使用自定义图层在tensorflow / tflearn中定义连接

Question

我想使用矩阵指定激活节点之间的连接，而不是完全连接的层。例如：

我有一个20节点层连接到10节点层。使用典型的完全连接层，我的W矩阵是20 x 10，具有大小为10的b向量。

我的激活看起来像relu(Wx + b)

如果我有一个与W大小相同的1和0矩阵，我们称之为F，我可以在W和F之间进行成对乘法，以删除我的第一层（20个节点）和第二层（10个节点）之间的连接）

这是我目前的代码：

F.shape
# (20, 10)
import tflearn
import tensorflow as tf

input = tflearn.input_data(shape=[None, num_input])

first = tflearn.fully_connected(input, 20, activation='relu')
# Here is where I want to use a custom function, that uses my F matrix
# I dont want the second layer to be fully connected to the first, 
# I want only connections that are ones (and not zeros) in F

# Currently:
second = tflearn.fully_connected(first, 10, activation='relu')
# What I want:
second = tflearn.custom_layer(first, my_fun)

my_fun给我的地方：relu( (FW)X + b)和FW是成对乘法

我该如何创建这个功能？我似乎无法找到关于如何完成的详细示例，但我也知道tflearn也允许基本张量流函数

Answer 1

很难用tflearn严格执行此操作，但如果您愿意包含基本张量流操作，那么它很简单：

F.shape
# (20, 10)
import tflearn
import tensorflow as tf

input = tflearn.input_data(shape=[None, num_input])
tf_F = tf.constant(F, shape=[20, 10])

first = tflearn.fully_connected(input, 20, activation='relu')
# Here is where I want to use a custom function, that uses my F matrix
# I want only connections that are ones (and not zeros) in F

# Old:
# second = tflearn.fully_connected(first, 10, activation='relu')
# Solution:
W = tf.Variable(tf.random_uniform([20, 10]), name='Weights')
b = tf.Variable(tf.zeros([10]), name='biases')
W_filtered = tf.mul(tf_F, W)
second = tf.matmul( W_filtered, first) + b