TensorFlow教程Softmax逻辑回归识别手写数字MNIST数据集
基于MNIST数据集的逻辑回归模型做十分类任务
没有隐含层的Softmax Regression只能直接从图像的像素点推断是哪个数字,而没有特征抽象的过程。多层神经网络依靠隐含层,则可以组合出高阶特征,比如横线、竖线、圆圈等,之后可以将这些高阶特征或者说组件再组合成数字,就能实现精准的匹配和分类。
import tensorflow as tf import numpy as np import input_data print('Download and Extract MNIST dataset') mnist = input_data.read_data_sets('data/', one_hot=True) # one_hot=True意思是编码格式为01编码 print("tpye of 'mnist' is %s" % (type(mnist))) print("number of train data is %d" % (mnist.train.num_examples)) print("number of test data is %d" % (mnist.test.num_examples)) trainimg = mnist.train.images trainlabel = mnist.train.labels testimg = mnist.test.images testlabel = mnist.test.labels print("MNIST loaded") """ print("type of 'trainimg' is %s" % (type(trainimg))) print("type of 'trainlabel' is %s" % (type(trainlabel))) print("type of 'testimg' is %s" % (type(testimg))) print("type of 'testlabel' is %s" % (type(testlabel))) print("------------------------------------------------") print("shape of 'trainimg' is %s" % (trainimg.shape,)) print("shape of 'trainlabel' is %s" % (trainlabel.shape,)) print("shape of 'testimg' is %s" % (testimg.shape,)) print("shape of 'testlabel' is %s" % (testlabel.shape,)) """ x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) # None is for infinite w = tf.Variable(tf.zeros([784, 10])) # 为了方便直接用0初始化,可以高斯初始化 b = tf.Variable(tf.zeros([10])) # 10分类的任务,10种label,所以只需要初始化10个b pred = tf.nn.softmax(tf.matmul(x, w) + b) # 前向传播的预测值 cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=[1])) # 交叉熵损失函数 optm = tf.train.GradientDescentOptimizer(0.01).minimize(cost) corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # tf.equal()对比预测值的索引和真实label的索引是否一样,一样返回True,不一样返回False accr = tf.reduce_mean(tf.cast(corr, tf.float32)) init = tf.global_variables_initializer() # 全局参数初始化器 training_epochs = 100 # 所有样本迭代100次 batch_size = 100 # 每进行一次迭代选择100个样本 display_step = 5 # SESSION sess = tf.Session() # 定义一个Session sess.run(init) # 在sess里run一下初始化操作 # MINI-BATCH LEARNING for epoch in range(training_epochs): # 每一个epoch进行循环 avg_cost = 0. # 刚开始损失值定义为0 num_batch = int(mnist.train.num_examples/batch_size) for i in range(num_batch): # 每一个batch进行选择 batch_xs, batch_ys = mnist.train.next_batch(batch_size) # 通过next_batch()就可以一个一个batch的拿数据, sess.run(optm, feed_dict={x: batch_xs, y: batch_ys}) # run一下用梯度下降进行求解,通过placeholder把x,y传进来 avg_cost += sess.run(cost, feed_dict={x: batch_xs, y:batch_ys})/num_batch # DISPLAY if epoch % display_step == 0: # display_step之前定义为5,这里每5个epoch打印一下 train_acc = sess.run(accr, feed_dict={x: batch_xs, y:batch_ys}) test_acc = sess.run(accr, feed_dict={x: mnist.test.images, y: mnist.test.labels}) print("Epoch: %03d/%03d cost: %.9f TRAIN ACCURACY: %.3f TEST ACCURACY: %.3f" % (epoch, training_epochs, avg_cost, train_acc, test_acc)) print("DONE")
迭代100次跑一下模型,最终,在测试集上可以达到92.2%的准确率,虽然还不错,但是还达不到实用的程度。手写数字的识别的主要应用场景是识别银行支票,如果准确率不够高,可能会引起严重的后果。
Epoch: 095/100 loss: 0.283259882 train_acc: 0.940 test_acc: 0.922
插一些知识点,关于tensorflow中一些函数的用法
sess = tf.InteractiveSession() arr = np.array([[31, 23, 4, 24, 27, 34], [18, 3, 25, 0, 6, 35], [28, 14, 33, 22, 30, 8], [13, 30, 21, 19, 7, 9], [16, 1, 26, 32, 2, 29], [17, 12, 5, 11, 10, 15]])
在tensorflow中打印要用.eval() tf.rank(arr).eval() # 打印矩阵arr的维度 tf.shape(arr).eval() # 打印矩阵arr的大小 tf.argmax(arr, 0).eval() # 打印最大值的索引,参数0为按列求索引,1为按行求索引
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