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。
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import tensorflow as tfimport numpy as npimport input_dataprint('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.imagestrainlabel = mnist.train.labelstestimg = mnist.test.imagestestlabel = mnist.test.labelsprint("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 infinitew = 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bpred = 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Falseaccr = 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# SESSIONsess = tf.Session() # 定义一个Sessionsess.run(init) # 在sess里run一下初始化操作# MINI-BATCH LEARNINGfor 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。
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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
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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]]) |
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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 |
以上就是TensorFlow教程Softmax逻辑回归识别手写数字MNIST数据集的详细内容，更多关于Softmax逻辑回归MNIST数据集手写识别的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/lwplwf/article/details/60603746
