其中
summary.image生成新的图像用于验证输入结果的准确与否
summary.scalar用于记录准确率、损失等信息
得到的结果需要进行 merged = tf.summary.merge_all()
最后要sess.run([train_step,merged])

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
max_steps = 1000
learning_rate = 0.001
dropout = 0.9
data_dir = ""
log_dir = "C:/Users/dongfanker/Desktop/log"
mnist = input_data.read_data_sets(data_dir, one_hot=True)
sess = tf.InteractiveSession()
with tf.name_scope("input"):
    x = tf.placeholder(tf.float32, [None, 784], name="x-input")
    y_ = tf.placeholder(tf.float32, [None, 10], name="y-input")
with tf.name_scope("input_reshape"):
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image("input", image_shaped_input, 10)
# 创建初始化参数的方法


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def variable_summaries(var):
    with tf.name_scope("summaries"):
        mean = tf.reduce_mean(var)
        tf.summary.scalar("mean", mean)
        with tf.name_scope("stddev"):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar("stddev", stddev)
    tf.summary.scalar("stddev", stddev)
    tf.summary.scalar("max", tf.reduce_max(var))
    tf.summary.scalar("min", tf.reduce_min(var))
    tf.summary.histogram("histogram", var)


def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
    with tf.name_scope(layer_name):
        with tf.name_scope("weights"):
            weights = weight_variable([input_dim, output_dim])
            variable_summaries(weights)
        with tf.name_scope("biases"):
            biases = bias_variable([output_dim])
            variable_summaries(biases)
        with tf.name_scope("linear_compute"):
            preactivate = tf.matmul(input_tensor, weights) + biases
            tf.summary.histogram("pre_activation", preactivate)
        activations = act(preactivate, name="activation")
        tf.summary.histogram("activation", activations)
        return activations


hidden1 = nn_layer(x, 784, 500, "layer1")

with tf.name_scope("dropout"):
    keep_prob = tf.placeholder(tf.float32)
    tf.summary.scalar("dropout_keep_probability", keep_prob)
    dropped = tf.nn.dropout(hidden1, keep_prob)
y = nn_layer(dropped, 500, 10, "layer2", act=tf.identity)

with tf.name_scope("cross_entropy"):
    diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
    with tf.name_scope("total"):
        cross_entropy = tf.reduce_mean(diff)
tf.summary.scalar("loss", cross_entropy)

with tf.name_scope("train"):
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)

with tf.name_scope("accuracy"):
    with tf.name_scope("correct_prediction"):
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    with tf.name_scope("accuracy"):
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)

merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(log_dir + "/train", sess.graph)
test_writer = tf.summary.FileWriter(log_dir + "/test")
tf.global_variables_initializer().run()


def feed_dict(train):
    if train:
        xs, ys = mnist.train.next_batch(100)
        k = dropout
    else:
        xs, ys = mnist.test.images, mnist.test.labels
        k = 1.0
    return {x: xs, y_: ys, keep_prob: k}


saver = tf.train.Saver()
for i in range(max_steps):
    if i % 10 == 0:
        summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
        test_writer.add_summary(summary, i)
        print("Accuracy at step %s: %s" % (i, acc))
    else:
        if i % 100 == 99:
            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
            run_metadata = tf.RunMetadata()
            summary, _ = sess.run([merged, train_step],
                                  feed_dict=feed_dict(True),
                                  options=run_options,
                                  run_metadata=run_metadata)
            train_writer.add_run_metadata(run_metadata, "step%03d" % i)
            train_writer.add_summary(summary, i)
            saver.save(sess, log_dir + "/model.ckpt", i)
            print("Adding run metadata for ", i)
        else:
            summary, _ = sess.run([merged, train_step],
                                  feed_dict=feed_dict(True))
            train_writer.add_summary(summary, i)
train_writer.close()
test_writer.close()