keras生成的網路結構如下圖：
程式碼如下：
from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import LSTM, Dense, Activation from keras.utils.vis_utils import plot_model import matplotlib.pyplot as plt import numpy as np seq = 10 x = np.arange(0, 6 * np.pi, 0.01) y = np.sin(x) + np.cos(x) * x fig = plt.figure(1) plt.plot(y, 'r') train = np.array(y).astype(float) scaler = MinMaxScaler() train = scaler.fit_transform(train) data = [] for i in range(len(train) - seq - 1): data.append(train[i: i + seq + 1]) data = np.array(data).astype('float64') x = data[:, :-1] y = data[:, -1] split = int(data.shape[0] * 0.5) train_x = x[: split] train_y = y[: split] test_x = x # [split:] test_y = y # [split:] train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 1)) test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 1)) model = Sequential() model.add(LSTM(input_dim=1, output_dim=6, return_sequences=True)) model.add(LSTM(100, return_sequences=False)) model.add(Dense(output_dim=1)) model.add(Activation('linear')) model.summary() model.compile(loss='mse', optimizer='rmsprop') model.fit(train_x, train_y, batch_size=50, nb_epoch=100, validation_split=0.1) predict_y = model.predict(test_x) predict_y = np.reshape(predict_y, (predict_y.size,)) predict_y = scaler.inverse_transform([[i] for i in predict_y]) test_y = scaler.inverse_transform(test_y) fig2 = plt.figure(2) plt.plot(predict_y, 'g') plt.plot(test_y, 'r') plt.show() plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=False)
擬合結果：
