python人工智能tensorflow函数tf.layers.dense使用方法

tf.layers.dense(
    inputs,					#层的输入
    units,					#该层的输出维度
    activation=None,		#激活函数
    use_bias=True,			
    kernel_initializer=None,  	# 卷积核的初始化器
    bias_initializer=tf.zeros_initializer(),  # 偏置项的初始化器
    kernel_regularizer=None,    # 卷积核的正则化
    bias_regularizer=None,    	# 偏置项的正则化
    activity_regularizer=None, 
    kernel_constraint=None,
    bias_constraint=None,
    trainable=True,
    name=None,  # 层的名字
    reuse=None  # 是否重复使用参数
)

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
def compute_accuracy(x_data,y_data):
    global dense2
    y_pre = sess.run(dense2,feed_dict={xs:x_data})
    correct_prediction = tf.equal(tf.arg_max(y_data,1),tf.arg_max(y_pre,1))     #判断是否相等
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))   #赋予float32数据类型，求平均。
    result = sess.run(accuracy,feed_dict = {xs:batch_xs,ys:batch_ys})   #执行
    return result
mnist = input_data.read_data_sets("MNIST_data",one_hot = "true")
xs = tf.placeholder(tf.float32,[None,784])
ys = tf.placeholder(tf.float32,[None,10])
dense1 = tf.layers.dense(
    xs,
    200,
    activation = tf.nn.tanh,            
    kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.3),
    bias_initializer=tf.constant_initializer(0.1),
    name='fc1'
)
dense2 = tf.layers.dense(
    dense1,
    10,
    activation = tf.nn.softmax,            
    kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.3),
    bias_initializer=tf.constant_initializer(0.1),
    name='fc2'
)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = dense2, labels = ys),name = 'loss')
#label是标签，logits是预测值，交叉熵。
train = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
init = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init)
    for i in range(5001):
        batch_xs,batch_ys = mnist.train.next_batch(100)
        sess.run(train,feed_dict = {xs:batch_xs,ys:batch_ys})
        if i % 1000 == 0:
            print("训练%d次的识别率为：%f。"%((i+1),compute_accuracy(mnist.test.images,mnist.test.labels)))