写一段一维Capsnet网络代码keras并训练

好的，以下是一个使用Keras实现的一维CapsNet网络，包括训练代码：

from keras import layers, models, optimizers
from keras import backend as K
from keras.utils import to_categorical
from keras.datasets import mnist
import numpy as np

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')
        super(Capsule, self).build(input_shape)

    def call(self, inputs):
        inputs_expand = K.expand_dims(inputs, 2)
        inputs_tiled = K.tile(inputs_expand, [1, 1, self.num_capsules, 1])
        inputs_hat = K.map_fn(lambda x: K.batch_dot(x, self.W, [2, 1]), elems=inputs_tiled)
        b = tf.zeros(shape=[K.shape(inputs_hat)[0], self.num_capsules, inputs.shape[1], 1])
        for i in range(self.routings):
            c = tf.nn.softmax(b, axis=1)
            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 K.reshape(outputs, [-1, self.num_capsules * self.capsule_dim])

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

    def get_config(self):
        config = {'num_capsules': self.num_capsules,
                  'capsule_dim': self.capsule_dim,
                  'routings': self.routings}
        base_config = super(Capsule, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

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

def build_capsnet(input_shape, n_class, routings):
    x = layers.Input(shape=input_shape)

    conv1 = layers.Conv1D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)

    primarycaps = layers.Conv1D(filters=256, kernel_size=9, strides=2, padding='valid', name='primarycaps')(conv1)
    primarycaps = layers.BatchNormalization()(primarycaps)
    primarycaps = layers.Activation('relu')(primarycaps)
    primarycaps = layers.Reshape(target_shape=[-1, 8], name='primarycaps_reshape')(primarycaps)

    digitcaps = Capsule(10, 16, routings=routings, name='digitcaps')(primarycaps)

    out_caps = layers.Length(name='out_caps')(digitcaps)

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

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

    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])

def margin_loss(y_true, y_pred):
    lamb, margin = 0.5, 0.1
    return K.sum(y_true * K.square(K.maximum(0., margin - y_pred)) + lamb * (1 - y_true) * K.square(K.maximum(0., y_pred - margin)))

def train(model, data, epochs):
    (x_train, y_train), (x_test, y_test) = data

    y_train = to_categorical(y_train.astype('float32'))
    y_test = to_categorical(y_test.astype('float32'))

    model.compile(optimizer=optimizers.Adam(lr=0.001),
                  loss=[margin_loss, 'mse'],
                  loss_weights=[1., 0.1],
                  metrics={'out_caps': 'accuracy'})

    model.fit([x_train, y_train], [y_train, x_train], batch_size=128, epochs=epochs,
              validation_data=[[x_test, y_test], [y_test, x_test]])

    return model

if __name__ == '__main__':
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.astype('float32') / 255.
    x_test = x_test.astype('float32') / 255.
    x_train = np.expand_dims(x_train, axis=-1)
    x_test = np.expand_dims(x_test, axis=-1)

    model = build_capsnet(input_shape=x_train.shape[1:], n_class=10, routings=3)

    model.summary()

    train(model=model, data=((x_train, y_train), (x_test, y_test)), epochs=20)

这个代码包含了Capsule和build_capsnet两个函数，以及margin_loss和train两个函数。Capsule和build_capsnet函数和之前提供的代码一样，margin_loss是定义了模型的损失函数，train则是进行模型的训练。在主函数中，我们加载了MNIST数据集，搭建了CapsNet网络，然后进行了20个epochs的训练。