면담보고서, model parameter adjusted

@@ -407,7 +407,7 @@ class NetVLADModelLF(models.BaseModel):
     random_frames = True
     cluster_size = 64
     hidden1_size = 1024
-    relu = False
+    relu = True
     dimred = -1
     gating = True
     remove_diag = False

@@ -75,7 +75,7 @@ if __name__ == "__main__":
   flags.DEFINE_integer(
       "num_gpu", 1, "The maximum number of GPU devices to use for training. "
       "Flag only applies if GPUs are installed")
-  flags.DEFINE_integer("batch_size", 256,
+  flags.DEFINE_integer("batch_size", 128,
                        "How many examples to process per batch for training.")
   flags.DEFINE_string("label_loss", "CrossEntropyLoss",
                       "Which loss function to use for training the model.")
@@ -83,24 +83,24 @@ if __name__ == "__main__":
       "regularization_penalty", 1.0,
       "How much weight to give to the regularization loss (the label loss has "
       "a weight of 1).")
-  flags.DEFINE_float("base_learning_rate", 0.01,
+  flags.DEFINE_float("base_learning_rate", 0.0006,
                      "Which learning rate to start with.")
   flags.DEFINE_float(
-      "learning_rate_decay", 0.95,
+      "learning_rate_decay", 0.8,
       "Learning rate decay factor to be applied every "
       "learning_rate_decay_examples.")
   flags.DEFINE_float(
-      "learning_rate_decay_examples", 4000000,
+      "learning_rate_decay_examples", 100,
       "Multiply current learning rate by learning_rate_decay "
       "every learning_rate_decay_examples.")
   flags.DEFINE_integer(
-      "num_epochs", 100, "How many passes to make over the dataset before "
+      "num_epochs", 5, "How many passes to make over the dataset before "
       "halting training.")
   flags.DEFINE_integer(
       "max_steps", None,
       "The maximum number of iterations of the training loop.")
   flags.DEFINE_integer(
-      "export_model_steps", 1,
+      "export_model_steps", 100,
       "The period, in number of steps, with which the model "
       "is exported for batch prediction.")