full trainer test

test/test_trainer.py

@@ -1,5 +1,48 @@
 from micrograd import Activation, Value
 
+from micrograd import Value, MLP, Optimizer , Trainer
+
+# Dataset
+inputs = [
+    [Value(1.0), Value(2.0)],
+    [Value(2.0), Value(3.0)],
+    [Value(3.0), Value(4.0)],
+    [Value(4.0), Value(5.0)]
+]
+targets = [
+    Value(9.0),
+    Value(14.0),
+    Value(19.0),
+    Value(24.0)
+]
+
+
+# Loss function
+def mean_squared_error(predicted: Value, target: Value) -> Value:
+    return (predicted - target) ** 2
+
+
+def test_complete_train(capsys):
+    with capsys.disabled():
+
+        # Model
+        model = MLP(input_size=2, layer_sizes=[3, 1])
+
+        # Optimizer
+        optimizer = Optimizer()
+
+        # Trainer
+        trainer = Trainer(model=model, loss_fn=mean_squared_error, optimizer=optimizer)
+
+        # Train
+        trainer.train(inputs, targets, epochs=100, learning_rate=0.01)
+
+        # Test
+        test_input = [Value(5.0), Value(6.0)]  # Expected output: 31
+        prediction = model(test_input)
+        print(f"Prediction for input {test_input}: {prediction.data:.4f}")
+
+
 
 def test_relu():
     assert Activation.relu(Value(3.0)).data == Value(3.0).data