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June 7, 2018 16:03
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Simple example of recurrent network using LSTM on keras
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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() |
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