添加层 def add_layer()
神经层里常见的参数通常有weights、biases和激励函数
weights为一个in_size行, out_size列的随机变量矩阵
biases的推荐值不为0,在0向量的基础上加0.1
Wx_plus_b, 即神经网络未激活的值
import tensorflow as tf
def add_layer(inputs, in_size, out_size, activation_function=None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
更新为:
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
)
Last updated
Was this helpful?