写用于故障诊断的一维胶囊网络代码keras

以下是一个使用Keras实现的一维胶囊网络（Capsule Network）用于故障诊断的示例代码：

from keras import layers
from keras import models
from keras import backend as K
from keras.utils import to_categorical

def squash(x, axis=-1):
    s_squared_norm = K.sum(K.square(x), axis, keepdims=True)
    scale = K.sqrt(s_squared_norm + K.epsilon())
    return x / scale

class Capsule(layers.Layer):
    def __init__(self, num_capsules, capsule_dim, routings=3, **kwargs):
        super(Capsule, self).__init__(**kwargs)
        self.num_capsules = num_capsules
        self.capsule_dim = capsule_dim
        self.routings = routings

    def build(self, input_shape):
        input_dim = input_shape[-1]
        self.W = self.add_weight(shape=(input_dim, self.num_capsules * self.capsule_dim),
                                 initializer='glorot_uniform',
                                 name='W')
        self.bias = self.add_weight(shape=(self.num_capsules, self.capsule_dim),
                                    initializer='zeros',
                                    name='bias',
                                    trainable=False)
        self.built = True

    def call(self, inputs):
        inputs_expand = K.expand_dims(inputs, 2)
        inputs_tiled = K.tile(inputs_expand, [1, 1, self.num_capsules, 1])
        W_expand = K.expand_dims(self.W, 0)
        W_tiled = K.tile(W_expand, [K.shape(inputs)[0], 1, 1])
        u_hat = K.batch_dot(inputs_tiled, W_tiled)
        b = K.zeros_like(u_hat[:, :, :, 0])
        for i in range(self.routings):
            c = K.softmax(b, axis=1)
            o = K.batch_dot(c, u_hat, [2, 2])
            o = squash(o)
            if i < self.routings - 1:
                b = K.batch_dot(o, u_hat, [2, 3])
                b = K.sum(b, axis=0)
        return o

    def compute_output_shape(self, input_shape):
        return tuple([None, self.num_capsules, self.capsule_dim])

def build_model(input_shape, num_classes):
    x = layers.Input(shape=input_shape)
    conv1 = layers.Conv1D(filters=256, kernel_size=9, padding='valid', activation='relu', strides=1)(x)
    primary_capsules = Capsule(num_capsules=8, capsule_dim=32, routings=3)(conv1)
    digit_capsules = Capsule(num_capsules=num_classes, capsule_dim=16, routings=3)(primary_capsules)
    output_capsule = layers.Lambda(lambda x: K.sqrt(K.sum(K.square(x), 2)))(digit_capsules)
    model = models.Model(inputs=x, outputs=output_capsule)
    return model

这个模型包含了一个胶囊层（Capsule）和两个卷积层（Conv1D）。在训练时，可以使用交叉熵作为损失函数，同时使用Adam优化器进行模型的优化：

from keras import optimizers

model = build_model(input_shape=(input_dim,), num_classes=num_classes)
model.compile(optimizer=optimizers.Adam(lr=0.001),
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.summary()

然后可以使用fit方法训练模型：

model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))

其中x_train和x_test是训练集和测试集的输入数据，y_train和y_test是对应的标签。训练完成后，可以使用evaluate方法评估模型的性能：

loss, acc = model.evaluate(x_test, y_test, batch_size=64)

以上就是一个使用Keras实现的一维胶囊网络用于故障诊断的示例代码，希望可以帮助你更好地理解和应用胶囊网络。