写一段用于故障诊断的一维胶囊网络代码keras并训练

好的，以下是一段用于故障诊断的一维胶囊网络代码keras，并附带训练代码：

from keras import layers, models, optimizers, callbacks
from sklearn.model_selection import train_test_split
import numpy as np

input_shape = (None, 1)

# 定义胶囊层
class CapsuleLayer(layers.Layer):
    def __init__(self, num_capsules, capsule_dim, routings=3):
        super(CapsuleLayer, self).__init__()
        self.num_capsules = num_capsules
        self.capsule_dim = capsule_dim
        self.routings = routings
        self.W = self.add_weight(shape=[1, input_shape[1], self.num_capsules * self.capsule_dim],
                                 initializer='random_normal', trainable=True)

    def call(self, inputs, **kwargs):
        # 转换输入形状为[batch_size, input_num_capsule, input_dim_capsule]
        inputs_expand = tf.expand_dims(inputs, axis=-1)
        inputs_tiled = tf.tile(inputs_expand, [1, 1, self.num_capsules, self.capsule_dim])
        inputs_hat = tf.keras.backend.batch_dot(inputs_tiled, self.W)
        # 动态路由过程
        b = tf.zeros(shape=[tf.shape(inputs_hat)[0], input_shape[1], self.num_capsules, 1])
        assert self.routings > 0
        for i in range(self.routings):
            c = tf.nn.softmax(b, axis=2)
            outputs = tf.keras.backend.batch_dot(tf.transpose(inputs_hat, perm=[0, 2, 1, 3]), c)
            if i < self.routings - 1:
                b += tf.keras.backend.batch_dot(outputs, inputs_hat, [3, 2])
        # 返回胶囊输出
        return tf.squeeze(outputs, axis=1)

# 定义模型
def get_model():
    x_input = layers.Input(shape=input_shape)
    conv1 = layers.Conv1D(filters=64, kernel_size=3, strides=1, padding='valid', activation='relu', name='conv1')(x_input)
    conv2 = layers.Conv1D(filters=64, kernel_size=3, strides=2, padding='valid', activation='relu', name='conv2')(conv1)
    primary_capsule = CapsuleLayer(num_capsules=8, capsule_dim=16, routings=3, name='primary_capsule')(conv2)
    output_capsule = CapsuleLayer(num_capsules=1, capsule_dim=16, routings=3, name='output_capsule')(primary_capsule)
    model = models.Model(inputs=x_input, outputs=output_capsule)
    return model

# 加载数据集
data = np.load('data.npy')
labels = np.load('labels.npy')

# 切分数据集
x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)

# 构建模型
model = get_model()
model.compile(optimizer=optimizers.Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])

# 训练模型
callbacks_list = [callbacks.EarlyStopping(monitor='val_loss', patience=5),
                  callbacks.ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
history = model.fit(x_train, y_train, batch_size=32, epochs=50, validation_data=(x_test, y_test), callbacks=callbacks_list)

# 评估模型
model.load_weights('best_model.h5')
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

这段代码定义了一个一维胶囊网络，用于故障诊断。在训练模型之前，我们需要先加载数据集并切分数据集为训练集和测试集。然后，我们构建模型，并使用Adam优化器和交叉熵损失函数编译模型。训练模型时使用早期停止和模型检查点回调函数，以防止过拟合并保存最佳模型。训练完成后，我们评估模型并输出测试集的损失和准确率。