【Deep Learning】Tensorflow MNIST测试

MNIST数据集下载

下载地址：http://yann.lecun.com/exdb/mnist/

新建测试代码

    import tensorflow.examples.tutorials.mnist.input_data as input_data
    import tensorflow as tf

    mnist = input_data.read_data_sets('E:DLdata', one_hot=True)
    sess = tf.InteractiveSession()
    x = tf.placeholder("float", shape=[None, 784])
    y_ = tf.placeholder("float", shape=[None, 10])
    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("float", [None,10])
    cross_entropy = -tf.reduce_sum(y_*tf.log(y))
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
    init = tf.initialize_all_variables()
    sess = tf.Session()
    sess.run(init)
    for i in range(10000):
        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, "float"))

    print("正确率",sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

    sess.close();

输出结果：正确率 0.9191

运行过程中查看GPU使用情况：

命令行输入：nvidia-smi