diff --git a/sobek/__init.py__ b/sobek/__init.py__
new file mode 100644
index 0000000..e69de29
diff --git a/neuralnetworkbuilder/test.py b/sobek/network.py
similarity index 57%
rename from neuralnetworkbuilder/test.py
rename to sobek/network.py
index ab86747..75d906a 100755
--- a/neuralnetworkbuilder/test.py
+++ b/sobek/network.py
@@ -3,6 +3,9 @@ import numpy as np
 class network:
 
     def __init__(self, inputLayerSize, *layerSizes):
+        if type(inputLayerSize) != int:
+            raise TypeError("The input layer size must be an int!")
+
         self.weights = []
         self.inputLayerSize = inputLayerSize
         self.oldLayerSize = inputLayerSize
@@ -10,40 +13,26 @@ class network:
             self.weights.append( np.random.default_rng(42).random((self.oldLayerSize, layerSize)) )
             self.oldLayerSize = layerSize
         self.biases = [[0]*layerSize for layerSize in layerSizes]
-        self.weights = np.array(self.weights)
-        self.biases = np.array(self.biases)
+        self.weights = np.array(self.weights, dtype=object)
+        self.biases = np.array(self.biases, dtype=object)
 
     def reLu(value):
         return max(0, value)
 
     def process(self, input):
         if type(input) != np.ndarray:
-            print("non")
+            raise TypeError("The input must be a vector!")
         if input.size != self.inputLayerSize:
-            print("vite")
+            raise ValueError("The input vector has the wrong size!")
         if input.dtype != np.float64:
-            print("aaa")
-        for layer, bias in zip(self.weights, self.biases):
-            print("---------------------")
-            print(input)
-            print(layer)
-            print(bias)
-            input = np.matmul(input, layer)
+            raise TypeError("The input vector must contain floats!")
+        
+        for layerWeights, bias in zip(self.weights, self.biases):
+            input = np.matmul(input, layerWeights)
             input = np.add(input, bias)
+            #reLu application
             with np.nditer(input, op_flags=['readwrite']) as layer:
                 for neuron in layer:
                     neuron = network.reLu(neuron)
-        return input
-
-
-test = network(16, 16, 8, 4)
-
-for y in test.weights:
-    print(y, end="\n\n")
-
-for y in test.biases:
-    print(y, end="\n\n")
-
-print(network.reLu(8))
-
-print(test.process(np.random.default_rng(42).random((16))))
\ No newline at end of file
+        
+        return input
\ No newline at end of file
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..96d04bd
--- /dev/null
+++ b/test.py
@@ -0,0 +1,14 @@
+import numpy as np
+from sobek.network import network
+
+test = network(16, 16, 8, 4)
+"""
+for y in test.weights:
+    print(y, end="\n\n")
+
+for y in test.biases:
+    print(y, end="\n\n")"""
+
+print(network.reLu(8))
+
+print(test.process(np.random.default_rng(42).random((16))))
\ No newline at end of file