tensorboard实现tensorflow可视化

1.工程目录

2.data、input_data.py的导入

在tensorflow更新之后可以进行直接的input_data的导入

# from tensorflow.examples.tutorials.mnist import input_data

链接：https://pan.baidu.com/s/1EBNyNurBXWeJVyhNeVnmnA 
提取码：4nnl

3.神经网络训练算法tensorboard.py

import tensorflow as tf
import input_data

max_steps = 1000
learning_rate = 0.001
dropout = 0.9

log_dir = 'logs/'

mnist = input_data.read_data_sets('data', 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)

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.sqrt(var - mean)))
        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('Wx_plus_b'):
            preactivate = tf.matmul(input_tensor, weights) + biases
            tf.summary.histogram('pre_activations', preactivate)
        activations = act(preactivate, name='activation')
        tf.summary.histogram('activations', 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(logits=y, labels=y_)
    with tf.name_scope('total'):
        cross_entropy = tf.reduce_mean(diff)
tf.summary.scalar('cross entropy', cross_entropy)

with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
with tf.name_scope('accurecy'):
    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' % (1, 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))
            train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
            train_writer.add_summary(summary, 1)
            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()

4.在本地训练完成之后可以进行tensorboard可视化

在terminal窗口输入

tensorboard --logdir "tensorboard/logs" --port=8082

在服务器运行之后访问

http://localhost:8082

ps：可能遇到的问题：

1.浏览器在访问 localhost:8082 时显示空白：

　　产生原因：tensorflow版本过低

　　解决办法：更新tensorflow

pip3 install --upgrade tensorflow

　　　　更新中可能遇到的问题：在某个盘需要管理员权限

　　　　解决办法：打开此电脑-->右键属性（待更改属性的盘）-->安全-->(Users)编辑-->将修改打勾之后应用