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 tf

import input_data

# number 1 to 10 data

mnist = 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 outputs

def 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 network

xs = tf.placeholder(tf.float32, [None, 784])  # 28x28

ys = tf.placeholder(tf.float32, [None, 10])

# add output layer

prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)

# the error between prediction and real data

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),

                                              reduction_indices=[1]))  # loss

train_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