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Runtime error
refactoring part1 - minor perf updates, removed single letter names, moved functions to separate files
517420b
| import numpy as np | |
| from sklearn.preprocessing import StandardScaler | |
| from tensorflow import keras | |
| verbose = 0 | |
| # TODO: Refactor this module | |
| def predict_series(values, r1_nodes=10, r2_nodes=0, fc1_nodes=0, steps=20, use_lstm=True, seq_length = 15, *args, **kwargs): | |
| # TODO: simplify and optimize creating windows | |
| train = np.array(values) | |
| train_last_value = train[-1] | |
| train = train[1:] - train[:-1] | |
| sc = StandardScaler() | |
| train = sc.fit_transform(train.reshape(-1, 1)) | |
| X, Y = [], [] | |
| for t in range(len(train) - seq_length): | |
| x = train[t:t + seq_length] | |
| X.append(x) | |
| Y.append(train[t + seq_length]) | |
| X = np.array(X).reshape(-1, seq_length, 1) | |
| Y = np.array(Y) | |
| # TODO: Add SimpleRNN | |
| if use_lstm: | |
| rnn_layer = keras.layers.LSTM | |
| else: | |
| rnn_layer = keras.layers.GRU | |
| model = keras.Sequential() | |
| model.add(rnn_layer(r1_nodes, return_sequences=bool(r2_nodes))) | |
| if r2_nodes: | |
| model.add(rnn_layer(r2_nodes)) | |
| if fc1_nodes: | |
| model.add(keras.layers.Dense(fc1_nodes, activation='relu')) | |
| model.add(keras.layers.Dense(1)) | |
| # TODO: optimize execution time | |
| model.compile( | |
| loss='mse', | |
| optimizer=keras.optimizers.Adamax(lr=0.2)) | |
| callbacks = [keras.callbacks.EarlyStopping(patience=150, monitor='loss', restore_best_weights=True)] | |
| r = model.fit( | |
| X, Y, | |
| epochs=500, | |
| callbacks=callbacks, | |
| verbose=verbose, | |
| validation_split=0.0) | |
| predictions = np.array([]) | |
| last_x = X[-1] | |
| for _ in range(steps): | |
| p = model.predict(last_x.reshape(1, -1, 1))[0, 0] | |
| predictions = np.append(predictions, p) | |
| last_x = np.roll(last_x, -1) | |
| last_x[-1] = p | |
| predictions = sc.inverse_transform(predictions.reshape(-1, 1)) | |
| predictions.reshape(-1) | |
| predictions[0] = train_last_value + predictions[0] | |
| for i in range(1, len(predictions)): | |
| predictions[i] += predictions[i-1] | |
| result = {'result': list(predictions.reshape(-1)), 'epochs': r.epoch[-1] + 1, 'loss': min(r.history['loss']), 'loss_last': r.history['loss'][-1]} | |
| return result | |
| if __name__ == "__main__": | |
| # Code for debugging/testing | |
| from time import time | |
| t1 = time() | |
| # verbose = 2 | |
| data = np.sin(np.arange(0.0, 28.0, 0.35)*2) | |
| result = predict_series(data, steps=66, r1_nodes=14, r2_nodes=14, fc1_nodes=20) | |
| print('exec time: {:8.3f}'.format(time()-t1)) | |
| print(print(result['epochs'], result['loss'])) | |
| import seaborn as sns | |
| sns.lineplot(x=range(30), y=data[-30:], color='r') | |
| sns.lineplot(x=range(30, 30+len(result['result'])), y=result['result'], color='b') | |