Python tensorflow实现mnist手写数字识别示例【非卷积与卷积实现】

Python  /  管理员 发布于 7年前   209

本文实例讲述了Python tensorflow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

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datadata_path = 'F:\CNN\data\mnist'mnist_data = input_data.read_data_sets(data_path,one_hot=True) #offline datasetx_data = tf.placeholder("float32", [None, 784]) # None means we can import any number of imagesweight = 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 wrongfor 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}))

卷积实现

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datadata_path = 'F:\CNN\data\mnist'mnist_data = input_data.read_data_sets(data_path,one_hot=True) #offline datasetx_data = tf.placeholder("float32", [None, 784]) # None means we can import any number of imagesx_image = tf.reshape(x_data, [-1,28,28,1])w_conv = tf.Variable(tf.ones([5,5,1,32])) #weightb_conv = tf.Variable(tf.ones([32]))    #biash_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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希望本文所述对大家Python程序设计有所帮助。