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diff --git a/docs/activation_functions.html b/docs/activation_functions.html
diff --git a/docs/cost_functions.html b/docs/cost_functions.html
diff --git a/docs/index.html b/docs/index.html
@@ -0,0 +1,38 @@
+<!DOCTYPE html>
+<html lang="fr">
+
+<head>
+    <meta charset="utf-8">
+    <title>Neural Networks V Module Docs</title>
+    <link href="style.css" rel="stylesheet">
+    <link href="webfonts/JetBrainsMono-Medium.woff2" type="font/woff2">
+    <link href="webfonts/JetBrainsMono-Regular.woff2" type="font/woff2">
+    <link href="webfonts/JetBrainsMono-Light.woff2" type="font/woff2">
+</head>
+
+<body>
+<div class="sidenavbar">
+    <a href="index.html">Home Page</a>
+    <a href="network.html">XOR Neural Network</a>
+</div>
+
+<div class="sidelist">
+    <a href="#HomePage">Home Page</a>
+</div>
+
+<div class="main">
+    <h1>Neural Networks V Module Docs</h1>
+    <div class="part">
+    <h2 id="HomePage">Home Page</h2>
+    <p>
+        This is the documentation for the <a href="https://github.com/Eliyaan/NeuralNetworks-V-Module" target="_blank">module</a>.
+    </p>
+    <p>
+        You can learn how to create a neural networks here: <a href="network.html">> Creating a network</a>.
+    </p>
+    </div>
+</div>
+
+</body>
+
+</html>
diff --git a/docs/layer_activation.html b/docs/layer_activation.html
diff --git a/docs/layer_dense.html b/docs/layer_dense.html
diff --git a/docs/network.html b/docs/network.html
@@ -0,0 +1,120 @@
+<!DOCTYPE html>
+<html lang="fr">
+
+<head>
+    <meta charset="utf-8">
+    <title>Creating a Network</title>
+    <link href="style.css" rel="stylesheet">
+</head>
+
+<body>
+<div class="sidenavbar">
+    <a href="index.html">Home Page</a>
+    <a href="network.html">XOR Neural Network</a>
+</div>
+
+<div class="sidelist">
+    <a href="#Requirements">Requirements</a>
+    <a href="#StructureOfTheCode">Structure of the code</a>
+</div>
+
+<div class="main">
+    <h1>> Creating a Neural Network</h1>
+    <div class="part">
+    <h2 id="Requirements">Requirements</h2>
+    <p>
+        You need to have V installed, you can verify that by running <i>v run .</i>.
+        You also need to have the vsl module installed, you can install it with <i>v install vsl</i>.
+        Then you need to clone the <a href="https://github.com/Eliyaan/NeuralNetworks-V-Module">module's repo</a> and create a v file in it that we are going to use.
+    </p>
+    </div>
+    <div class="part">
+    <h2 id="StructureOfTheCode">Structure of the code</h2>
+    <p>
+        We are going to create a neural network to train a neural network that will be able to perform the XOR logic gate. The result can be found <a href="https://github.com/Eliyaan/NeuralNetworks-V-Module/blob/main/examples/train_xor.v" target="_blank">here</a>.
+    </p>
+    <p>
+        First we are going to import the neural network module and create a main function.
+        Then we create the neural network that we are going to train.
+        The 0 is the seed for the random weights and biases to be able to get the same neural network at each run.
+    </p>
+    <p class="snippet">
+import neural_networks as nn
+
+fn main() {
+    mut model := nn.NeuralNetwork.new(0)
+}
+    </p>
+    <p>
+        Then we add the layers that we want our network to have.
+        We need our network to have 2 inputs and 1 output to match the XOR gate.
+        So we will first add a Dense layer with 2 inputs and 3 outputs, 3 is arbitrary but works well.
+        The two numbers after the number of inputs/outputs is the range for the initialisation of random weights and biases.
+        Then an Activation layer, the Dense and Activation layers are complementary so we will add one per Dense layer.
+        The activation function that we will use for this layer is leaky relu, as it is convenient.
+        We add a second Dense layer with 3 input and 1 output and the Activation layer that goes with it.
+    </p>
+    <p class="snippet">
+model.add_layer(nn.Dense.new(2, 3, 0.7, 0.65))
+model.add_layer(nn.Activation.new(.leaky_relu))
+model.add_layer(nn.Dense.new(3, 1, 0.6, 0.65))
+model.add_layer(nn.Activation.new(.leaky_relu))
+    </p>
+    <p>
+        Then we need to create the parametters for the training.
+        The learning rate, momentum, number of epochs are found by trial and error and these work well.
+        The cost function that we will use is the Mean Squared Error (MSE).
+        We then add the dataset that the network will use for it's training.
+        And same for the testing, in a real example the test data is unseen during the training to be able to see how well the networks does in an unseen situation
+        but as we have only 4 different possible inputs we can not show unseen data to the network so we will use the same data.
+        The neural newtork will print it's performance every <i>print_interval</i> epochs.
+        For the test parameters, every <i>training_interval</i> epochs it will run the test dataset and print the results from the <i>print_start</i>th element of the test dataset to the <i>print_end</i>th one.
+    </p>
+    <p class="snippet">
+training_parameters := nn.BackpropTrainingParams{
+    learning_rate: 0.37
+    momentum: 0.9
+    nb_epochs: 300
+    print_interval: 25
+    cost_function: .mse // mean squared error
+    training: nn.Dataset {
+        inputs: [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
+        expected_outputs: [[0.0], [1.0], [1.0], [0.0]]
+    }
+    test: nn.Dataset {
+        inputs: [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
+        expected_outputs: [[0.0], [1.0], [1.0], [0.0]]
+    }
+    test_params: nn.TestParams{
+        print_start: 0
+        print_end: 3
+        training_interval: 100
+    }
+}
+    </p>
+    <p>
+        Now it's the time to train the network!
+    </p>
+    <p class="snippet">
+model.train(training_parameters)
+    </p>
+    <p>
+        We can also save the model by adding that to the end of the program:
+    </p>
+    <p class="snippet">
+model.save_model('saveXOR')
+    </p>
+    <p>
+        And to load a model (to use it or to train it further) you just need to create an empty model like we did at the start and then do:
+    </p>
+    <p class="snippet">
+model.load_model('saveXOR')
+    </p>
+    <p>
+        There it is, we can just run the program and it will train!
+    </p>
+    </div>
+</div>
+</body>
+
+</html> 
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diff --git a/docs/training_backprop.html b/docs/training_backprop.html
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diff --git a/examples/train_xor.v b/examples/train_xor.v
@@ -27,7 +27,7 @@ fn main() {
 
 	training_parameters := nn.BackpropTrainingParams{
 		learning_rate: 0.37
-		momentum: 0.5
+		momentum: 0.9
 		nb_epochs: 300
 		print_interval: 25
 		cost_function: .mse // mean squared error

diff --git a/neural_networks/cost_functions.v b/neural_networks/cost_functions.v
@@ -18,7 +18,7 @@ pub fn mse(y_true []f64, y_pred []f64) f64 { // mean squared error
 	for elem in not_squared_error {
 		mean += elem * elem
 	}
-	return mean / f64(not_squared_error.len)
+	return mean
 }
 
 pub fn mse_prime(y_true []f64, y_pred []f64) []f64 {