写一维胶囊网络的代码keras并训练

好的，下面是一维胶囊网络的Keras代码和训练：

from keras import layers, models

# 定义胶囊层
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):
        assert len(input_shape) >= 3
        self.input_num_capsule = input_shape[-2]
        self.input_dim_capsule = input_shape[-1]

        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 (None, self.num_capsule, self.dim_capsule)


# 定义Squash函数
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


# 定义模型
def CapsuleNetwork(input_shape, n_class, num_capsule, dim_capsule, routings):
    x = layers.Input(shape=input_shape)

    # 第一层卷积和池化
    conv1 = layers.Conv1D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)
    primarycaps = PrimaryCapsule(conv1, dim_capsule, num_capsule)

    # 第二层胶囊网络
    digitcaps = CapsuleLayer(num_capsule=n_class, dim_capsule=dim_capsule, routings=routings, name='digitcaps')(primarycaps)

    # 输出层
    out_caps = layers.Lambda(lambda x: K.sqrt(K.sum(K.square(x), 2)), name='out_caps')(digitcaps)
    y = layers.Input(shape=(n_class,))
    masked_by_y = Mask()([digitcaps, y])
    masked = Mask()(digitcaps)

    # Margin loss
    L = layers.Lambda(lambda x: K.sum(x, 1), name='L')(masked_by_y)

    # 构建模型
    model = models.Model([x, y], [out_caps, L])
    return model


# 定义PrimaryCapsule层
def PrimaryCapsule(inputs, dim_capsule, n_channels, kernel_size=9, strides=2, padding='valid'):
    output = layers.Conv1D(filters=dim_capsule*n_channels, kernel_size=kernel_size, strides=strides, padding=padding, name='primarycaps_conv')(inputs)
    outputs = layers.Reshape(target_shape=[-1, dim_capsule], name='primarycaps_reshape')(output)
    return layers.Lambda(squash, name='primarycaps_squash')(outputs)


# 定义Mask层
class Mask(layers.Layer):
    def call(self, inputs, **kwargs):
        if type(inputs) is list:
            assert len(inputs) == 2
            inputs, mask = inputs
        else:
            x = tf.sqrt(tf.reduce_sum(tf.square(inputs), -1))
            mask = tf.one_hot(tf.argmax(x, 1), inputs.get_shape().as_list()[1])

        masked = K.batch_flatten(inputs * K.expand_dims(mask, -1))
        return masked

    def compute_output_shape(self, input_shape):
        if type(input_shape[0]) is tuple:
            return tuple([None, input_shape[0][-1] * input_shape[1][-1]])
        else:
            return tuple([None, input_shape[-1]])

# 训练模型
model = CapsuleNetwork(input_shape=(784, 1), n_class=10, num_capsule=10, dim_capsule=16, routings=3)
model.summary()
model.compile(optimizer='adam', loss=['mse', 'mae'], loss_weights=[0.5, 0.5], metrics={'out_caps': 'accuracy'})
model.fit([x_train, y_train], [y_train, y_train], batch_size=128, epochs=50, validation_data=[[x_test, y_test], [y_test, y_test]])

其中，`CapsuleLayer` 定义了胶囊层，`PrimaryCapsule` 定义了 PrimaryCapsule 层，`Mask` 定义了 mask 层。`CapsuleNetwork` 定义了整个模型，其中包括卷积层、PrimaryCapsule 层、胶囊层和输出层。最后通过 `model.fit()` 函数进行训练。