162 lines
4.8 KiB
Python
162 lines
4.8 KiB
Python
import pandas as pd
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import numpy as np
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import tensorflow as tf
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import matplotlib.pyplot as plt
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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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# 归一化处理
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X = (X - np.min(X)) / (np.max(X) - np.min(X))
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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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for i in range(0, len(X) - time_steps):
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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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return np.array(X_series).reshape(-1, time_steps, 1), np.array(Y_series)
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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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# 获取类别数量
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n_classes = len(np.unique(Y_train))
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# 构建模型
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model = tf.keras.models.Sequential([
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tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(34, 1)),
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tf.keras.layers.LSTM(50),
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tf.keras.layers.Dense(n_classes, activation='softmax') # 修改为适应多分类
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])
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# 编译模型
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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# 训练模型
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model.fit(X_train, Y_train, epochs=3000, validation_split=0.2)
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# 评估模型
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loss, accuracy = model.evaluate(X_test, Y_test)
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# 制作扰动数据
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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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# 定义损失函数
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loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()
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# 转换X_train和Y_train为TensorFlow张量
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X_train_tensor = tf.convert_to_tensor(X_train, dtype=tf.float32)
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Y_train_tensor = tf.convert_to_tensor(Y_train, 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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# 计算每个输入的梯度的L2范数
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gradient_magnitudes = tf.norm(gradients, axis=1)
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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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# 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]: # 你需要选择一个合适的γ值
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num_gradients_to_select = int(
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gamma * tf.size(gradient_magnitudes, out_type=tf.dtypes.float32))
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# 获取最大梯度的索引
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# tf.argsort返回的是升序排列的，所以用[::-1]来获得降序的索引
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top_gradients_indices = tf.argsort(gradient_magnitudes, direction='DESCENDING')[
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:num_gradients_to_select]
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# 创建集合A，这里是所有选定的最大梯度的索引
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A = top_gradients_indices.numpy()
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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(gradients)
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# 获取所有索引
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all_indices = tf.range(start=0, limit=tf.size(
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gradient_magnitudes, out_type=tf.dtypes.int32), dtype=tf.int32)
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# 创建一个布尔掩码，其中集合A中的索引为False，其他为True
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mask = ~tf.reduce_any(tf.equal(tf.expand_dims(
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all_indices, 1), tf.reshape(A, (1, -1))), axis=1)
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# 使用这个掩码更新不在集合A中的梯度为0
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updated_gradients = tf.where(
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mask, updated_gradients, tf.zeros_like(updated_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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# 更新X_train_tensor
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X_train_updated = X_train_tensor - updated_gradients
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# 编译模型
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model.compile(
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optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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# 训练模型
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model.fit(X_train, Y_train, epochs=3000, validation_split=0.2)
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# 评估模型
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loss, accuracy = model.evaluate(X_test, Y_test)
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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, 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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