Python tensorflow实现mnist手写数字识别示例【非卷积与卷积实现】
本文实例讲述了Pythontensorflow实现mnist手写数字识别。分享给大家供大家参考，具体如下：
非卷积实现
importtensorflowastf
fromtensorflow.examples.tutorials.mnistimportinput_data
data_path='F:\CNN\data\mnist'
mnist_data=input_data.read_data_sets(data_path,one_hot=True)#offlinedataset
x_data=tf.placeholder("float32",[None,784])#Nonemeanswecanimportanynumberofimages
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)#resetvaluestowrong
foriinrange(100000):
batch_xs,batch_ys=mnist_data.train.next_batch(50)
sess.run(train,feed_dict={x_data:batch_xs,y_data:batch_ys})
ifi%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}))
卷积实现
importtensorflowastf
fromtensorflow.examples.tutorials.mnistimportinput_data
data_path='F:\CNN\data\mnist'
mnist_data=input_data.read_data_sets(data_path,one_hot=True)#offlinedataset
x_data=tf.placeholder("float32",[None,784])#Nonemeanswecanimportanynumberofimages
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)
foriinrange(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})
ifi%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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