使用卷積神經網路實現手寫體數字識別，在過程中使用了不同的卷積層個數及全連線層的長度，在組建模型的過程中發現卷積層中的影象深度可以調節，在全連線層中，全連線層的長度可以自由定義。例如：對於乙個捲及神經網路模型，
| conv1-5x5x32 | max_pool1-2x2 |
|conv2-5x5x64|max_pool2-2x2|
| fc1-512 | fc2-10 |
對此模型，我們可以進行更改，將此模型改為
#coding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("mnist_data",one_hot=true)
x=tf.placeholder(tf.float32,shape=[none,784])
y_=tf.placeholder(tf.float32,shape=[none,10])
x_image=tf.reshape(x,[-1,28,28,1])
#定義權重
def weight_variable(shape):
initializer=tf.truncated_normal(shape,stddev=0.1)
return tf.variable(initializer)
def biase_variable(shape):
initializer=tf.constant(0.1,shape=shape)
return tf.variable(initializer)
def conv2d(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([5,5,1,16])
b_conv1=biase_variable([16])
h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1)
#第一層池化
h_pool1=max_pool_2x2(h_conv1)
#第二層卷積
w_conv2=weight_variable([5,5,16,64])
b_conv2=weight_variable([64])
h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2)
#第三層卷積
w_conv3=weight_variable([5,5,64,128])
b_conv3=weight_variable([128])
h_conv3=tf.nn.relu(conv2d(h_conv2,w_conv3)+b_conv3)
#第二層池化
h_pool2=max_pool_2x2(h_conv3)
#第一層全連線
w_fc1=weight_variable([7*7*128,3000])
b_fc1=biase_variable([3000])
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*128])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,w_fc1)+b_fc1)
keep_prob=tf.placeholder(tf.float32)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)
#第二層全連線層
w_fc2=weight_variable([3000,1024])
b_fc2=biase_variable([1024])
y_conv2=tf.matmul(h_fc1_drop,w_fc2)+b_fc2
#第三層全連線層
w_fc3=weight_variable([1024,10])
b_fc3=weight_variable([10])
y_conv=tf.matmul(y_conv2,w_fc3)+b_fc3
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y_conv))
train_step=tf.train.adamoptimizer(1e-4).minimize(cross_entropy)
corrent_validation=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
accuray=tf.reduce_mean(tf.cast(corrent_validation,dtype=tf.float32))
sess=tf.interactivesession()
sess.run(tf.global_variables_initializer())
for i in range(2000):
batch=mnist.train.next_batch(50)
if i %100==0:
train_accurcy=accuray.eval(feed_dict=)
print("step %d,training accuracy is %g"%(i,train_accurcy))
train_step.run(feed_dict=)
print("test accurcy %g"%accuray.eval(feed_dict=))
step 1400,training accuracy is 0.96
step 1500,training accuracy is 0.96
step 1600,training accuracy is 0.96
step 1700,training accuracy is 0.98
step 1800,training accuracy is 0.96
step 1900,training accuracy is 1
2018-10-16 17:00:04.703214:#########
2018-10-16 17:00:05.853831: ########
2018-10-16 17:00:07.134570: ########
test accurcy 0.957
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