264 lines
7.7 KiB
Python
264 lines
7.7 KiB
Python
from tqdm import tqdm
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from sklearn.preprocessing import MinMaxScaler
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from keras.layers import Dropout
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from keras.callbacks import TensorBoard, LearningRateScheduler
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from keras import regularizers
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import matplotlib.pyplot as plt
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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 os
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import warnings
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warnings.filterwarnings('ignore')
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tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
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def data_read(data_address):
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df = pd.read_csv(data_address)
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label_mapping = {label: idx for idx,
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label in enumerate(df['Problem'].unique())}
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df['Problem'] = df['Problem'].map(label_mapping)
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df.replace([np.inf, -np.inf], np.nan, inplace=True)
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df.dropna(inplace=True)
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X = np.array(df['Voltage'])
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Y = np.array(df['Problem'])
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# 转换为时间序列数据格式
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time_steps = 34
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X_series, Y_series = [], []
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i = 0
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while i < len(X):
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X_series.append(X[i:(i + time_steps)])
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Y_series.append(Y[i + time_steps - 1])
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i += time_steps
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return np.array(X_series), np.array(Y_series)
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# 编写绘图函数，画出训练集电压数据
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def plant_for_voltage(x_train_new, x_train_orginal, gamma, learning_rate, y_train, different_location):
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# 绘制X_train的图形
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for i in different_location:
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if i % 100 == 0:
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plt.figure()
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time_Step = list(range(0, 34))
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plt.plot(time_Step,
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x_train_new[i])
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# 添加标题和标签
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plt.title(
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f'gamma:{gamma},learning_rate:{learning_rate},Y:{y_train[i]}')
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plt.xlabel('time_step')
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plt.ylabel('voltage')
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try:
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os.makedirs(
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f'Liu/picture/gamma{gamma} learningrate{learning_rate}')
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except FileExistsError:
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pass
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plt.savefig(
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f'Liu/picture/gamma{gamma} learningrate{learning_rate}/X_train_new_{i}.png')
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plt.close()
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plt.clf()
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# 画出原始图像
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plt.figure()
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time_Step = list(range(0, 34))
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plt.plot(time_Step,
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x_train_orginal[i])
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# 添加标题和标签
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plt.title(
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f'gamma:{gamma},learning_rate:{learning_rate},Y:{y_train[i]}')
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plt.xlabel('time_step')
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plt.ylabel('voltage')
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try:
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os.makedirs(
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f'Liu/picture/gamma{gamma} learningrate{learning_rate}')
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except FileExistsError:
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pass
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plt.savefig(
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f'Liu/picture/gamma{gamma} learningrate{learning_rate}/X_train_{i}.png')
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plt.close()
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plt.clf()
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def if_diff(a, b):
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# 比较两个数组相同位置上的元素是否相等
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diff = np.where(a != b)
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list_diff = []
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# 打印不同元素的索引及其对应的元素
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for i in range(len(diff[0])):
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idx = (diff[0][i], diff[1][i])
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list_diff.append(idx[0])
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return list_diff
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X_train, Y_train = data_read(
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'Liu\data\VOLTAGE-QUALITY-CLASSIFICATION-MODEL--main\Voltage Quality.csv')
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X_test, Y_test = data_read(
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'Liu/data/VOLTAGE-QUALITY-CLASSIFICATION-MODEL--main/Voltage Quality Test.csv')
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# 初始化归一化模型
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sc = MinMaxScaler(feature_range=(-1, 1))
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X_train = sc.fit_transform(X_train)
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X_test = sc.transform(X_test)
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X_train.reshape(-1, 34, 1)
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X_test.reshape(-1, 34, 1)
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np.random.seed(7)
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np.random.shuffle(X_train)
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np.random.seed(7)
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np.random.shuffle(Y_train)
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tf.random.set_seed(7)
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# 获取类别数量
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n_classes = len(np.unique(Y_train))
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# 损失函数
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loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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# 构建使用 RNN 的模型
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model = tf.keras.models.Sequential([
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tf.keras.layers.SimpleRNN(100, return_sequences=True, input_shape=(34, 1)),
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Dropout(0.2),
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tf.keras.layers.SimpleRNN(100),
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Dropout(0.2),
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tf.keras.layers.Dense(n_classes, activation='relu',
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) # kernel_regularizer=regularizers.l2(0.3)
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])
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# 编译模型
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model.compile(
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optimizer='SGD',
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loss=loss_fn,
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metrics=['accuracy'])
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# 定义学习率指数递减的函数
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def lr_schedule(epoch):
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initial_learning_rate = 0.01
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decay_rate = 0.1
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decay_steps = 250
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new_learning_rate = initial_learning_rate * \
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decay_rate ** (epoch / decay_steps)
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return new_learning_rate
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# 定义学习率调度器
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lr_scheduler = LearningRateScheduler(lr_schedule)
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# TensorBoard 回调
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log_dir = "logs/fit"
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tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)
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# 训练模型，添加 TensorBoard 回调
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model.fit(X_train, Y_train, epochs=500,
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batch_size=32, callbacks=[tensorboard_callback, lr_scheduler])
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# 评估模型
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loss, accuracy = model.evaluate(X_test, Y_test)
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print("Test original accuracy:", accuracy)
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# 制作扰动数据
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# 转换X_test和Y_test为TensorFlow张量
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X_train_tensor = tf.convert_to_tensor(X_test, dtype=tf.float64)
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Y_train_tensor = tf.convert_to_tensor(Y_test, dtype=tf.int32)
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# 使用tf.GradientTape来计算梯度
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with tf.GradientTape() as tape:
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# 确保tape监视输入张量
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tape.watch(X_train_tensor)
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# 前向传播，计算预测值
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predictions = model(X_train_tensor)
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# 计算损失
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loss = loss_fn(Y_train_tensor, predictions)
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# 计算关于输入X的梯度
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gradients = tape.gradient(loss, X_train_tensor)
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# 创建每个gamma对应的准确率的字典
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accuracy_per_gamma = {}
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# 平坦化梯度
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flattened_gradients = tf.reshape(gradients, [-1])
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# 选择最大的γ * |X|个梯度
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for gamma in [0.05, 0.1, 0.2, 0.4]:
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num_gradients_to_select = int(
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gamma * tf.size(flattened_gradients, out_type=tf.dtypes.float32))
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top_gradients_indices = tf.argsort(flattened_gradients, direction='DESCENDING')[
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:num_gradients_to_select]
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# 创建一个新的梯度张量，初始化为原始梯度的副本
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updated_gradients = tf.identity(flattened_gradients)
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# 创建一个布尔掩码，其中选定的最大梯度为False，其他为True
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mask = tf.ones_like(updated_gradients, dtype=bool)
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mask = tf.tensor_scatter_nd_update(mask, tf.expand_dims(
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top_gradients_indices, 1), tf.zeros_like(top_gradients_indices, dtype=bool))
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# 使用这个掩码更新梯度
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updated_gradients = tf.where(mask, tf.zeros_like(
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updated_gradients), updated_gradients)
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# 将梯度重构为原始形状
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updated_gradients = tf.reshape(updated_gradients, tf.shape(gradients))
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# 创建准确率列表
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accuracy_list = []
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for learning_rate in [0.1, 0.2, 0.3, 0.4, 0.5]:
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# 应用学习率到梯度
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scaled_gradients = (learning_rate * 17000) * updated_gradients
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# 使用缩放后的梯度更新X_train_tensor
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X_train_updated = tf.add(X_train_tensor, scaled_gradients)
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X_train_updated = X_train_updated.numpy()
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# 显示扰动数据和原始数据的可视化图像
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# plant_for_voltage(X_train_updated, X_train, gamma, learning_rate, Y_train, list_diff)
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# 评估模型
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loss, accuracy = model.evaluate(X_train_updated, Y_test)
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print(f"Accuracy gamma: {gamma},learning:{learning_rate}", accuracy)
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# 记录准确率
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accuracy_list.append(accuracy)
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# 记录该gamma下的准确率
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accuracy_per_gamma[gamma] = accuracy_list
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# 学习率样本
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learning_rates = [0.1, 0.2, 0.3, 0.4, 0.5]
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# 不同的gamma值
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gammas = [0.05, 0.1, 0.2, 0.4]
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# 创建图像
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plt.figure(figsize=(10, 6))
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# 为每个gamma值绘制曲线
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for gamma in gammas:
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plt.plot(learning_rates,
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accuracy_per_gamma[gamma], marker='o', label=f'Gamma={gamma}')
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# 添加标题和标签
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plt.title('Accuracy vs Learning Rate for Different Gammas')
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plt.xlabel('Learning Rate')
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plt.ylabel('Accuracy')
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plt.legend()
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# 显示图像
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plt.show()
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