怎么用keras实现胶囊网络

使用Keras实现胶囊网络的步骤如下：

1. 安装Capsule Layers库（pip install capsulelayers）。

2. 导入所需库（numpy，tensorflow，Keras，Capsule Layers等）。

3. 构建一个胶囊层（Capsule Layer），这里以Digit Capsule层为例：

    from capsulelayers import CapsuleLayer, PrimaryCap, Length

    class DigitCaps(CapsuleLayer):
        def call(self, inputs, routing):
            # input shape=[None, input_num_capsule, input_dim_capsule]
            # inputs = K.expand_dims(inputs, 2)
            inputs = K.tile(inputs, [1, 1, self.num_capsule, 1])
            inputs = K.permute_dimensions(inputs, (0, 2, 1, 3))
            # inputs shape = [None, num_capsule, input_num_capsule, input_dim_capsule]
            inputs_hat = tf.scan(lambda ac, x:
                                     K.batch_dot(x, self.W, [2, 3]),
                                 elems=inputs,
                                 initializer=K.zeros([self.batch_size, self.num_capsule, self.input_num_capsule]))
            b = tf.zeros_like(inputs_hat[:, :, :, 0])
            for i in range(self.n_routing):
                # c shape=[batch_size, num_capsule, input_num_capsule]
                if i < self.n_routing - 1:
                    c = tf.nn.softmax(b, axis=1)
                    # outputs shape=[None, num_capsule, output_dim_capsule]
                    outputs = squash(K.batch_dot(c, inputs_hat))
                    b += K.batch_dot(outputs, inputs_hat, [2, 3])
                else: 
                    # final round，不需要使用softmax激活函数
                    outputs = squash(K.batch_dot(inputs_hat, K.softmax(b, axis=1)))
            return outputs

4. 构建主胶囊层（Primary Capsule Layer）：

    x = layers.Input(shape=input_shape)
    conv1 = layers.Conv2D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)
    primarycaps = PrimaryCap(conv1, dim_capsule=8, n_channels=32, kernel_size=9, strides=2, padding='valid')

5. 构建分类器（Classifier）：

    digitcaps = DigitCaps(num_capsule=n_class, dim_capsule=16, batch_size=batch_size, name='digitcaps')(primarycaps)
    out_caps = Length(name='capsnet')(digitcaps)

    y = layers.Input(shape=(n_class,))
    masked_by_y = Mask()([digitcaps, y])  # The true label is used to mask the output of capsule layer.
    masked = Mask()(digitcaps)  # Mask using the capsule with maximal length. For correct training this is important!

6. 编译模型并训练：

    from keras.models import Model
    from keras import optimizers
    from keras import metrics

    # 构建模型
    model = Model(inputs=[x, y], outputs=[out_caps, masked_by_y])

    # 编译模型
    model.compile(optimizer=optimizers.Adam(lr=lr),
                  loss=[margin_loss, 'mse'],
                  loss_weights=[1., 0.5],
                  metrics={'capsnet': 'accuracy'})

    # 训练模型
    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]])

我们可以根据自己的实际需求，对上述代码进行修改和优化。