Initial Neural network with forward feeding

[handwriting-recognition.git] / README.md

diff --git a/README.md b/README.md
index fa1c48496f16f54aaba8b1d40f637dc5db10e0b4..96974c990119387ee722e8f5d809536d0885233c 100644 (file)
--- a/README.md
+++ b/README.md
@@ -5,6 +5,7 @@ Basics:
  - [MNIST database of handwritten digits](http://yann.lecun.com/exdb/mnist/)
  - [Neuron](https://en.wikipedia.org/wiki/Artificial_neuron)
  - [Perceptron](https://en.wikipedia.org/wiki/Perceptron)
+ - [Backpropagation](https://en.wikipedia.org/wiki/Backpropagation)
  - [3Blue1Brown video series](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
 
 Too high-level for first-time learning, but apparently very abstract and powerful for real-life:
@@ -37,3 +38,14 @@ plt.close()
  - Read the MNIST database into numpy arrays with `./read_display_mnist.py`. Plot the first ten images and show their labels, to make sure the data makes sense:
 
    ![visualize training data](screenshots/mnist-visualize-training-data.png)
+
+ - 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%
+```