From bc66016be3e21a17d42de2921f4765626c5809bf Mon Sep 17 00:00:00 2001
From: Okerew <93822247+Okerew@users.noreply.github.com>
Date: Fri, 30 Aug 2024 21:25:40 +0200
Subject: [PATCH] Update test.py

---
 tests/test.py | 35 ++++++++++++++++++++++++++++++++---
 1 file changed, 32 insertions(+), 3 deletions(-)

diff --git a/tests/test.py b/tests/test.py
index 63afe70..28f725a 100644
--- a/tests/test.py
+++ b/tests/test.py
@@ -1,5 +1,5 @@
 from okrolearn.okrolearn import *
-from okrolearn.optimizers import SGDOptimizer
+from okrolearn.optimizers import SGDOptimizer, RMSPropOptimizer
 
 def print_epoch_start(epoch, total_epochs):
     print(f"Starting epoch {epoch + 1}/{total_epochs}")
@@ -28,10 +28,39 @@ def print_epoch_start(epoch, total_epochs):
 network.add(GELUActivationLayer())
 inputs = Tensor(np.random.rand(100, 3))
 targets = Tensor(np.random.randint(0, 3, size=(100,)))
+hyperparameters = [
+    {
+        'lr_scheduler': LearningRateScheduler(initial_lr=0.01),
+        'optimizer': SGDOptimizer(lr=0.01),
+        'temperature': 1.0
+    },
+    {
+        'lr_scheduler': StepLRScheduler(initial_lr=0.001, step_size=30, gamma=0.1),
+        'optimizer': AdamOptimizer(lr=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8),
+        'temperature': 1.0
+    },
+    {
+        'lr_scheduler': ExponentialDecayScheduler(initial_lr=0.001, decay_rate=0.1),
+        'optimizer': NadamOptimizer(epsilon=1e-8),
+        'temperature': 0.5
+    },
+    {
+        'lr_scheduler': LinearDecayScheduler(initial_lr=0.001, final_lr=0.0001, total_epochs=100),
+        'optimizer': AdadeltaOptimizer(epsilon=1e-8),
+        'temperature': 1.5
+    },
+    {
+        'lr_scheduler': TimeBasedDecayScheduler(initial_lr=0.001, decay_rate=0.01),
+        'optimizer': RMSPropOptimizer(lr=0.001, epsilon=1e-8),
+        'temperature': 1.0
+    }
+]
+
+network.hyperparameter_tuning(inputs, targets, batch_size=10, epochs=30, loss_function=CrossEntropyLoss(), hyperparameters=hyperparameters)
 loss_function = CrossEntropyLoss()
 optimizer = SGDOptimizer(lr=0.01)
 
-losses = network.train(inputs, targets, epochs=50, lr=0.01, optimizer=optimizer, batch_size=10, loss_function=loss_function, )
+losses = network.train(inputs, targets, epochs=50, lr_scheduler=LearningRateScheduler(0.01), optimizer=optimizer, batch_size=10, loss_function=loss_function, )
 network.plot_parameter_changes()
 network.analyze_gradients()
 network.print_profile_stats()
@@ -61,7 +90,7 @@ def print_epoch_start(epoch, total_epochs):
 test_network.add(GELUActivationLayer())
 
 test_network.load_weights('model.pt')
-test_network.train(inputs, targets, epochs=50, lr=0.01, optimizer=optimizer, batch_size=10, loss_function=loss_function)
+test_network.train(inputs, targets, epochs=50, lr_scheduler=LearningRateScheduler(0.01), optimizer=optimizer, batch_size=10, loss_function=loss_function)
 test_network.save("model2.pt")
 
 test_inputs = Tensor(np.random.rand(10, 3))