想知道这段代码具体表示什么意思 有点看不懂 大神最好给个注释 谢谢了

Question

import tensorflow as tfimport input_data# number 1 to 10 datamnist = input_data.read_data_sets('MNIST_data/', one_hot=True)def add_layer(inputs, in_size, out_size, activation_function=None, ):    # add one more layer and return the output of this layer    Weights = tf.Variable(tf.random_normal([in_size, out_size]))    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, )    Wx_plus_b = tf.matmul(inputs, Weights) + biases    if activation_function is None:        outputs = Wx_plus_b    else:        outputs = activation_function(Wx_plus_b, )    return outputsdef compute_accuracy(v_xs, v_ys):    global prediction    y_pre = sess.run(prediction, feed_dict={xs: v_xs})    correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})    return result# define placeholder for inputs to networkxs = tf.placeholder(tf.float32, [None, 784])  # 28x28ys = tf.placeholder(tf.float32, [None, 10])# add output layerprediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)# the error between prediction and real datacross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),                                              reduction_indices=[1]))  # losstrain_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)sess = tf.Session()init = tf.global_variables_initializer()sess.run(init)for i in range(1000):    batch_xs, batch_ys = mnist.train.next_batch(100)    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})    if i % 50 == 0:        print(compute_accuracy(            mnist.test.images, mnist.test.labels))

Answer 1

这个要全部解释比较多，前面代码主要是读取mnist数据集，然后经过训练，计算出图片的准确率和标签，可以多看看TensorFlow的文档