shortcut_weights.npy"),
                          std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
                          PadStrideInfo(1, 1, 0, 0))
+                     .set_name(unit_name + "shortcut/convolution")
                      << BatchNormalizationLayer(
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_mean.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_variance.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_gamma.npy"),
-                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_beta.npy"),
-                         0.0000100099996416f);
-
-                graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right));
+                         get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_moving_mean.npy"),
+                         get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_moving_variance.npy"),
+                         get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_gamma.npy"),
+                         get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_beta.npy"),
+                         0.0000100099996416f)
+                     .set_name(unit_name + "shortcut/BatchNorm");
+
+                graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add");
             }
             else if(middle_stride > 1)
             {
                 SubStream left(graph);
-                left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true));
+                left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
 
-                graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right));
+                graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add");
             }
             else
             {
                 SubStream left(graph);
-                graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right));
+                graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add");
             }
 
-            graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+            graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
         }
     }
 };
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index 9e8e69411f..9c2763f649 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -100,7 +100,8 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv1_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_1/Relu")
               << convolution3x3_hint
               // Layer 2
               << ConvolutionLayer(
@@ -108,108 +109,123 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv1_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               // Layer 3
               << ConvolutionLayer(
                   3U, 3U, 128U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv2_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_1/Relu")
               // Layer 4
               << ConvolutionLayer(
                   3U, 3U, 128U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv2_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               // Layer 5
               << ConvolutionLayer(
                   3U, 3U, 256U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv3_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_1/Relu")
               // Layer 6
               << ConvolutionLayer(
                   3U, 3U, 256U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv3_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_2/Relu")
               // Layer 7
               << ConvolutionLayer(
                   3U, 3U, 256U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv3_3")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
               // Layer 8
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv4_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_1/Relu")
               // Layer 9
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv4_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_2/Relu")
               // Layer 10
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv4_3")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
               // Layer 11
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv5_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_1/Relu")
               // Layer 12
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv5_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_2/Relu")
               // Layer 13
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv5_3")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
               // Layer 14
               << FullyConnectedLayer(
                   4096U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy"))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("fc6")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu")
               // Layer 15
               << FullyConnectedLayer(
                   4096U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy"))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("fc7")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1")
               // Layer 16
               << FullyConnectedLayer(
                   1000U,
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_b.npy"))
+              .set_name("fc8")
               // Softmax
-              << SoftmaxLayer()
+              << SoftmaxLayer().set_name("prob")
               << OutputLayer(get_output_accessor(label, 5));
 
         // Finalize graph
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index fed2c806ee..0684309111 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -100,126 +100,145 @@ public:
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv1_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_1/Relu")
               << convolution3x3_hint
               << ConvolutionLayer(
                   3U, 3U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv1_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv1_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               // Layer 2
               << ConvolutionLayer(
                   3U, 3U, 128U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv2_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 128U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv2_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv2_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               // Layer 3
               << ConvolutionLayer(
                   3U, 3U, 256U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv3_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 256U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv3_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_2/Relu")
               << ConvolutionLayer(
                   3U, 3U, 256U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv3_3")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_3/Relu")
               << ConvolutionLayer(
                   3U, 3U, 256U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv3_4_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv3_4")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_4/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
               // Layer 4
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv4_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv4_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_2/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv4_3")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_3/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv4_4_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv4_4")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_4/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
               // Layer 5
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_1_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv5_1")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_1/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_2_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv5_2")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_2/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_3_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("conv5_3")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_3/Relu")
               << ConvolutionLayer(
                   3U, 3U, 512U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/conv5_4_b.npy"),
                   PadStrideInfo(1, 1, 1, 1))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)))
+              .set_name("conv5_4")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_4/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
               // Layer 6
               << FullyConnectedLayer(
                   4096U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc6_b.npy"))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("fc6")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu")
               // Layer 7
               << FullyConnectedLayer(
                   4096U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc7_b.npy"))
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              .set_name("fc7")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1")
               // Layer 8
               << FullyConnectedLayer(
                   1000U,
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_w.npy"),
                   get_weights_accessor(data_path, "/cnn_data/vgg19_model/fc8_b.npy"))
+              .set_name("fc8")
               // Softmax
-              << SoftmaxLayer()
+              << SoftmaxLayer().set_name("prob")
               << OutputLayer(get_output_accessor(label, 5));
 
         // Finalize graph