胶囊网络文本分类代码

以下是一个简单的胶囊网络文本分类代码示例：

import tensorflow as tf
from tensorflow.keras import layers

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

    def build(self, input_shape):
        _, input_num_capsules, input_capsule_dim = input_shape
        self.input_num_capsules = input_num_capsules
        self.input_capsule_dim = input_capsule_dim

        self.W = self.add_weight(
            shape=[input_num_capsules, self.num_capsules, input_capsule_dim, self.capsule_dim],
            initializer='random_normal',
            trainable=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.einsum('bcd,eid->bcei', inputs_tiled, self.W)

        b = tf.zeros([tf.shape(inputs_hat)[0], self.input_num_capsules, self.num_capsules])

        for i in range(self.routings):
            c = tf.nn.softmax(b, axis=2)
            outputs = self.squash(tf.reduce_sum(c * inputs_hat, axis=1, keepdims=True))

            if i < self.routings - 1:
                b += tf.reduce_sum(inputs_hat * outputs, axis=-1)

        return tf.squeeze(outputs, axis=1)

    def squash(self, inputs):
        squared_norm = tf.reduce_sum(tf.square(inputs), axis=-1, keepdims=True)
        scale = squared_norm / (1 + squared_norm)
        return scale * inputs / tf.sqrt(squared_norm)

class CapsuleNetwork(tf.keras.Model):
    def __init__(self, num_classes):
        super(CapsuleNetwork, self).__init__()
        self.embedding = layers.Embedding(input_dim=10000, output_dim=128)
        self.capsule_layer = CapsuleLayer(num_capsules=10, capsule_dim=16)
        self.dense = layers.Dense(units=num_classes, activation='softmax')

    def call(self, inputs):
        x = self.embedding(inputs)
        x = tf.reduce_mean(x, axis=1)
        x = self.capsule_layer(x)
        outputs = self.dense(x)
        return outputs

# 使用示例
model = CapsuleNetwork(num_classes=10)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=10, validation_data=(x_val, y_val))

请注意，这只是一个简单的示例代码，具体的实现细节可能会根据问题的具体要求而有所不同。在实际使用时，您可能需要根据自己的数据和任务对模型进行调整和优化。