函数原型：

tf.layers.dense(
	inputs,
	units,
	activation=None,
	use_bias=True,
	kernel_initializer=None,
	bias_initializer=tf.zeros_initializer(),
	kernel_regularizer=None,
	bias_regularizer=None,
	activity_regularizer=None,
	kernel_constraint=None,
	bias_constraint=None,
	trainable=True,
	name=None,
	reuse=None 
	)

---

举例：

假设有维度为4 的输入向量 (3, 4, 5, 6)，要经过全连接层计算得到输出为2的向量， 对应的权重为(1, 1, 1, 1) 和 (2, 2, 2, 2), 全连接层的计算示意图如下：

矩阵表示为：

o u t p u t = i n p u t × W T = [ 3 4 5 6 ] [ 1 2 1 2 1 2 1 2 ] = [ 18 36 ] output=input \times W^T= \left[ \begin{matrix} 3 & 4 & 5 & 6 \\ \end{matrix} \right] \left[ \begin{matrix} 1 & 2 \\ 1 & 2\\ 1 & 2\\ 1 & 2\\ \end{matrix} \right] = \left[ \begin{matrix} 18 & 36 \\ \end{matrix} \right] output=input×WT=[3​4​5​6​]⎣⎢⎢⎡​1111​2222​⎦⎥⎥⎤​=[18​36​]

import tensorflow as tf
import numpy as np

# input_shape = [C, W, H]
input = [[1, 2, 3, 4]]

# input_shape = [input_channel;, width, height]
weights = [[1, 1, 1, 1],
           [2, 2, 2, 2]]

input_tf = tf.constant(input, dtype=tf.float32)
weights = np.transpose(weights, (1, 0))
weights_init = tf.constant_initializer(weights)

output_tf = tf.layers.dense(input_tf, 2, kernel_initializer=weights_init)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    output = sess.run(output_tf)
    print(output)


# 输出为：
# [[10. 20.]]