+
+ - Define the structure of the neural network: two hidden layers with parametrizable sizes. Initialize weights and biases randomly. This gives totally random classifications of course, but at least makes sure that the data structures and computations work:
+
+```
+$ ./train.py
+output vector of first image: [ 0. 52766.88424917 0. 0.
+ 14840.28619491 14164.62850135 0. 7011.882333
+ 0. 46979.62976127]
+classification of first image: 1 with confidence 52766.88424917019; real label 5
+correctly recognized images after initialization: 10.076666666666668%
+```