30 lines
998 B
Python
30 lines
998 B
Python
import pandas as pd
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.optimizers import Adam
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from keras import regularizers
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from helper import prepare_tensors
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hp_train = pd.read_csv('hp_train.csv')
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hp_dev = pd.read_csv('hp_dev.csv')
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X_train, Y_train = prepare_tensors(hp_train)
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X_dev, Y_dev = prepare_tensors(hp_dev)
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model = Sequential()
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model.add(Dense(64, input_dim=7, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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model.add(Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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model.add(Dense(1, activation='linear'))
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adam = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-7)
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model.compile(optimizer=adam, loss='mean_squared_error')
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model.fit(X_train, Y_train, epochs=20, batch_size=32, validation_data=(X_dev, Y_dev))
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model.save('hp_model.h5')
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