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train.py

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