写一维胶囊网络的代码keras并训练

好的，以下是一维胶囊网络的Keras代码及训练过程：

from keras import layers, models

# 定义一维胶囊网络
class Capsule(layers.Layer):
    def __init__(self, num_capsules, capsule_dim, routings=3):
        super(Capsule, self).__init__()
        self.num_capsules = num_capsules
        self.capsule_dim = capsule_dim
        self.routings = routings

    def build(self, input_shape):
        input_dim_capsule = input_shape[-1]
        self.W = self.add_weight(shape=[input_dim_capsule, 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 = tf.expand_dims(inputs, axis=2)
        inputs_tiled = tf.tile(inputs_expand, [1, 1, self.num_capsules, 1])
        inputs_hat = tf.keras.backend.batch_dot(inputs_tiled, self.W)
        for i in range(self.routings):
            c = tf.nn.softmax(self.bias, axis=1)
            outputs = tf.keras.backend.batch_dot(c, inputs_hat, [2, 2])
            if i < self.routings - 1:
                outputs = tf.reduce_sum(outputs, axis=1, keepdims=True)
                self.bias += tf.reduce_sum(inputs_hat * outputs, axis=-1, keepdims=True)
        return tf.squeeze(outputs, axis=1)

# 定义模型
def CapsNet(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_caps = Capsule(num_capsules=32, capsule_dim=8, routings=3)(conv1)
    digit_caps = Capsule(num_capsules=num_classes, capsule_dim=16, routings=3)(primary_caps)
    out_caps = layers.Lambda(lambda x: tf.sqrt(tf.reduce_sum(tf.square(x), axis=-1)))(digit_caps)
    y = layers.Input(shape=(num_classes,))
    masked_by_y = layers.Multiply()([out_caps, y])
    masked = layers.Lambda(lambda x: x[0])(masked_by_y)
    masked = layers.Dense(units=512, activation='relu')(masked)
    masked = layers.Dense(units=1024, activation='relu')(masked)
    masked = layers.Dense(units=input_shape[0], activation='sigmoid')(masked)
    model = models.Model([x, y], [out_caps, masked])
    return model

# 训练模型
model = CapsNet(input_shape=(28, 28), num_classes=10)
model.compile(optimizer='adam', loss=['mse', 'binary_crossentropy'], metrics=['accuracy'])
model.fit([x_train, y_train], [y_train, x_train], batch_size=128, epochs=50, validation_data=[[x_test, y_test], [y_test, x_test]])

请注意，这只是一份简单的示例代码，您需要根据您的数据和任务进行适当的修改和调整。