CNN From Scratch

Implemented a convolutional neural network for an image classification task. There are six basic components in the neural network:

• Convolution layer : there will be four (hyper)parameters: the number of output channels, filter dimension, stride, padding.
• Activation layer : implement an element-wise ReLU.
• Max-pooling layer : there will be two parameters: filter dimension, stride.
• Fully-connected layer : a dense layer. There will be one parameter: output dimension.
• Flattening layer : it will convert a (series of) convolutional filter maps to a column vector.
• Softmax layer : it will convert final layer projections to normalized probabilities.

The model architecture should be given in a text file. A sample architecture is given in the input file for convenience. Two models were trained on the given architecture on MNIST and CIFAR-10 dataset both for 5 epochs and the evaluation scores are given in the files mnist_report.txt and CIFAR-10_report.txt.

How to run

$ python 1605042.py <input_architecture_file>