Numpy CNN

A numpy based CNN implementation for classifying images.

Usage

Follow the steps listed below for using this repository after cloning it.
For examples, you can look at the code in fully_connected_network.py and cnn.py.
Data : https://www.kaggle.com/c/painter-by-numbers/data

The directory structure looks as follows

root
- data\
- layers\
- loss\
- utilities\
- cnn.py
- fully_connected_network.py

Import the required layer classes from layers folder, for example

from layers.fully_connected import FullyConnected
from layers.convolution import Convolution
from layers.flatten import Flatten

Import the activations and losses in a similar way, for example

from layers.activation import Elu, Softmax
from loss.losses import CategoricalCrossEntropy

Import the model class from utilities folder
```
from utilities.model import Model
```

Create a model using Model and layer classes

model = Model(
    Convolution(filters=5, padding='same'),
    Relu(),
    Convolution(filters=5, padding='same'),
    Relu(),
    Convolution(filters=5, padding='same'),
    Relu(),
    Convolution(filters=5, padding='same'),
    Relu(),
    Convolution(filters=5, padding='same'),
    Relu(),
    Pooling(mode='max', kernel_shape=(2, 2), stride=2),
    Flatten(),
    FullyConnected(units=4),
    FullyConnected(units=4),
    FullyConnected(units=4),
    Softmax(),
    name='cnn5'
)

Set model loss
```
model.set_loss(CategoricalCrossEntropy)
```
Train the model using
```
model.train(data, labels)
```
- set load_and_continue = True for loading trained weights and continue training
- By default the model uses AdamOptimization with AMSgrad
- It also saves the weights after each epoch to a models folder within the project
For prediction, use
```
prediction = model.predict(data)
```
For calculating accuracy, the model class provides its own function
```
accuracy = model.evaluate(data, labels)
```
To load model in a different place with the trained weights, follow till step 5 and then
```
model.load_weights()
```
Note: You will have to have similar directory structure.

The CNN implemented here is based on Andrej Karpathy's notes

adem0zer/CNNArtStyle

Numpy CNN

Usage