logistic_regression_keras.py

from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import accuracy_score

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

model.compile(optimizer='sgd', loss='binary_crossentropy', metrics = ['acc'])
history = model.fit(x, y, batch_size=1, epochs=100, shuffle=True, verbose=0)

print("Classification accuracy: {0:0.2%}".format(accuracy_score(y, y_pred)))

plt.scatter(x,y, label='original data')
plt.scatter(x,y_pred, label='ANN prediction')
plt.xlabel('year')
plt.ylabel('harvest good/bad')
plt.title('classification')
plt.legend();