Classifying images in the MNIST database for handwritten digits using the TensorFlow library with Python.

The conv2d model consists of four pairs of conv2d-max_pooling2d layers connected to two dense layers, each with 512 nodes. The output of the last dense layer is normalized such that the output of each node lies in between -1 and +1. The normalized output is fed into the output layer (dense, 10 nodes) which applies a sigmoid activation function. The argmax of the activated outputs of the output layer is deemed as the predicted digit.

This model was trained for 100 epochs and achieved an accuracy of 99.32% on the test image dataset.

Correct classifications
Predicted	4	0	1	8	7
Confidence	100.00%	100.00%	100.00%	100.00%	100.00%
Actual	4	0	1	8	7

Incorrect classifications
Predicted	0	8	2	0	3
Confidence	79.08%	30.70%	98.27%	87.85%	91.36%
2nd best prediction	6	6	8	2	5
Actual	6	0	8	2	5

The second best prediction is simply the argmax of the output with the initial prediction ignored.

The model has a very high accuracy because it can successfully recognize features such as edges and shapes, and use them to come to a conclusion. The misclassifications are mainly skewed, rotated, or otherwise abnormal (ie: Misclassification #4).

To improve accuracy, the images can be cleaned up (rotation, skew correction, growth/decay of pixels towards the average*, etc.) before they are provided to the network during both training and testing.

* This would help by normalizing images where the numbers are too narrow or bold

The flat model consists of a simple feedforward neural network with 8 layers. The input layer contains 784 nodes which represent the flattened image of a handwritten digit. The output layer consists of 10 nodes and the argmax of the output layer is deemed as the predicted digit.

This model was trained for 100 epochs and achieved an accuracy of 97.46% on the test image dataset.

Correct classifications
Predicted	5	2	0	6	4
Actual	5	2	0	6	4

Incorrect classifications
Predicted	7	8	2	2	7
Actual	1	5	0	8	8

Although the model has a relatively high accuracy, it fails to generalize well and misclassified some images that are easily legible due to the structure and approach to the problem (flattening the image).