Use trained model produced by Keras in a Racket Scheme app
Shapes and output CSV files in model_data directory:
w1 (9, 64) w1.csv w2 (64, 64) w2.csv w3 (64, 1) w3.csv
Note: biases removed.
The first two layers use a RELU and the last (third) layer uses a Sigmoid
for old Racket 6.x: raco pkg install csv-reader
for new Racket with Chez backend: raco pkg install csv
Keras Deep Neural Network using Breast Cancer Data with Explanation of Predictions
This model is trained on 497 training examples and is tested for accuracy on 151 different testing examples. The accuracy is about 97%.
The Python example code provides a simple example of using CSV data files with TensorFlow and training a model with three hidden layers.
I assume that you have Keras and TensorFlow installed.
Please read this excellent paper by Mukund Sundararajan, Ankur Taly, and Qiqi Yan
When making a prediction, you can get a scaling of which input features most contributed to a classifiaction made by the model.
** Contributions to classification for sample type benign sample ** Clump Thickness : -15 Uniformity of Cell Size : 19 Uniformity of Cell Shape : -5 Marginal Adhesion : -15 Single Epithelial Cell Size : -100 Bare Nuclei : -5 Bland Chromatin : -70 Normal Nucleoli : -5 Mitoses : 9 ** Contributions to classification for sample type malignant sample ** Clump Thickness : 27 Uniformity of Cell Size : 8 Uniformity of Cell Shape : 15 Marginal Adhesion : -21 Single Epithelial Cell Size : -8 Bare Nuclei : 100 Bland Chromatin : 20 Normal Nucleoli : 5 Mitoses : 3
A version of this code was used in a book I wrote
The github repository for my book "Introduction to Cognitive Computing" contains an older version of this example.
Universary of Wisconcin Cancer Data
- 0 Clump Thickness 1 - 10 - 1 Uniformity of Cell Size 1 - 10 - 2 Uniformity of Cell Shape 1 - 10 - 3 Marginal Adhesion 1 - 10 - 4 Single Epithelial Cell Size 1 - 10 - 5 Bare Nuclei 1 - 10 - 6 Bland Chromatin 1 - 10 - 7 Normal Nucleoli 1 - 10 - 8 Mitoses 1 - 10 - 9 Class (0 for benign, 1 for malignant)
I modified the original data slightly by removing the randomized patient ID and changing the target class values from (2,4) to (0,1) for (no cancer, cancer).
10,10,10,8,6,1,8,9,1,1 6,2,1,1,1,1,7,1,1,0 2,5,3,3,6,7,7,5,1,1
The last value on each input line is 0 or 1 indicating the target classification.
This example just has 2 target classifications, but you can have any number. Label target class values 0, 1, 2, etc.