Created ReadMe

Author: akutzer

README.md

@@ -1,2 +1,81 @@
-# numpy_cnn
-Small NeuralNet-Framework implemented with numpy (Convolution|TransposeConv|Linear)
+# NumPy CNN
+
+This is a small NeuralNet-Framework implemented only with NumPy.
+It contains Linear-, Convolution-, TransposedConv- and Pooling-Layers.
+The Framework is not really intended to be used, because you can't save the model (yet) and it's poorly optimized,
+but more for curious GitHub-user, who want to learn more about the popular Layers/Activations/etc. in NeuralNets.
+ 
+ 
+ 
+### Requirements
+
+For the core CNN-Frame you only need NumPy.
+```
+pip install numpy
+```
+If you want to run the FashionNet example, you need Matplotlib (for live-plotting), tqdm (loading-bar)
+and gzip (read compressed trainings data).
+```
+pip install matplotlib
+pip install tqdm
+(gzip is part of the python standard library)
+```
+For the test_CNN script you will also need PyTorch, because I confirmed my results with the PyTorch-Autograd Engine.
+```
+pip install torch===1.4.0
+```
+or use [the PyTorch website](https://pytorch.org/)
+
+
+
+### Testing
+
+The ```test_CNN.py``` script runs the forward- and backwardpass of all Layers, Activations and Losses with random shaped inputs
+and checks the results with the PyTorch-Autograd Engine.
+
+I also wrote a small Network in the ```FashionNet.py``` file, which trains a small Model with the FashionMNIST dataset.
+The Model was trained for only one epoch and returned some descend results. They aren't the best, but my test with the same Model in PyTorch got a similar result, so it must be the bad architecture and the short training of only one epoch.
+![Plot of Loss and Accuracy](FashionMNIST_model_graph.png)
+*Note: the Testing Loss and Accuracy  is more stable because the testing batch was four times the size of the training batch*
+
+
+
+### Features
+
+**Layers:**
+  - Linear
+  - Convolution (2D)
+  - Transposed Convolution (2D)  - MaxPool (2D)
+ 
+**Activations:**
+  - ReLU
+  - LeakyReLU
+  - Tanh
+  - Sigmoid
+  - Softmax
+  - LogSoftmax
+
+**Losses:**
+  - MSELoss, MAELoss (L1-Loss)
+  - BinaryCrossEntropyLoss
+  - CrossEntropyLoss
+  - SoftmaxLoss (Softmax + CrossEntropy)
+  
+**Optimizer:**
+  - SGD (Momentum, Nesterov)
+  - Adagrad
+  - RMSprop
+  - Adam
+  
+ 
+ 
+ ### Acknowledgments
+ For the Softmax, LogSoftmax and CrossEntropyLoss-Module I used
+ the numerical more stable functions implemented in the PyTorch Library!
+ You should definetly check this amazing Library out ;) luv u :*
+ 
+ Also a great source for Convolutions and Optimizer were [the CS231n course notes](http://cs231n.github.io/)
+
+ 
+ 
+