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Python tensorflow實現(xiàn)mnist手寫數(shù)字識別示例【非卷積與卷積實現(xiàn)】

更新時間：2019年12月19日 08:40:16 作者：nudt_qxx

這篇文章主要介紹了Python tensorflow實現(xiàn)mnist手寫數(shù)字識別,結(jié)合實例形式分析了基于tensorflow模塊使用非卷積與卷積算法實現(xiàn)手寫數(shù)字識別的具體操作技巧,需要的朋友可以參考下

本文實例講述了Python tensorflow實現(xiàn)mnist手寫數(shù)字識別。分享給大家供大家參考，具體如下：

非卷積實現(xiàn)

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
data_path = 'F:\CNN\data\mnist'
mnist_data = input_data.read_data_sets(data_path,one_hot=True) #offline dataset
x_data = tf.placeholder("float32", [None, 784]) # None means we can import any number of images
weight = tf.Variable(tf.ones([784,10]))
bias = tf.Variable(tf.ones([10]))
Y_model = tf.nn.softmax(tf.matmul(x_data ,weight) + bias)
#Y_model = tf.nn.sigmoid(tf.matmul(x_data ,weight) + bias)
'''
weight1 = tf.Variable(tf.ones([784,256]))
bias1 = tf.Variable(tf.ones([256]))
Y_model1 = tf.nn.softmax(tf.matmul(x_data ,weight1) + bias1)
weight1 = tf.Variable(tf.ones([256,10]))
bias1 = tf.Variable(tf.ones([10]))
Y_model = tf.nn.softmax(tf.matmul(Y_model1 ,weight1) + bias1)
'''
y_data = tf.placeholder("float32", [None, 10])
loss = tf.reduce_sum(tf.pow((y_data - Y_model), 2 ))#92%-93%
#loss = tf.reduce_sum(tf.square(y_data - Y_model)) #90%-91%
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(100000):
  batch_xs, batch_ys = mnist_data.train.next_batch(50)
  sess.run(train, feed_dict = {x_data: batch_xs, y_data: batch_ys})
  if i%50==0:
    correct_predict = tf.equal(tf.arg_max(Y_model,1),tf.argmax(y_data,1))
    accurate = tf.reduce_mean(tf.cast(correct_predict,"float"))
    print(sess.run(accurate,feed_dict={x_data:mnist_data.test.images,y_data:mnist_data.test.labels}))

卷積實現(xiàn)

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
data_path = 'F:\CNN\data\mnist'
mnist_data = input_data.read_data_sets(data_path,one_hot=True) #offline dataset
x_data = tf.placeholder("float32", [None, 784]) # None means we can import any number of images
x_image = tf.reshape(x_data, [-1,28,28,1])
w_conv = tf.Variable(tf.ones([5,5,1,32])) #weight
b_conv = tf.Variable(tf.ones([32]))    #bias
h_conv = tf.nn.relu(tf.nn.conv2d(x_image , w_conv,strides=[1,1,1,1],padding='SAME')+ b_conv)
h_pool = tf.nn.max_pool(h_conv,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
w_fc = tf.Variable(tf.ones([14*14*32,1024]))
b_fc = tf.Variable(tf.ones([1024]))
h_pool_flat = tf.reshape(h_pool,[-1,14*14*32])
h_fc = tf.nn.relu(tf.matmul(h_pool_flat,w_fc) +b_fc)
W_fc = w_fc = tf.Variable(tf.ones([1024,10]))
B_fc = tf.Variable(tf.ones([10]))
Y_model = tf.nn.softmax(tf.matmul(h_fc,W_fc) +B_fc)
y_data = tf.placeholder("float32",[None,10])
loss = -tf.reduce_sum(y_data * tf.log(Y_model))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
  batch_xs,batch_ys =mnist_data.train.next_batch(5)
  sess.run(train_step,feed_dict={x_data:batch_xs,y_data:batch_ys})
  if i%50==0:
    correct_prediction = tf.equal(tf.argmax(Y_model,1),tf.argmax(y_data,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
    print(sess.run(accuracy,feed_dict={x_data:mnist_data.test.images,y_data:mnist_data.test.labels}))

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