import tensorflow as tf
import numpy as np
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
mnist = read_data_sets(
"f:/python/mnist_data/"
, one_hot=
true
)x=tf.placeholder(tf.float32,
[none
,784])
y_ = tf.placeholder(
"float",[
none,10
])defweight_variable
(shape)
:
initial=tf.truncated_normal(shape=shape,stddev=
0.1)
return tf.variable(initial)
defbias_variable
(shape)
: initial = tf.constant(
0.1, shape=shape)
return tf.variable(initial)
defconv2d
(x, w)
:return tf.nn.conv2d(x, w, strides=[1
,1,1
,1], padding=
'same'
)def
max_pool_2x2
(x):
return tf.nn.max_pool(x, ksize=[1
,2,2
,1],strides=[1
,2,2
,1], padding=
'same'
)#第一層卷積
w_conv1 = weight_variable([5
,5,1
,32])
b_conv1 = bias_variable([32
])x_image = tf.reshape(x,[-
1,28,
28,1]
)h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1)
+ b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#第二層卷積
w_conv2 = weight_variable([5
,5,32
,64])
b_conv2 = bias_variable([64
])h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2)
+ b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
#全連線層
w_fc1 = weight_variable([7
*7*64
,1024])
b_fc1 = bias_variable(
[1024])
h_pool2_flat = tf.reshape(h_pool2,[-
1,7*
7*64]
)h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1)
+ b_fc1)
#dropout層取消一些神經元，防止過擬合
keep_prob = tf.placeholder(
"float"
)h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
#輸出層
w_fc2 = weight_variable(
[1024,10
])b_fc2 = bias_variable([10
])y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2)
+ b_fc2)
#訓練cross_entropy =
-tf.reduce_sum(y_*tf.log(y_conv)
)#使用adam優化器
train_step = tf.train.adamoptimizer(1e-
4).minimize(cross_entropy)
#計算準確率，equal比較大小，argmax返回最大值下標
correct_prediction = tf.equal(tf.argmax(y_conv,1)
, tf.argmax(y_,1)
)#轉換成浮點數並計算平均值
accuracy = tf.reduce_mean(tf.cast(correct_prediction,
"float"))
sess = tf.session(
)sess.run(tf.global_variables_initializer())
for i in
range
(2000):
#其中的n代表返回多少個訓練資料集和對應的標籤
batch = mnist.train.next_batch(50)
if i%
100==0:
#batch[0]:訓練，batc[1]:訓練標籤，餵給x,y_
train_accuracy = sess.run(accuracy, feed_dict=
)#列印訓練輪數和準確率
print
("step %d, training accuracy %g"
%(i, train_accuracy)
) sess.run(train_step,feed_dict=
)#測試資料
print
("test accuracy %g"
%sess.run(accuracy,feed_dict=
))
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