Chapter 4: Evaluate Your Model with Cross-Validation with Keras Wrappers

Activity 5: Model Evaluation Using Cross-Validation for a Diabetes Diagnosis Classifier

Solution:

1. Load the dataset and print its properties:

   # Load the dataset

   import numpy

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

   X = data[:,0:8]

   y = data[:,8]

   # Print the sizes of the dataset

   print(“Number of Examples in the Dataset = “, X.shape[0])

   print(“Number of Features for each example = “, X.shape[1])

   print(“Possible Output Classes = “, numpy.unique(y))

2. Here’s the expected output:
   

   Figure 4.15: Properties of the pima-indians-diabets.csv dataset

3. Define the function that returns the Keras model:

   from keras.models import Sequential

   from keras.layers import Dense

   # Create the function that returns the keras model

   def build_model():

   model = Sequential()

   model.add(Dense(16, input_dim=8, activation=’relu’))

   ...