Tensorflow詳細は、名前空間のtensorboard上のノードを-P290-

いくつかの重要な知識を説明するために、

1、再利用のための（）tf.get_variableでは、変数名がある場合はそれと全く同じ意味、この変数の継続使用、変数名でない場合と全く同じ、その変数を作成
2を、オプション= run_options、run_metadata = run_metadata この悪いことでは
3、ARGMAXの精度を（覚えている）
4、精度三段階求めて：（1）ARGMAX（）（ 2）キャスト（）（3）reduce_meanを（）

次はmnist_inferenceの内容です

import tensorflow as tf

INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500


def get_weight_variable(shape, regularizer):
    weights = tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
    if regularizer != None: tf.add_to_collection('losses', regularizer(weights))
    return weights


def inference(input_tensor, regularizer):
    with tf.variable_scope('layer1'):

        weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)
        biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))
        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases)


    with tf.variable_scope('layer2'):
        weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)
        biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))
        layer2 = tf.matmul(layer1, weights) + biases

    return layer2

以下は、コンテンツの列車です

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference
from mnist_inference import inference


BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99


def train(mnist):
    #  输入数据的命名空间。
    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')

    with tf.variable_scope("layer"):
        regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
        y = mnist_inference.inference(x, regularizer)
        global_step = tf.Variable(0, trainable=False)

    # 处理滑动平均的命名空间。
    with tf.name_scope("moving_average"):
        variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
        variables_averages_op = variable_averages.apply(tf.trainable_variables())

    # 计算损失函数的命名空间。
    with tf.name_scope("loss_function"):
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
        cross_entropy_mean = tf.reduce_mean(cross_entropy)
        loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))

    with tf.name_scope("layer"):
        logits = inference(x, None)
        accuracy_op = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)), tf.float32))
    # 定义学习率、优化方法及每一轮执行训练的操作的命名空间。
    with tf.name_scope("train_step"):
        learning_rate = tf.train.exponential_decay(
            LEARNING_RATE_BASE,
            global_step,
            mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,
            staircase=True)

        train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

        with tf.control_dependencies([train_step, variables_averages_op]):
            train_op = tf.no_op(name='train')

    writer = tf.summary.FileWriter("log", tf.get_default_graph())  # 注意这个是写在前面的

    # 训练模型。
    with tf.Session() as sess:
        tf.global_variables_initializer().run()
        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)

            if i % 1000 == 0:
                # 配置运行时需要记录的信息。
                run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
                # 运行时记录运行信息的proto。
                run_metadata = tf.RunMetadata()
                _, loss_value, step = sess.run(
                    [train_op, loss, global_step], feed_dict={x: xs, y_: ys})
                    # options=run_options, run_metadata=run_metadata)  # 看这里，在运行[train_op, loss, global_step]的时候,后边配置
                # options = run_options, run_metadata = run_metadata
                accuracy = sess.run(accuracy_op, feed_dict={x: mnist.validation.images, y_: mnist.validation.labels})
                writer.add_run_metadata(run_metadata=run_metadata, tag=("tag%d" % i), global_step=i)
                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
                print("After %d training step(s), accuracy on validation batch is %g." % (step, accuracy))
            else:
                _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})

    writer.close()


def main(argv=None):
    mnist = input_data.read_data_sets("./MNIST_data", one_hot=True)
    train(mnist)


if __name__ == '__main__':
    main()

ここTensorboardの結果であります