klab-02-1-linear_regression.py

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


x_train = [1, 2, 3, 4]
y_train = [0, -1, -2, -3]

model = Sequential()
model.add(Dense(1, input_dim=1))

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

# prints summary of the model to the terminal
model.summary()

model.fit(x_train, y_train, epochs=200)

y_predict = model.predict(np.array([5]))
print(y_predict)