From 04cb333bc4b174760958a77c7ce076eae38fe8e4 Mon Sep 17 00:00:00 2001
From: Jatin Chowdhury <jatinchowdhury18@gmail.com>
Date: Thu, 30 Nov 2023 18:00:15 -0800
Subject: [PATCH] More tweaks in benchmark code

---
 bench/layer_creator.hpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/bench/layer_creator.hpp b/bench/layer_creator.hpp
index 610e30f..edc8dde 100644
--- a/bench/layer_creator.hpp
+++ b/bench/layer_creator.hpp
@@ -135,28 +135,28 @@ std::unique_ptr<RTNeural::Layer<Float>>
 create_layer(const std::string &layer_type, size_t in_size, size_t out_size) {
   if (layer_type == "dense") {
     auto layer = std::make_unique<RTNeural::Dense<Float>>(in_size, out_size);
-    randomise_dense(*layer);
+    randomise_dense<Float>(*layer);
     return std::move(layer);
   }
 
   if (layer_type == "conv1d") {
     const auto kernel_size = in_size - 1;
     auto layer = std::make_unique<RTNeural::Conv1D<Float>>(in_size, out_size, kernel_size, 1);
-    randomise_conv1d(*layer, kernel_size);
+    randomise_conv1d<Float>(*layer, kernel_size);
     return std::move(layer);
   }
 
   if (layer_type == "gru") {
     auto layer =
         std::make_unique<RTNeural::GRULayer<Float>>(in_size, out_size);
-    randomise_gru(*layer);
+    randomise_gru<Float>(*layer);
     return std::move(layer);
   }
 
   if (layer_type == "lstm") {
     auto layer =
         std::make_unique<RTNeural::LSTMLayer<Float>>(in_size, out_size);
-    randomise_lstm(*layer);
+    randomise_lstm<Float>(*layer);
     return std::move(layer);
   }
 
@@ -166,7 +166,7 @@ create_layer(const std::string &layer_type, size_t in_size, size_t out_size) {
   }
 
   if (layer_type == "relu") {
-    auto layer = std::make_unique<RTNeural::ReLuActivation<double>>(in_size);
+    auto layer = std::make_unique<RTNeural::ReLuActivation<Float>>(in_size);
     return std::move(layer);
   }