99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
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import matplotlib.pyplot as plt
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import tensorflow as tf
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import numpy as np
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import keras
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from data_load import data_format
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# 加载数据集
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X_train, X_test, Y_train, Y_test = data_format('data/archive/PowerQualityDistributionDataset1.csv')
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# 设置随机种子以确保重现性
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np.random.seed(7)
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np.random.shuffle(X_test)
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np.random.seed(7)
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np.random.shuffle(Y_test)
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tf.random.set_seed(7)
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# 加载训练好的模型
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model = keras.models.load_model('model_nomal')
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# 使用测试集评估模型的初始准确率
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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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# 定义损失函数
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loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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# 将测试数据转换为TensorFlow张量
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X_test_tensor = tf.convert_to_tensor(X_test, dtype=tf.float64)
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Y_test_tensor = tf.convert_to_tensor(Y_test, dtype=tf.int32)
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# 用于存储不同gamma值下的准确率
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accuracy_per_gamma = {}
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# 遍历不同的gamma值
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for gamma in [0.05, 0.1, 0.2, 0.4]:
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# 使用GradientTape计算梯度
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with tf.GradientTape() as tape:
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tape.watch(X_test_tensor)
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predictions = model(X_test_tensor)
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loss = loss_fn(Y_test_tensor, predictions)
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# 计算关于输入的梯度
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gradients = tape.gradient(loss, X_test_tensor)
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# 平坦化梯度以便进行处理
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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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num_gradients_to_select = int(gamma * tf.size(flattened_gradients, out_type=tf.dtypes.float32))
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top_gradients_indices = tf.argsort(flattened_gradients, direction='DESCENDING')[:num_gradients_to_select]
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# 创建新的梯度张量，初始值为原始梯度
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updated_gradients = tf.identity(flattened_gradients)
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# 创建布尔掩码，用于选择特定梯度
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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(top_gradients_indices, 1), tf.zeros_like(top_gradients_indices, dtype=bool))
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# 应用掩码更新梯度
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updated_gradients = tf.where(mask, tf.zeros_like(updated_gradients), updated_gradients)
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# 将梯度恢复到原始形状
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updated_gradients = tf.reshape(updated_gradients, tf.shape(gradients))
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# 用于存储不同学习率下的准确率
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accuracy_list = []
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# 遍历不同的学习率
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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 * 700) * updated_gradients
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# 更新X_test_tensor
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X_train_updated = tf.add(X_test_tensor, scaled_gradients)
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X_train_updated = X_train_updated.numpy()
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# 评估更新后的模型
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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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# 存储每个gamma值下的准确率
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accuracy_per_gamma[gamma] = accuracy_list
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# 定义学习率和gamma值
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learning_rates = [0.1, 0.2, 0.3, 0.4, 0.5]
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gammas = [0.05, 0.1, 0.2, 0.4]
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# 创建并绘制结果图
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plt.figure(figsize=(10, 6))
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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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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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plt.show()
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