TF之NN：利用DNN算法(SGD+softmax+cross_entropy)对mnist手写数字图片识别训练集(TF自带函数下载)实现87.4%识别
目录
输出结果
代码设计
输出结果
代码设计
import numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltfrom tensorflow.examples.tutorials.mnist import input_dataprint ("packs loaded")print ("Download and Extract MNIST dataset")mnist = input_data.read_data_sets('/tmp/data/', one_hot=True)printprint (" tpye of 'mnist' is %s" % (type(mnist)))print (" number of trian data is %d" % (mnist.train.num_examples))print (" number of test data is %d" % (mnist.test.num_examples))packs loadedDownload and Extract MNIST datasettpye of 'mnist' is <class 'tensorflow.contrib.learn.python.learn.datasets.base.Datasets'>number of trian data is 55000number of test data is 10000
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data ＃这是TensorFlow 为了教学Mnist而提前设计好的程序# number 1 to 10 datamnist = input_data.read_data_sets('MNIST_data', one_hot=True) #TensorFlow 会检测数据是否存在。当数据不存在时，系统会自动将数据下载到MNIST_data／文件夹中。当执行完语句后，读者可以自行前往MNIST_data／文件夹下查看上述4 个文件是否已经被正确地下载def add_layer(inputs, in_size, out_size, activation_function=None,):# add one more layer and return the output of this layerWeights = tf.Variable(tf.random_normal([in_size, out_size]))biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)Wx_plus_b = tf.matmul(inputs, Weights) + biasesif activation_function is None:outputs = Wx_plus_belse:outputs = activation_function(Wx_plus_b,)return outputsdef compute_accuracy(v_xs, v_ys):global prediction y_pre = sess.run(prediction, feed_dict={xs: v_xs}) correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys}) return result# define placeholder for inputs to networkxs = tf.placeholder(tf.float32, [None, 784]) ys = tf.placeholder(tf.float32, [None, 10]) # add output layerprediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)# the error between prediction and real datacross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1]))train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.Session()# important stepsess.run(tf.global_variables_initializer())for i in range(1000):batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})if i % 50 == 0:print(compute_accuracy(mnist.test.images, mnist.test.labels))
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TF之NN：(TF自带函数下载MNIST55000训练集图片)实现手写数字识别87.4%准确率识别：SGD法+softmax法+cross_entropy法
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TF之NN：(TF自带函数下载MNIST55000训练集图片)实现手写数字识别87.4%准确率识别：SGD法+softmax法+cross_entropy法
TF之DNN：(TF自带函数下载MNIST55000训练集图片)利用 784 个神经元的三层全连接的DNN对MNIST手写数字识别实现98%准确率
TF之NN：利用DNN算法(SGD+softmax+cross_entropy)对mnist手写数字图片识别训练集(TF自带函数下载)实现87.4%识别
