TF：利用是Softmax回归+GD算法实现MNIST手写数字图片识别(10000张图片测试得到的准确率为92%)
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设计思路
全部代码
设计思路
全部代码
#TF：利用是Softmax回归+GD算法实现手写数字识别(10000张图片测试得到的准确率为92%)#思路：对输入的图像，计算它属于每个类别的概率，找出最大概率即为预测值import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)#读入MNIST数据x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10]))y = tf.nn.softmax(tf.matmul(x, W) + b) y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y)))train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) sess = tf.InteractiveSession() tf.global_variables_initializer().run() print('start training...')for _ in range(1000):batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) # 0.9185
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TF：利用是Softmax回归+GD算法实现MNIST手写数字识别(10000张图片测试得到的准确率为92%)
