diff --git a/src/network/train.h b/src/network/train.h
index f90c811..bacd5fc 100644
--- a/src/network/train.h
+++ b/src/network/train.h
@@ -33,7 +33,8 @@ namespace nn {
     class Trainer {
     public:
 
-        Trainer(const std::string &training_data, const std::optional<std::string> &network_path, float learning_rate, int epochs, int batch_size, int thread_count) : adam(learning_rate), parser(training_data), batch_size(batch_size), thread_count(thread_count) {
+        Trainer(const std::string &training_data, const std::string &validation_data, const std::optional<std::string> &network_path, float learning_rate, int epochs, int batch_size, int thread_count) :
+        adam(learning_rate), training_parser(training_data), validation_parser(validation_data), batch_size(batch_size), thread_count(thread_count) {
 
             if (!std::filesystem::exists("networks")) {
                 std::filesystem::create_directory("networks");
@@ -53,7 +54,7 @@ namespace nn {
             entries_next = new TrainingEntry[batch_size];
 
             bool _;
-            parser.read_batch(batch_size, entries_next, _);
+            training_parser.read_batch(batch_size, entries_next, _);
 
             int64_t iter = 0;
             for (int epoch = 1; epoch <= epochs; epoch++) {
@@ -76,11 +77,11 @@ namespace nn {
                     errors.assign(thread_count, 0.0f);
                     accuracy.assign(thread_count, 0);
 
-                    std::thread th_loading = std::thread(&DataParser::read_batch, &parser, batch_size, entries_next, std::ref(is_new_epoch));
+                    std::thread th_loading = std::thread(&DataParser::read_batch, &training_parser, batch_size, entries_next, std::ref(is_new_epoch));
 
                     std::vector<std::thread> ths;
                     for (int id = 0; id < thread_count; id++) {
-                        ths.emplace_back(&Trainer::process_batch, this, id);
+                        ths.emplace_back(&Trainer::process_batch<true>, this, id);
                     }
 
                     for (std::thread &th : ths) {
@@ -99,6 +100,7 @@ namespace nn {
                     if (iter % 20 == 0) {
                         float average_error = checkpoint_error / float(batch_size * checkpoint_iter);
                         float average_accuracy = float(checkpoint_accuracy) / float(batch_size * checkpoint_iter);
+                        auto [val_loss, val_acc] = test_validation();
 
                         int64_t current_time = now();
                         int64_t elapsed_time = current_time - start_time;
@@ -124,7 +126,7 @@ namespace nn {
                         std::cout << "] - Epoch " << epoch << " - Iteration " << iter << " - Error " << average_error << " - ETA " << (eta / 1000) << "s - " << pos_per_s << " pos/s \r" << std::flush;
 
 
-                        log_file << iter << " " << average_error << " " << pos_per_s << " " << average_accuracy << "\n";
+                        log_file << iter << " " << average_error << " " << pos_per_s << " " << average_accuracy << " " << val_loss << " " << val_acc << "\n";
                         log_file.flush();
                         checkpoint_error = 0.0f;
                         checkpoint_accuracy = 0;
@@ -142,7 +144,8 @@ namespace nn {
     private:
         Network network;
         Adam adam;
-        DataParser parser;
+        DataParser training_parser;
+        DataParser validation_parser;
         unsigned int entry_count;
         int batch_size, thread_count;
         std::vector<Gradient> gradients;
@@ -150,6 +153,39 @@ namespace nn {
         std::vector<int> accuracy;
         TrainingEntry *entries, *entries_next;
 
+        std::pair<float, float> test_validation() {
+            bool _;
+            delete[] entries;
+            entries = new TrainingEntry[batch_size];
+            validation_parser.read_batch(batch_size, entries, _);
+
+            errors.assign(thread_count, 0.0f);
+            accuracy.assign(thread_count, 0);
+
+            std::vector<std::thread> ths;
+            for (int id = 0; id < thread_count; id++) {
+                ths.emplace_back(&Trainer::process_batch<false>, this, id);
+            }
+
+            for (std::thread &th : ths) {
+                if (th.joinable()) th.join();
+            }
+
+            float val_loss = 0.0f;
+            int correct = 0.0f;
+
+            for (int id = 0; id < thread_count; id++) {
+                val_loss += errors[id];
+                correct += accuracy[id];
+            }
+
+            val_loss /= batch_size;
+
+            float val_acc = float(correct) / float(batch_size);
+            return {val_loss, val_acc};
+        }
+
+        template<bool train>
         void process_batch(int id) {
             Gradient &g = gradients[id];
 
@@ -168,10 +204,12 @@ namespace nn {
                 errors[id] += error;
                 accuracy[id] += ((entry.wdl - 0.5f) * (prediction - 0.5f) > 0.0f) || std::abs(entry.wdl - prediction) < 0.05f;
 
-                std::array<float, 1> l1_loss = {(1 - EVAL_INFLUENCE) * 2.0f * (prediction - entry.wdl) + EVAL_INFLUENCE * 2.0f * (prediction - entry.eval)};
+                if constexpr (train) {
+                    std::array<float, 1> l1_loss = {(1 - EVAL_INFLUENCE) * 2.0f * (prediction - entry.wdl) + EVAL_INFLUENCE * 2.0f * (prediction - entry.eval)};
 
-                network.l1.backward(l1_loss, l0_output, l1_output, l0_loss, g.l1);
-                network.l0.backward(l0_loss, entry.features, l0_output, g.l0);
+                    network.l1.backward(l1_loss, l0_output, l1_output, l0_loss, g.l1);
+                    network.l0.backward(l0_loss, entry.features, l0_output, g.l0);
+                }
             }
         }
 
diff --git a/src/uci/uci.h b/src/uci/uci.h
index b7e9b18..4587a1b 100644
--- a/src/uci/uci.h
+++ b/src/uci/uci.h
@@ -22,6 +22,7 @@
 #include "../selfplay/selfplay.h"
 #include "../network/train.h"
 #include "../tests/perft.h"
+#include "../utils/split.h"
 #include "../utils/logger.h"
 #include "../utils/utilities.h"
 #include "command.h"
@@ -99,17 +100,25 @@ namespace uci {
             std::optional<int> dropout = find_element<int>(tokens, "dropout");
             selfplay::start_generation(limits, book.value_or("book.epd"), output.value_or("data.plain"), thread_count.value_or(1), dropout.value_or(1));
         });
+        commands.emplace_back("split", [&](context tokens){
+            std::optional<std::string> input_data = find_element<std::string>(tokens, "input");
+            std::optional<std::string> output_data1 = find_element<std::string>(tokens, "output1");
+            std::optional<std::string> output_data2 = find_element<std::string>(tokens, "output2");
+            std::optional<int> rate = find_element<int>(tokens, "rate");
+            split_data(input_data.value_or("data.plain"), output_data1.value_or("train.plain"), output_data2.value_or("validation.plain"), rate.value_or(10));
+        });
         commands.emplace_back("train", [&](context tokens){
             std::optional<std::string> network_path = find_element<std::string>(tokens, "network");
-            std::optional<std::string> training_data = find_element<std::string>(tokens, "in");
+            std::optional<std::string> training_data = find_element<std::string>(tokens, "training_data");
+            std::optional<std::string> validation_data = find_element<std::string>(tokens, "validation_data");
             std::optional<float> learning_rate = find_element<float>(tokens, "lr");
             std::optional<int> epochs = find_element<int>(tokens, "epochs");
             std::optional<int> batch_size = find_element<int>(tokens, "batch");
             std::optional<int> threads = find_element<int>(tokens, "threads");
-            nn::Trainer trainer(training_data.value_or("data.plain"), network_path, learning_rate.value_or(0.001f),
+            nn::Trainer trainer(training_data.value_or("train.plain"), validation_data.value_or("validation.plain"), network_path, learning_rate.value_or(0.001f),
                                 epochs.value_or(10), batch_size.value_or(16384), threads.value_or(4));
         });
-        commands.emplace_back("learn", [&](context tokens){
+        /*commands.emplace_back("learn", [&](context tokens){
             std::optional<int> thread_count = find_element<int>(tokens, "threads");
             std::optional<int> iterations = find_element<int>(tokens, "iter");
             std::optional<int> nodes = find_element<int>(tokens, "nodes");
@@ -130,7 +139,7 @@ namespace uci {
                 std::this_thread::sleep_for(std::chrono::seconds(1));
                 nn::net = nn::QNetwork("corenet.bin");
             }
-        });
+        });*/
         commands.emplace_back("perft", [&](context tokens) {
             int depth = find_element<int>(tokens, "perft").value_or(5);
             U64 node_count = test::perft<true, false>(board, depth);
diff --git a/src/utils/split.h b/src/utils/split.h
new file mode 100644
index 0000000..98e6055
--- /dev/null
+++ b/src/utils/split.h
@@ -0,0 +1,40 @@
+// WhiteCore is a C++ chess engine
+// Copyright (c) 2023 Balázs Szilágyi
+//
+// This program is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with this program.  If not, see <https://www.gnu.org/licenses/>.
+//
+
+#pragma once
+
+#include <random>
+
+inline void split_data(const std::string &input, const std::string &output1, const std::string &output2, int rate) {
+    std::ifstream in(input, std::ios::in);
+    std::ofstream out1(output1, std::ios::out | std::ios::app);
+    std::ofstream out2(output2, std::ios::out | std::ios::app);
+
+    std::random_device rd;
+    std::mt19937 g(rd());
+    std::uniform_int_distribution<int> dist(0, rate);
+
+    std::string line;
+    while (std::getline(in, line)) {
+        if (dist(g) == 0) out2 << line << "\n";
+        else out1 << line << "\n";
+    }
+
+    in.close();
+    out1.close();
+    out2.close();
+}
\ No newline at end of file
diff --git a/train/trainer.py b/train/trainer.py
index 14c2951..0d27bc8 100644
--- a/train/trainer.py
+++ b/train/trainer.py
@@ -27,8 +27,7 @@
     config={
         "learning_rate": 0.001,
         "architecture": 3,
-        "dataset": "data.plain",
-        "epochs": 10,
+        "epochs": 30,
         "batch_size": 16384,
         "thread_count": 4
     }
@@ -38,7 +37,7 @@
 current_time = datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
 wandb.run.name = f"exp-{commit_hash}-{current_time}"
 
-command_string = f"train in {wandb.run.config['dataset']} lr {wandb.run.config['learning_rate']} " \
+command_string = f"train lr {wandb.run.config['learning_rate']} " \
                  f"epochs {wandb.run.config['epochs']} batch {wandb.run.config['batch_size']} " \
                  f"threads {wandb.run.config['thread_count']}\n"
 
@@ -64,6 +63,8 @@
                 wandb.run.summary["iterations"] = iteration
                 wandb.log({"training loss": float(data[1]),
                            "training accuracy": float(data[3]),
+                           "validation loss": float(data[4]),
+                           "validation accuracy": float(data[5]),
                            "positions per second": int(data[2])})
         f.close()
     except Exception as e: