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