长短期记忆模型作回归分析的示例代码 python
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from keras.models import Sequential from keras.layers import LSTM, Dense,Dropout import numpy as np import matplotlib.pyplot as plt data_dim = 16 timesteps = 8 num_classes = 1 model = Sequential() model.add(LSTM(32, return_sequences=True, input_shape=(timesteps, data_dim))) model.add(Dropout(0.5)) model.add(LSTM(32, return_sequences=True)) model.add(Dropout(0.5)) model.add(LSTM(32)) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='mae', optimizer='adam') # Generate dummy training data x_train = np.random.normal(2,0.5,size=(1000, timesteps, data_dim)) y_train = np.random.normal(2,0.5,size=(1000, num_classes)) # Generate dummy validation data x_val = np.random.normal(2,0.5,size=(100, timesteps, data_dim)) y_val = np.random.normal(2,0.5,size=(100, num_classes)) history = model.fit(x_train, y_train, batch_size=64, epochs=50, validation_data=(x_val, y_val)) # #显示训练中的训练损失和测试损失 plt.plot(history.history['loss'], label='train') plt.plot(history.history['val_loss'], label='test') plt.legend() plt.show()