import os os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0" from keras.models import Sequential from keras.layers import BatchNormalization, Dropout, Dense, Flatten, Conv1D from keras.optimizers import Adam import pandas as pd import sys def main(): X_train = pd.read_csv("data/X_train.csv") X_val = pd.read_csv("data/X_val.csv") y_train = pd.read_csv("data/y_train.csv") y_val = pd.read_csv("data/y_val.csv") X_train = X_train.to_numpy() X_val = X_val.to_numpy() y_train = y_train.to_numpy() y_val = y_val.to_numpy() X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1) X_val = X_val.reshape(X_val.shape[0], X_val.shape[1], 1) learning_rate = float(sys.argv[1]) epochs = int(sys.argv[2]) model = Sequential( [ Conv1D(32, 2, activation="relu", input_shape=X_train[0].shape), BatchNormalization(), Dropout(0.2), Conv1D(64, 2, activation="relu"), BatchNormalization(), Dropout(0.5), Flatten(), Dense(64, activation="relu"), Dropout(0.5), Dense(1, activation="sigmoid"), ] ) model.compile( optimizer=Adam(learning_rate=learning_rate), loss="binary_crossentropy", metrics=["accuracy"], ) model.fit( X_train, y_train, validation_data=(X_val, y_val), epochs=epochs, verbose=1, ) os.makedirs("model", exist_ok=True) model.save("model/model.keras") if __name__ == "__main__": main()