Chapter 5: Improving Model Accuracy

Activity 8: Weight Regularization on a Diabetes Diagnosis Classifier

Solution:

1. Load the dataset and split the dataset into a training set and a test set:

   # Load The dataset

   import numpy

   data = numpy.loadtxt(“./data/pima-indians-diabetes.csv”, delimiter=”,”)

   X = data[:,0:8]

   y = data[:,8]

   # Split the dataset into training set and test set with a 0.7-0.3 ratio

   from sklearn.model_selection import train_test_split

   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

2. Define a Keras model with two hidden layers of size eight to perform the classification. Train the model and plot the trends in training error and test error:

   # define a seed for random number generator so the result will be reproducible

   import numpy

   seed = 1

   numpy.random.seed(seed)

   # define the keras model

   from keras.models import Sequential

   from keras.layers import Dense

   model = Sequential()

   model.add(Dense(8, input_dim=8, activation=’...