Using scikit API make_blob and keras, I created distinct number of clusters (number of classes) of data that I trained using a simple fully connected multilayer-perceptron network.
HOW TO RUN PROGRAM
1. Using anaconda, import the environment from environment.yml
2. Make any desired changes to parameters.py
3. Run classifier.py
4. Run tester.py
5. Check out the figs/ folder for results
After running the program, new plots should be generated under figs/ The trained model should be saved to saved_models/ A gif of the networks predictions at the various epochs can also be found in figs/ but will also be saved to networks/
Below are the results using my data and network parameters.
The model is a simple mlp with 3 hidden layers, all using the relu activation function (output layer uses sigmoid). The summary can be found below:
| Model Summary |
| Layer (type) | Output Shape | Param # |
|---|---|---|
| dense_1 (Dense) | (None, 32) | 96 |
| dense_2 (Dense) | (None, 32) | 1056 |
| dense_3 (Dense) | (None, 16) | 528 |
| dense_4 (Dense) | (None, 4) | 68 |
Total params: 1,748
Trainable params: 1,748
Non-trainable params: 0
The training data consists of 4 distinct classes, made up from 100 data points.
Here is a plot of the training and validation loss dring the training process. The data can be also found under saved_models/old_log.txt.
The test data is similar to the training data, however there is more data (200 points). When the model evaluates the training data, the labels should correspond to this graph.
Here is a plot of the models predictions over time:
The final result:
As you can see, the model predicted almost all of the samples correctly, except for the two points in the black region that were misclassified as red.
Overall, the network had an accuracy of 99.0%
Chaning n_features is not advised, as this would make the plots innacurate since they dont show the additional dimensions.
Changing n_classes will change the number of clusters. Numbers more than 4 may run into the problem of overlap in the clusters.
The seed is randomly generated, however you can easily change it to be any number you want if you want to work with the same dataset every time.
The number of nodes for each of the 3 layers are also editable.
For a full list of the editable paramaters, make sure you check out parameters.py
