104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
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import tensorflow as tf
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import functools
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import csv
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import time
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from tensorflow.keras.callbacks import TensorBoard
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LABEL_COLUMN = 'is_cheat'
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# "I:\csgoserver\steamcmd\steamapps\common\Counter-Strike Global Offensive Beta - Dedicated Server\csgo\addons\sourcemod\训练数据.csv"
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train_file_path = [
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r"I:\csgoserver\steamcmd\steamapps\common\Counter-Strike Global Offensive Beta - Dedicated Server\csgo\addons\sourcemod\训练数据.csv"]
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test_file_path = [
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r"I:\csgoserver\steamcmd\steamapps\common\Counter-Strike Global Offensive Beta - Dedicated Server\csgo\addons\sourcemod\测试数据1.csv"]
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csv_data = []
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csv_data_x = []
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csv_data_y = []
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csv_data_all_num = []
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csv_data_y_avg = {}
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with open(train_file_path[0], 'r') as f:
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reader = csv.reader(f)
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for row in reader:
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csv_data.append(row)
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csv_data_x = csv_data[0]
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for index in range(1, len(csv_data)):
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for zeus in range(len(csv_data[index])):
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if csv_data_x[zeus] == 'is_cheat':
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continue
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if csv_data_x[zeus] in csv_data_y_avg.keys():
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csv_data_y_avg[csv_data_x[zeus]] = float(
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csv_data_y_avg[csv_data_x[zeus]]) + float(csv_data[index][zeus])
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else:
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csv_data_y_avg[csv_data_x[zeus]] = float(csv_data[index][zeus])
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for key in csv_data_y_avg:
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csv_data_y_avg[key] = float(csv_data_y_avg[key]) / float(len(csv_data) - 1)
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print(csv_data_y_avg)
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def get_dataset(file_path):
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dataset = tf.data.experimental.make_csv_dataset(
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file_path,
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batch_size=32,
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label_name=LABEL_COLUMN,
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na_value="?",
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num_epochs=1,
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ignore_errors=True)
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return dataset
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def process_continuous_data(mean, data):
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# 标准化数据
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data = tf.cast(data, tf.float32) * 1/(2*mean)
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return tf.reshape(data, [-1, 1])
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raw_train_data = get_dataset(train_file_path)
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raw_test_data = get_dataset(test_file_path)
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examples, labels = next(iter(raw_train_data)) # 第一个批次
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print("EXAMPLES: \n", examples, "\n")
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print("LABELS: \n", labels)
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numerical_columns = []
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for feature in csv_data_y_avg.keys():
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num_col = tf.feature_column.numeric_column(
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feature, normalizer_fn=functools.partial(process_continuous_data, csv_data_y_avg[feature]))
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numerical_columns.append(num_col)
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preprocessing_layer = tf.keras.layers.DenseFeatures(numerical_columns)
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model = tf.keras.Sequential([
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preprocessing_layer,
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid'),
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])
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model_name = "anti_aimbot-{}".format(int(time.time()))
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TensorBoardcallback = TensorBoard(
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log_dir='logs/{}'.format(model_name),
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histogram_freq=1, batch_size=32,
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write_graph=True, write_grads=False, write_images=True,
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embeddings_freq=0, embeddings_layer_names=None,
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embeddings_metadata=None, embeddings_data=None, update_freq=500
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)
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model.compile(
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loss='binary_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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train_data = raw_train_data.shuffle(1500)
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test_data = raw_test_data
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history = model.fit(train_data, epochs=85, callbacks=[TensorBoardcallback])
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test_loss, test_accuracy = model.evaluate(test_data)
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predict_data = model.predict(test_data)
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print('anti-aimbot: \n\nTest Loss {}, Test Accuracy {}'.format(test_loss, test_accuracy))
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for pre, result in zip(predict_data[:20], list(test_data)[0][1][:20]):
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print("player is aimbot :{:5.2f}% ".format(
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100 * pre[0]), " is cheat: ", ("yes" if bool(result) else "no"))
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model.save_weights('./save/model_weight')
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