写一维胶囊网络的代码keras并训练

好，下面是一维胶囊网络的Keras代码及训练过程：

from keras import layers, models
import numpy as np

# 定义胶囊网络层
class CapsuleLayer(layers.Layer):
    def __init__(self, num_capsule, dim_capsule, routings=3, **kwargs):
        super(CapsuleLayer, self).__init__(**kwargs)
        self.num_capsule = num_capsule
        self.dim_capsule = dim_capsule
        self.routings = routings

    def build(self, input_shape):
        self.input_num_capsule = input_shape[1]
        self.input_dim_capsule = input_shape[2]

        self.W = self.add_weight(shape=[self.input_num_capsule, self.num_capsule, self.input_dim_capsule, self.dim_capsule], initializer='glorot_uniform', name='W')

        self.built = True

    def call(self, inputs, training=None):
        inputs_expand = K.expand_dims(inputs, 2)

        inputs_tiled = K.tile(inputs_expand, [1, 1, self.num_capsule, 1])

        inputs_hat = K.map_fn(lambda x: K.batch_dot(x, self.W, [2, 3]), elems=inputs_tiled)

        b = tf.zeros(shape=[K.shape(inputs_hat)[0], self.input_num_capsule, self.num_capsule])

        for i in range(self.routings):
            c = tf.nn.softmax(b, axis=2)

            outputs = squash(K.batch_dot(c, inputs_hat, [2, 2]))

            if i < self.routings - 1:
                b += K.batch_dot(outputs, inputs_hat, [2, 3])

        return outputs

    def compute_output_shape(self, input_shape):
        return tuple([None, self.num_capsule, self.dim_capsule])

    def get_config(self):
        config = {'num_capsule': self.num_capsule, 'dim_capsule': self.dim_capsule, 'routings': self.routings}
        base_config = super(CapsuleLayer, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

# 定义模型
def get_model(input_shape, num_classes):
    x = layers.Input(shape=input_shape)

    conv1 = layers.Conv1D(filters=256, kernel_size=5, padding='valid', activation='relu', strides=1)(x)
    conv1 = layers.BatchNormalization()(conv1)

    primarycaps = PrimaryCap(conv1, dim_capsule=8, n_channels=32, kernel_size=3, strides=1, padding='valid')

    digitcaps = CapsuleLayer(num_capsule=num_classes, dim_capsule=16, routings=3)(primarycaps)

    out_caps = Length(name='capsnet')(digitcaps)

    y = layers.Input(shape=(num_classes,))

    masked = Mask()([digitcaps, y])

    x_recon = layers.Dense(512, activation='relu')(masked)
    x_recon = layers.Dense(1024, activation='relu')(x_recon)
    x_recon = layers.Dense(np.prod(input_shape), activation='sigmoid')(x_recon)
    x_recon = layers.Reshape(target_shape=input_shape, name='out_recon')(x_recon)

    return models.Model([x, y], [out_caps, x_recon])

# 训练模型
def train(model, data, epochs, batch_size=100):
    (x_train, y_train), (x_test, y_test) = data

    model.compile(optimizer='adam', loss=[margin_loss, 'mse'], loss_weights=[1., 0.5], metrics={'capsnet': 'accuracy'})

    model.fit([x_train, y_train], [y_train, x_train], batch_size=batch_size, epochs=epochs, validation_data=[[x_test, y_test], [y_test, x_test]])

这是一个简单的一维胶囊网络，包括了自定义的胶囊层和其他常见的神经网络层。使用该模型进行训练时，需要提供一个数据集（x_train, y_train, x_test, y_test），以及训练轮数和批次大小。