Keras中如何使用Capsule網(wǎng)絡(luò)

小樊
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2024-03-08 12:12:34

在Keras中實(shí)現(xiàn)Capsule網(wǎng)絡(luò)可以通過使用keras.layers中的Capsule和PrimaryCap層來實(shí)現(xiàn)。下面是一個(gè)簡單的示例：

from keras import layers
from keras.models import Model

# 定義Capsule網(wǎng)絡(luò)架構(gòu)
def CapsuleModel(input_shape, n_class, routings):
    x = layers.Input(shape=input_shape)

    # 定義第一個(gè)Capsule層
    conv1 = layers.Conv2D(128, (9, 9), activation='relu', padding='valid')(x)
    primarycaps = PrimaryCap(conv1, dim_capsule=8, n_channels=32, kernel_size=9, strides=2, padding='valid')

    # 定義第二個(gè)Capsule層
    digitcaps = CapsuleLayer(num_capsule=n_class, dim_capsule=16, routings=routings)(primarycaps)

    # 輸出分類結(jié)果
    out_caps = Length()(digitcaps)
    
    return Model(x, out_caps)

# 定義PrimaryCapsule層
def PrimaryCap(inputs, dim_capsule, n_channels, kernel_size, strides, padding):
    output = layers.Conv2D(filters=dim_capsule*n_channels, kernel_size=kernel_size, strides=strides, padding=padding)(inputs)
    outputs = layers.Reshape(target_shape=(-1, dim_capsule))(output)
    return layers.Lambda(lambda x: x / K.sqrt(K.sum(K.square(x), axis=-1, keepdims=True)))(outputs)

# 定義Capsule層
class CapsuleLayer(layers.Layer):
    def __init__(self, num_capsule, dim_capsule, routings=3, kernel_initializer='glorot_uniform', **kwargs):
        super(CapsuleLayer, self).__init__(**kwargs)
        self.num_capsule = num_capsule
        self.dim_capsule = dim_capsule
        self.routings = routings
        self.kernel_initializer = initializers.get(kernel_initializer)

    def build(self, input_shape):
        input_dim_capsule = input_shape[-1]
        self.W = self.add_weight(shape=[input_dim_capsule, self.num_capsule * self.dim_capsule], initializer=self.kernel_initializer, name='W')
        self.built = True

    def call(self, inputs):
        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, 1]), elems=inputs_tiled)
        b = tf.zeros(shape=[K.shape(inputs_hat)[0], self.num_capsule, K.shape(inputs_hat)[2]])
        assert self.routings > 0
        for i in range(self.routings):
            c = tf.nn.softmax(b, dim=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 outputs

    def compute_output_shape(self, input_shape):
        return input_shape

# 定義Length層
class Length(layers.Layer):
    def call(self, inputs, **kwargs):
        return K.sqrt(K.sum(K.square(inputs), -1))

    def compute_output_shape(self, input_shape):
        return input_shape[:-1]

# 構(gòu)建Capsule網(wǎng)絡(luò)模型
model = CapsuleModel((28, 28, 1), 10, 3)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

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