[LOG] print output dim instead of input dim in model summary

When we print the model architecture using summarize method,
nntrainer prints input dimension of each layer.

But, tensorflow and pytorch are printing output dimmension
of each layer in the summary, thus it is inconvenient
to compare each layer with tf and torch models.

Thus, I suggest to print output dimension of each layer
instead of input dimension in the model summary.

**Self evaluation:**
1. Build test:	 [X]Passed [ ]Failed [ ]Skipped
2. Run test:	 [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Seungbaek Hong <sb92.hong@samsung.com>

diff --git a/nntrainer/models/neuralnet.cpp b/nntrainer/models/neuralnet.cpp
index 0db418f..d9b62e8 100644 (file)
--- a/nntrainer/models/neuralnet.cpp
+++ b/nntrainer/models/neuralnet.cpp
@@ -1214,7 +1214,7 @@ void NeuralNetwork::print(std::ostream &out, unsigned int flags,
 
     out << std::string(total_col_size, '=') << '\n';
     print_graph_layer_info(
-      out, {"Layer name", "Layer type", "Input dimension", "Input layer"});
+      out, {"Layer name", "Layer type", "Output dimension", "Input layer"});
     out << std::string(total_col_size, '=') << '\n';
     if (compiled) {
       props::GenericShape dim_property;
@@ -1222,10 +1222,10 @@ void NeuralNetwork::print(std::ostream &out, unsigned int flags,
       for (auto iter = model_graph.cbegin(); iter != model_graph.cend();
            iter++) {
         std::string first_dim;
-        if (iter->getInputDimensions().empty()) {
+        if (iter->getOutputDimensions().empty()) {
           first_dim = "";
         } else {
-          dim_property.set(iter->getInputDimensions()[0]);
+          dim_property.set(iter->getOutputDimensions()[0]);
           first_dim = to_string(dim_property);
         }
         const std::vector<std::string> &input_layer_names =