以CWRU的0HP文件夹数据集为例编写基于python的mmd损失训练DCNN代码
时间: 2024-05-10 13:14:48 浏览: 207
基于CNN训练demo
4星 · 用户满意度95%
好的,以下是基于Python的使用MMD损失函数训练DCNN模型的代码:
首先,我们需要导入需要的库,包括tensorflow、numpy、sklearn等:
```python
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
```
然后,我们需要定义一些超参数,包括学习率、迭代次数、批次大小等:
```python
learning_rate = 0.001
num_epochs = 100
batch_size = 128
```
接着,我们需要读取数据集。这里我们使用CWRU的0HP文件夹数据集,该数据集包含10个类别的轴承故障数据。我们将数据集分为训练集和测试集,并且将数据进行标准化处理:
```python
# 读取数据集
data = np.load('0HP.npy')
labels = np.load('0HP_labels.npy')
# 将数据集分为训练集和测试集
train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size=0.2)
# 对数据进行标准化处理
mean = np.mean(train_data, axis=0)
std = np.std(train_data, axis=0)
train_data = (train_data - mean) / std
test_data = (test_data - mean) / std
```
接下来,我们需要构建DCNN模型。这里我们使用包含3个卷积层和2个全连接层的模型。我们将MMD损失函数定义为模型的一部分,并且使用Adam优化器进行训练:
```python
# 定义MMD损失函数
def mmd_loss(source_features, target_features):
source_mean = tf.reduce_mean(source_features, axis=0)
target_mean = tf.reduce_mean(target_features, axis=0)
source_cov = tf.matmul(tf.transpose(source_features - source_mean), source_features - source_mean)
target_cov = tf.matmul(tf.transpose(target_features - target_mean), target_features - target_mean)
return tf.reduce_sum(tf.square(source_cov - target_cov))
# 定义DCNN模型
def dcnn_model(input_shape, num_classes):
model = tf.keras.Sequential([
tf.keras.layers.Conv1D(32, 5, activation='relu', input_shape=input_shape),
tf.keras.layers.MaxPooling1D(2),
tf.keras.layers.Conv1D(64, 5, activation='relu'),
tf.keras.layers.MaxPooling1D(2),
tf.keras.layers.Conv1D(128, 5, activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
return model
# 构建模型
input_shape = train_data[0].shape
num_classes = len(np.unique(train_labels))
model = dcnn_model(input_shape, num_classes)
# 将MMD损失函数添加到模型中
source_features = model(train_data)
target_features = model(test_data)
mmd_loss_value = mmd_loss(source_features, target_features)
model.add_loss(mmd_loss_value)
# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# 训练模型
model.fit(train_data, train_labels, epochs=num_epochs, batch_size=batch_size, validation_data=(test_data, test_labels))
```
最后,我们可以使用训练好的模型对测试集进行预测,并且计算混淆矩阵和分类准确率:
```python
# 对测试集进行预测
predictions = model.predict(test_data)
predicted_labels = np.argmax(predictions, axis=1)
# 计算混淆矩阵和分类准确率
cm = confusion_matrix(test_labels, predicted_labels)
accuracy = np.trace(cm) / np.sum(cm)
print('Confusion matrix:\n', cm)
print('Classification accuracy:', accuracy)
```
希望这份代码能对你有所帮助!
阅读全文