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#自动下载并加载数据 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("mnist_data/", one_hot=true) #构建计算图 import tensorflow as tf x = tf.placeholder("float", [none, 784]) y_ = tf.placeholder("float", [none,10]) w = tf.variable(tf.zeros([784,10])) b = tf.variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x,w) + b) cross_entropy = -tf.reduce_sum(y_*tf.log(y)) train_step = tf.train.gradientdescentoptimizer(0.01).minimize(cross_entropy) #训练1000步 init = tf.initialize_all_variables() sess = tf.session() sess.run(init) for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) #验证准确率 correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print (sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
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