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import keras | |
from keras.models import Sequential | |
from keras.layers import Dense | |
from keras.optimizers import SGD | |
from sklearn.metrics import r2_score | |
# neural network with 1 neuron and a linear activation | |
model = Sequential() | |
model.add(Dense(1, activation = 'linear', input_dim = 1)) | |
# training the network | |
model.compile(optimizer=SGD(), loss='mse') | |
model.fit(x, y, batch_size=1, epochs=5, shuffle=True, verbose=0) | |
# prediction | |
y_pred = model.predict(x) | |
# plotting | |
plt.plot(x,y, label='data') | |
plt.plot(x, beta_0 + beta_1*x, label='regression line') | |
plt.plot(x, y_pred, label='predicted ANN') | |
plt.xlabel('x') | |
plt.ylabel('y') | |
plt.legend() | |
plt.title('sample line with noise'); | |
# compare estimated parameters | |
W = model.get_weights() | |
print('slope and intercept - regression line: {0:.3f}, {1:.3f}'.format(beta_0, beta_1)) | |
print('slope and intercept - ANN: {0:.3f}, {1:.3f}'.format(W[1][0], W[0][0][0])) | |
# evaluate performance | |
print('R2 score - regression line: {0:0.2%}'.format(r2_score(y, beta_0+beta_1*x))) | |
print('R2 score - ANN : {0:0.2%}'.format(r2_score(y, y_pred))) | |
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