42 lines
1008 B
Python
42 lines
1008 B
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, Activation
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from keras.layers import LSTM
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from keras import backend
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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_gift_data
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if __name__ == "__main__":
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x_train, y_train, tx_train, ty_train = load_gift_data()
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model = Sequential()
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units = 400
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model.add(LSTM(units, activation='relu', input_shape=(4,1) ))
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model.add(Dropout(0.2))
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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=1, epochs=50)
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model.save("./predict_gift")
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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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comp = (p_data[i][0] - ty_train[i]) / ty_train[i]
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print(comp, p_data[i][0], ty_train[i])
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if abs(comp) >= 0.1:
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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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