MNIST資料集手寫數字識別的一點小問題

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
#載入資料集
mnist = input_data.
read_data_sets
("mnist_data"
,one_hot=true)
#批次大小並計算批次數
batch_size =
100n_batch = mnist.train.num_examples // batch_size
x = tf.
placeholder
(tf.float32,
[none,
784]
)y = tf.
placeholder
(tf.float32,
[none,10]
)#神經網路
#第一層
weights_l1 = tf.
variable
(tf.
zeros([
784,
300]))
biases_l1 = tf.
variable
(tf.
zeros([
300]))
wx_plus_b_l1 = tf.
matmul
(x,weights_l1)
+biases_l1
l1= tf.nn.
tanh
(wx_plus_b_l1)
#第二層
weights_l2 = tf.
variable
(tf.
zeros([
300,10]
))biases_l2 = tf.
variable
(tf.
zeros([
10]))
wx_plus_b_l2 = tf.
matmul(l1
,weights_l2)
+biases_l2
prediction = tf.nn.
tanh
(wx_plus_b_l2)
loss = tf.
reduce_mean
(tf.
square
(y-prediction)
)train_step = tf.train.
gradientdescentoptimizer
(0.2).
minimize
(loss)
init = tf.
global_variables_initializer()
correct_prediction = tf.
equal
(tf.
argmax
(y,1
),tf.
argmax
(prediction,1)
)#argmax返回一維張量中最大的值所在位置。
#準確率
accuracy = tf.
reduce_mean
(tf.
cast
(correct_prediction,tf.float32)
)with tf.
session()
as sess:
sess.
run(init)
for epoch in
range(21
):for batch in
range
(n_batch)
: batch_xs,batch_ys = mnist.train.
next_batch
(batch_size)
sess.
run(train_step,feed_dict=
) acc = sess.
run(accuracy,feed_dict=
)print
("iter :"
+str
(epoch)
+",testing accuracy :"
+str
(acc)
)

weights_l1 = tf.

variable
(tf.
zeros([
784,
300]))
biases_l1 = tf.
variable
(tf.
zeros([
300]))
wx_plus_b_l1 = tf.
matmul
(x,weights_l1)
+biases_l1
l1= tf.nn.
tanh
(wx_plus_b_l1)
#第二層
weights_l2 = tf.
variable
(tf.
zeros([
300,10]
))biases_l2 = tf.
variable
(tf.
zeros([
10]))
wx_plus_b_l2 = tf.
matmul(l1
,weights_l2)
+biases_l2
prediction = tf.nn.
tanh
(wx_plus_b_l2)

如果改為

weights_l1 = tf.

variable
(tf.
truncated_normal([
784,
500]
, stddev=
0.1)

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