Simple example of recurrent network using LSTM on keras

rnn.py

from math import sin

import numpy as np
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from matplotlib import pyplot


def seq_func(x):
    return sin(x)


def fit_lstm(train_x, train_y, nb_epoch, neurons):
    x, y = np.array(train_x), np.array(train_y)
    x = x.reshape(len(x), 1, 1)
    model = Sequential()
    model.add(LSTM(neurons, batch_input_shape=(1, 1, 1), stateful=True))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer='adam')
    for i in range(nb_epoch):
        model.fit(x, y, epochs=1, batch_size=1, verbose=1, shuffle=False)
        model.reset_states()
    return model


series = [seq_func(x) for x in range(36)]
supervised = [seq_func(x + 1) for x in range(len(series))]

lstm_model = fit_lstm(series, supervised, 1000, 16)

train_reshaped = np.array(series[:-1]).reshape(len(series)-1, 1, 1)
lstm_model.predict(train_reshaped, batch_size=1)

predictions = []
expectations = []
forecast_result = series[-1]
for i in range(36):
    forecast_result_reshaped = np.array([forecast_result]).reshape(1, 1, 1)
    forecast_result = lstm_model.predict(forecast_result_reshaped)[0, 0]
    predictions.append(forecast_result)
    expected = seq_func(len(series) + i)
    expectations.append(expected)
    print('x=%d, Predicted=%f, Expected=%f' % (len(series) + i, forecast_result, expected))

pyplot.plot(series + expectations)
pyplot.plot(series + predictions)
pyplot.show()