ium_487197/ium_train.py

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from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
import pandas as pd
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import LabelEncoder
import argparse


def get_x_y(data):

    lb = LabelEncoder()
    data = data.drop(["Location 1"], axis=1)
    data = data.drop(columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post", "CrimeDate", "Inside/Outside"], axis=1)
    for column_name in data.columns:
        data[column_name] = lb.fit_transform(data[column_name])
    x = data.drop('Weapon', axis=1)
    y = data['Weapon']



    return data, x, y


def train_model():

    parser = argparse.ArgumentParser(description='Train')

    parser.add_argument('-epochs', type=int, default=20)
    parser.add_argument('-lr', type=float, default=0.01)
    parser.add_argument('-validation_split', type=int, default=0.2)
    args = parser.parse_args()
    
    train = pd.read_csv('baltimore_train.csv')

    data_train, x_train, y_train = get_x_y(train)
    normalizer = tf.keras.layers.Normalization(axis=1)
    normalizer.adapt(np.array(x_train))
    model = Sequential(normalizer)
    model.add(Dense(64, activation="relu"))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(5, activation="softmax"))
    model.compile(Adam(learning_rate=args.lr), loss='sparse_categorical_crossentropy', metrics = ['accuracy'] )
    model.summary()

    history = model.fit(
        x_train,
        y_train,
        epochs=args.epochs,
        validation_split=args.validation_split)
    hist = pd.DataFrame(history.history)
    hist['epoch'] = history.epoch
    model.save('baltimore_model.h5')


train_model()