For beginners Detecting diabetes in indians from numpy import loadtxt from keras.models import Sequential from keras.layers import Dense from keras.models import model_from_json
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=',') X = dataset[:,0:8] y = dataset[:,8]
model = Sequential() model.add(Dense(12, input_dim=8, activation='relu')) model.add(Dense(8, activation='relu')) model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X, y, epochs=5, batch_size=10) _, accuracy = model.evaluate(X, y) print('Accuracy: %.2f' % (accuracy*100))
model_json = model.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) model.save_weights("model.h5") print("Saved model to disk")
from numpy import loadtxt from keras.models import model_from_json dataset = loadtxt('pima-indians-diabetes.csv', delimiter=',') X = dataset[:,0:8] y = dataset[:,8]
json_file = open('model.json', 'r') loaded_model_json = json_file.read() json_file.close() model = model_from_json(loaded_model_json) model.load_weights("model.h5") print("Loaded model from disk")
predictions = model.predict_classes(X)
for i in range(5,10):
print('%s => %d (expected %d)' % (X[i].tolist(), predictions[i], y[i]))
