验证可能是数据集存在问题，准备大改

main.py

@@ -1,14 +1,20 @@
 import pandas as pd
 import numpy as np
+import os
+import warnings
+warnings.filterwarnings('ignore')
 import tensorflow as tf
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 import matplotlib.pyplot as plt
 from keras import regularizers
 from keras.callbacks import TensorBoard, LearningRateScheduler
 from keras.layers import Dropout
 from sklearn.preprocessing import MinMaxScaler
+from tqdm import tqdm
 
 
 def data_read(data_address):
+  
     df = pd.read_csv(data_address)
     label_mapping = {label: idx for idx,
                      label in enumerate(df['Problem'].unique())}
@@ -20,8 +26,6 @@ def data_read(data_address):
     X = np.array(df['Voltage'])
     Y = np.array(df['Problem'])
 
-    X = tf.nn.relu(X)
-
     # 转换为时间序列数据格式
     time_steps = 34
     X_series, Y_series = [], []
@@ -32,20 +36,72 @@ def data_read(data_address):
     return np.array(X_series), np.array(Y_series)
   
 
+# 编写绘图函数，画出训练集电压数据
+def plant_for_voltage(x_train_new, x_train_orginal, gamma, learning_rate, y_train, different_location):
+  # 绘制X_train的图形
+  for i in different_location:
+    if i % 100 == 0:
+      plt.figure()
+      time_Step = list(range(0, 34))
+      plt.plot(time_Step,
+              x_train_new[i])
+      # 添加标题和标签
+      plt.title(f'gamma:{gamma},learning_rate:{learning_rate},Y:{y_train[i]}')
+      plt.xlabel('time_step')
+      plt.ylabel('voltage')
+      try:
+        os.makedirs(f'Liu/picture/gamma{gamma} learningrate{learning_rate}')
+      except FileExistsError:
+        pass
+      plt.savefig(f'Liu/picture/gamma{gamma} learningrate{learning_rate}/X_train_new_{i}.png')
+      plt.close()
+      plt.clf()
+      
+      # 画出原始图像
+      plt.figure()
+      time_Step = list(range(0, 34))
+      plt.plot(time_Step,
+              x_train_orginal[i])
+      # 添加标题和标签
+      plt.title(f'gamma:{gamma},learning_rate:{learning_rate},Y:{y_train[i]}')
+      plt.xlabel('time_step')
+      plt.ylabel('voltage')
+      try:
+        os.makedirs(f'Liu/picture/gamma{gamma} learningrate{learning_rate}')
+      except FileExistsError:
+        pass
+      plt.savefig(f'Liu/picture/gamma{gamma} learningrate{learning_rate}/X_train_{i}.png')
+      plt.close()
+      plt.clf()
+
+
+
+def if_diff(a, b):
+  # 比较两个数组相同位置上的元素是否相等
+  diff = np.where(a != b)
+  
+  list_diff = []
+
+  # 打印不同元素的索引及其对应的元素
+  for i in range(len(diff[0])):
+      idx = (diff[0][i], diff[1][i])
+      list_diff.append(idx[0])
+  return list_diff
+
 X_train, Y_train = data_read(
     'Liu\data\VOLTAGE-QUALITY-CLASSIFICATION-MODEL--main\Voltage Quality.csv')
 X_test, Y_test = data_read(
     'Liu/data/VOLTAGE-QUALITY-CLASSIFICATION-MODEL--main/Voltage Quality Test.csv')
 
-# 归一化
+# 初始化归一化模型
-sc = MinMaxScaler(feature_range=(0, 1))
+sc = MinMaxScaler(feature_range=(-1, 1))
 X_train = sc.fit_transform(X_train)
 X_test = sc.transform(X_test)
 
+
 X_train.reshape(-1, 34, 1)
 X_test.reshape(-1, 34, 1)
 
-
 np.random.seed(7)
 np.random.shuffle(X_train)
 np.random.seed(7)
@@ -66,7 +122,7 @@ model = tf.keras.models.Sequential([
     tf.keras.layers.SimpleRNN(100),
     Dropout(0.2),
     tf.keras.layers.Dense(n_classes, activation='relu',
-                          kernel_regularizer=regularizers.l2(0.3))  # 适应多分类
+                          )  # kernel_regularizer=regularizers.l2(0.3)
 ])
 
 # 编译模型
@@ -94,6 +150,7 @@ initial_weights = model.get_weights()
 log_dir = "logs/fit"
 tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)
 
+
 # 制作扰动数据
 
 # 转换X_train和Y_train为TensorFlow张量
@@ -119,7 +176,7 @@ accuracy_per_gamma = {}
 flattened_gradients = tf.reshape(gradients, [-1])
 
 # 选择最大的γ * |X|个梯度
-for gamma in [0.05, 0.1, 0.2, 0.4]:
+for gamma in tqdm([0.6, 0.7, 0.8, 0.9, 0.99]):
     num_gradients_to_select = int(
         gamma * tf.size(flattened_gradients, out_type=tf.dtypes.float32))
     top_gradients_indices = tf.argsort(flattened_gradients, direction='DESCENDING')[
@@ -143,24 +200,29 @@ for gamma in [0.05, 0.1, 0.2, 0.4]:
     # 创建准确率列表
     accuracy_list = []
 
-    for learning_rate in [0.1, 0.2, 0.3, 0.4, 0.5]:
+    for learning_rate in tqdm([0.1, 0.2, 0.3, 0.4, 0.5]):
 
         # 应用学习率到梯度
-        scaled_gradients = learning_rate * updated_gradients
+        scaled_gradients = (learning_rate * 100) * updated_gradients
-
         # 使用缩放后的梯度更新X_train_tensor
         X_train_updated = tf.add(X_train_tensor, scaled_gradients)
         X_train_updated = X_train_updated.numpy()
         
+        list_diff = if_diff(X_train, X_train_updated)
+       
+        # 显示扰动数据和原始数据的可视化图像
+        # plant_for_voltage(X_train_updated, X_train, gamma, learning_rate, Y_train, list_diff)
+    
         # Reset model weights to initial weights
         model.set_weights(initial_weights)
 
         # 训练模型，添加 TensorBoard 回调
-        history = model.fit(X_train_updated, Y_train, epochs=1500,
+        history = model.fit(X_train_updated, Y_train, epochs=500,
-                            batch_size=32, callbacks=[tensorboard_callback, lr_scheduler])
+                            batch_size=32, callbacks=[tensorboard_callback, lr_scheduler], verbose=0)
 
         # 评估模型
         loss, accuracy = model.evaluate(X_test, Y_test)
+        print(f"Accuracy gamma: {gamma},learning:{learning_rate}", accuracy)
 
         # 记录准确率
         accuracy_list.append(accuracy)
@@ -177,7 +239,7 @@ learning_rates = [0.1, 0.2, 0.3, 0.4, 0.5]
 gammas = [0.05, 0.1, 0.2, 0.4]
 
 # 创建图像
-last_plt = plt.figure(figsize=(10, 6))
+plt.figure(figsize=(10, 6))
 
 # 为每个gamma值绘制曲线
 for gamma in gammas: