klab-05-2-logistic_regression_diabetes.py

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
import numpy as np

x_data = [[1, 2],
          [2, 3],
          [3, 1],
          [4, 3],
          [5, 3],
          [6, 2]]
y_data = [[0],
          [0],
          [0],
          [1],
          [1],
          [1]]

model = Sequential()
model.add(Dense(1, input_dim=2, activation='sigmoid'))

sgd = SGD(lr=0.1)
model.compile(loss='binary_crossentropy', optimizer=sgd)

model.summary()
model.fit(x_data, y_data, epochs=2000)

print("2,1", model.predict_classes(np.array([[2, 1]])))
print("6,5", model.predict_classes(np.array([[6, 5]])))