56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
|
# This is a sample Python script.
|
||
|
|
|
||
|
|
# Press Shift+F10 to execute it or replace it with your code.
|
||
|
|
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
|
||
|
|
from keras.models import Sequential, load_model
|
||
|
|
from keras.layers import Dense, Dropout
|
||
|
|
from keras.optimizers import Adam
|
||
|
|
import pandas as pd
|
||
|
|
import tensorflow as tf
|
||
|
|
import numpy as np
|
||
|
|
from sklearn.preprocessing import LabelEncoder
|
||
|
|
|
||
|
|
|
||
|
|
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():
|
||
|
|
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=0.01), loss='sparse_categorical_crossentropy', metrics = ['accuracy'] )
|
||
|
|
model.summary()
|
||
|
|
|
||
|
|
history = model.fit(
|
||
|
|
x_train,
|
||
|
|
y_train,
|
||
|
|
epochs=20,
|
||
|
|
validation_split=0.2)
|
||
|
|
hist = pd.DataFrame(history.history)
|
||
|
|
hist['epoch'] = history.epoch
|
||
|
|
model.save('baltimore_model3')
|
||
|
|
|
||
|
|
|
||
|
|
train_model()
|
||
|
|
|