47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Embedding
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from keras.layers import InputLayer
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from keras.layers import LSTM
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from keras import backend as K
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from keras.losses import mean_squared_error
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from keras.layers.recurrent import SimpleRNN
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import pymysql
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import pickle
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import os
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import numpy
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from data import load_pay_data
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def mean_squared_error(y_true, y_pred):
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print(dir(y_true), y_true.consumers)
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print(y_true, y_pred)
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return K.mean(K.square(y_pred - y_true), axis=-1)
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if __name__ == "__main__":
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x_train, y_train, tx_train, ty_train, input_shape = load_pay_data(80)
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model = Sequential()
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units = 500
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model.add(LSTM(units, activation='relu', dropout=0.1, input_shape=input_shape))
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# model.add(SimpleRNN(units, activation='relu'))
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# model.add(Dropout(0.1))
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model.add(Dense(1))
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model.summary()
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model.compile(loss = "mse", optimizer = 'adam')
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model.fit(x_train, y_train, batch_size=128, epochs=500)
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model.save("./predict_pay")
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p_data = model.predict(tx_train)
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for i in range(len(p_data)):
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print((p_data[i][0] - ty_train[i]) / ty_train[i], p_data[i][0], ty_train[i])
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# print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])
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