基于注意力机制的胶囊神经的Python代码
时间: 2024-05-10 13:18:07 浏览: 19
以下是一个基于注意力机制的胶囊神经网络的Python代码示例:
```python
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):
self.input_num_capsules = input_shape[1]
self.input_capsule_dim = input_shape[2]
self.W = self.add_weight(shape=[self.input_num_capsules, self.num_capsules, self.input_capsule_dim, self.capsule_dim], initializer='glorot_uniform', name='W')
def squash(self, x):
squared_norm = tf.reduce_sum(tf.square(x), axis=-1, keepdims=True)
scale = squared_norm / (1 + squared_norm) / tf.sqrt(squared_norm + 1e-8)
return scale * x
def call(self, inputs, training=None):
inputs_expand = tf.expand_dims(inputs, axis=2)
inputs_tiled = tf.tile(inputs_expand, [1, 1, self.num_capsules, 1])
inputs_hat = tf.matmul(inputs_tiled, self.W)
b = tf.zeros(shape=[inputs.shape[0], self.input_num_capsules, self.num_capsules, 1])
for i in range(self.routings):
c = tf.nn.softmax(b, axis=2)
s = tf.reduce_sum(tf.multiply(c, inputs_hat), axis=1, keepdims=True)
v = self.squash(s)
b += tf.reduce_sum(tf.multiply(inputs_hat, v), axis=-1, keepdims=True)
return tf.squeeze(v, axis=1)
class AttentionLayer(layers.Layer):
def __init__(self, hidden_dim):
super(AttentionLayer, self).__init__()
self.hidden_dim = hidden_dim
def build(self, input_shape):
self.W = self.add_weight(shape=[input_shape[-1], self.hidden_dim], initializer='glorot_uniform', name='W')
self.b = self.add_weight(shape=[self.hidden_dim], initializer='zeros', name='b')
self.u = self.add_weight(shape=[self.hidden_dim], initializer='glorot_uniform', name='u')
def call(self, inputs, training=None):
inputs_hidden = tf.nn.tanh(tf.matmul(inputs, self.W) + self.b)
score = tf.matmul(inputs_hidden, self.u)
attention_weights = tf.nn.softmax(score, axis=1)
weighted_inputs = tf.multiply(inputs, tf.expand_dims(attention_weights, axis=-1))
weighted_sum = tf.reduce_sum(weighted_inputs, axis=1)
return weighted_sum
class CapsuleAttentionModel(tf.keras.Model):
def __init__(self, num_capsules, capsule_dim, hidden_dim, output_dim, routings=3):
super(CapsuleAttentionModel, self).__init__()
self.capsule_layer = CapsuleLayer(num_capsules=num_capsules, capsule_dim=capsule_dim, routings=routings)
self.attention_layer = AttentionLayer(hidden_dim=hidden_dim)
self.output_layer = layers.Dense(output_dim, activation='softmax')
def call(self, inputs, training=None):
x = self.capsule_layer(inputs)
x = self.attention_layer(x)
x = self.output_layer(x)
return x
```
这个代码实现了一个基于注意力机制的胶囊神经网络模型,包括一个胶囊层、一个注意力层和一个输出层。在实现过程中,我们使用了TensorFlow 2.x的Keras API。