diff --git a/src/main/java/de/lluni/javann/Network.java b/src/main/java/de/lluni/javann/Network.java
index f03555b..d62648d 100644
--- a/src/main/java/de/lluni/javann/Network.java
+++ b/src/main/java/de/lluni/javann/Network.java
@@ -79,8 +79,9 @@ public class Network {
      * @param y_train labels
      * @param epochs amount of training iterations
      * @param learningRate step size of gradient descent
+     * @param optimize if step size should decrease for each subsequent epoch
      */
-    public void fit(SimpleMatrix[] X_train, SimpleMatrix[] y_train, int epochs, double learningRate) {
+    public void fit(SimpleMatrix[] X_train, SimpleMatrix[] y_train, int epochs, double learningRate, boolean optimize) {
         int samples = X_train.length;
 
         for (int i = 0; i < epochs; i++) {
@@ -98,7 +99,11 @@ public class Network {
                 // backward propagation
                 SimpleMatrix error = lossPrime.apply(y_train[j], output);
                 for (int k = layers.size() - 1; k >= 0; k--) {
-                    error = layers.get(k).backwardPropagation(error, learningRate / (i+1));
+                    if (optimize) {
+                        error = layers.get(k).backwardPropagation(error, learningRate / (i+1));
+                    } else {
+                        error = layers.get(k).backwardPropagation(error, learningRate);
+                    }
                 }
             }
             // calculate average error on all samples
diff --git a/src/main/java/de/lluni/javann/examples/ExampleSine.java b/src/main/java/de/lluni/javann/examples/ExampleSine.java
index c0701d2..1572b0a 100644
--- a/src/main/java/de/lluni/javann/examples/ExampleSine.java
+++ b/src/main/java/de/lluni/javann/examples/ExampleSine.java
@@ -57,7 +57,7 @@ public class ExampleSine {
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
 
         // train network on X_train and y_train
-        network.fit(X_train, y_train, 100, 0.05d);
+        network.fit(X_train, y_train, 100, 0.05d, true);
 
         // predict X_test and output results to console
         SimpleMatrix[] output = network.predict(X_test);
diff --git a/src/main/java/de/lluni/javann/examples/ExampleXOR.java b/src/main/java/de/lluni/javann/examples/ExampleXOR.java
index f647673..7f4d56f 100644
--- a/src/main/java/de/lluni/javann/examples/ExampleXOR.java
+++ b/src/main/java/de/lluni/javann/examples/ExampleXOR.java
@@ -26,7 +26,7 @@ public class ExampleXOR {
         network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
 
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
-        network.fit(X_train, y_train, 1000, 0.1d);
+        network.fit(X_train, y_train, 1000, 0.1d, false);
 
         SimpleMatrix[] output = network.predict(X_train);
         for (SimpleMatrix entry : output) {
diff --git a/src/main/java/de/lluni/javann/examples/ExampleXORAddedNeurons.java b/src/main/java/de/lluni/javann/examples/ExampleXORAddedNeurons.java
index b19ab58..af8c31a 100644
--- a/src/main/java/de/lluni/javann/examples/ExampleXORAddedNeurons.java
+++ b/src/main/java/de/lluni/javann/examples/ExampleXORAddedNeurons.java
@@ -27,7 +27,7 @@ public class ExampleXORAddedNeurons {
         network.addNeuron(0, 2);
 
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
-        network.fit(X_train, y_train, 1000, 0.1d);
+        network.fit(X_train, y_train, 1000, 0.1d, false);
 
         SimpleMatrix[] output = network.predict(X_train);
         for (SimpleMatrix entry : output) {