def test(X, y, initializer):
title = f" test {type(initializer).__name__} "
print("-" * 20 + title + "-" * 20)
# create RBF network as keras sequential model
model = Sequential()
rbflayer = RBFLayer(10,
initializer=initializer,
betas=2.0,
input_shape=(1, ))
outputlayer = Dense(1, use_bias=False)
model.add(rbflayer)
model.add(outputlayer)
model.compile(loss='mean_squared_error', optimizer=RMSprop())
# fit and predict
model.fit(X, y, batch_size=50, epochs=2000, verbose=0)
y_pred = model.predict(X)
# show graph
plt.plot(X, y_pred) # prediction
plt.plot(X, y) # response from data
plt.plot([-1, 1], [0, 0], color='black') # zero line
plt.xlim([-1, 1])
# plot centers
centers = rbflayer.get_weights()[0]
widths = rbflayer.get_weights()[1]
plt.scatter(centers, np.zeros(len(centers)), s=20 * widths)
plt.show()
# calculate and print MSE
y_pred = y_pred.squeeze()
print(f"MSE: {MSE(y, y_pred):.4f}")
# saving to and loading from file
filename = f"rbf_{type(initializer).__name__}.h5"
print(f"Save model to file {filename} ... ", end="")
model.save(filename)
print("OK")
print(f"Load model from file {filename} ... ", end="")
newmodel = load_model(filename, custom_objects={'RBFLayer': RBFLayer})
print("OK")
# check if the loaded model works same as the original
y_pred2 = newmodel.predict(X).squeeze()
print("Same responses: ", all(y_pred == y_pred2))
# I know that I compared floats, but results should be identical
# save, widths & weights separately
np.save("centers", centers)
np.save("widths", widths)
np.save("weights", outputlayer.get_weights()[0])
rbflayer = RBFLayer(10,
initializer=InitCentersRandom(X),
betas=2.0,
input_shape=(1,))
model.add(rbflayer)
model.add(Dense(1))
model.compile(loss='mean_squared_error',
optimizer=RMSprop())
model.fit(X, y,
batch_size=50,
epochs=2000,
verbose=1)
y_pred = model.predict(X)
print(rbflayer.get_weights())
plt.plot(X, y_pred)
plt.plot(X, y)
plt.plot([-1, 1], [0, 0], color='black')
plt.xlim([-1, 1])
centers = rbflayer.get_weights()[0]
widths = rbflayer.get_weights()[1]
plt.scatter(centers, np.zeros(len(centers)), s=20*widths)
plt.show()
optimizer=RMSprop(),
metrics=['accuracy', 'mse', 'mae'])
history = model.fit(X_train, y_train,
batch_size=50,
epochs=200,
verbose=1)
y_pred = model.predict(X_test)
# get time
end = time.time()
time_total = end-start
y_pred_denormalization = scaler.inverse_transform(y_pred)
y_test_denormalization = scaler.inverse_transform(y_test)
error_graph()
graph()
print(rbflayer.get_weights())
# MAPE
mape = mape(y_test_denormalization, y_pred_denormalization)
# RMSE
rmse = np.sqrt(mean_squared_error(
y_test_denormalization, y_pred_denormalization))
forecast_result = scaler.inverse_transform(model.predict(
data_bm.iloc[-1:,0:1]))