Tensorflow 04__:tensorboard的官网教程

前言

这篇博文主要是依照官网，介绍各种类型的summary的使用，包括标量scalar类型的，图像类型的image，直方图累心的histogram．

代码

该代码主要是使用普通的神经网络对MNIST手写体数字进行分类识别．网络各层的神经元个数为：784-500-10．７８4代表输入神经元个数为784，隐层神经元个数为500，输出层为10，代表10个类别．

# coding=utf-8"""　tensorboard 的使用"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport sysimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataFLAGS = Nonedef train():    # 读取MNIST数据    mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, fake_data=FLAGS.fake_data)    sess = tf.InteractiveSession()    # 给下面的 tensor　加上词头; 【注：】with　不能改变其下变量的作用域    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_shape'):        image_shaped_input = tf.reshape(x, shape=[-1, 28, 28, 1])        # image类型的summary        tf.summary.image('input', image_shaped_input, 10)    # 网络参数初始化    def weight_variable(shape):        initial = tf.truncated_normal(shape=shape, stddev=0.1)        return tf.Variable(initial)    def bias_varibale(shape):        initial = tf.constant(0.1, shape=shape)        return tf.Variable(initial)    # 要写到summary的信息    def variable_summaries(var):        with tf.name_scope('summaries'):            mean = tf.reduce_mean(var)            # 标量型summary            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('max', tf.reduce_max(var))            tf.summary.scalar('min', tf.reduce_min(var))            # 直方图型summary，可以用来查看tensor的值的分布            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_varibale([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(labels=y_, logits=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(FLAGS.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)    # 合并所有类型的 summary    merged = tf.summary.merge_all()    train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)    test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')    tf.global_variables_initializer().run()    # 获取sess.run中需要的feed_dict    def feed_dict(train):        if train or FLAGS.fake_data:            xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)            k = FLAGS.dropout        else:            xs, ys = mnist.test.images, mnist.test.labels            k = 1.0        return {x: xs, y_: ys, keep_prob: k}    # 网络的训练和summary的写入    for i in range(FLAGS.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))                # train_writer.add_run_metadata(run_metadata, 'step%03d' % i)                train_writer.add_summary(summary, 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()def main(_):    # tensorflow 中的文件操作操作类tf.gfile    if tf.gfile.Exists(FLAGS.log_dir):        tf.gfile.DeleteRecursively(FLAGS.log_dir)    tf.gfile.MakeDirs(FLAGS.log_dir)    train()if __name__ == "__main__":    parser = argparse.ArgumentParser()    parser.add_argument('--fake_data', nargs='?', const=True, type=bool,                        default=False,                        help='If true, uses fake data for unit testing.')    parser.add_argument('--max_steps', type=int, default=1000,                        help='Number of steps to run trainer.')    parser.add_argument('--learning_rate', type=float, default=0.001,                        help='Initial learning rate')    parser.add_argument('--dropout', type=float, default=0.9,                        help='Keep probability for training dropout.')    parser.add_argument('--data_dir', type=str, default='MNIST_data',                        help='Directory for storing input data')    parser.add_argument('--log_dir', type=str, default='MNIST_Log',                        help='Summaries log directory')    FLAGS, unparsed = parser.parse_known_args()    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

代码中产生各种类型的summary的可视化结果如下：
标量类型：

图片类型：

图模型类型：

分布类型：

直方图类型：

注意事项

（１）对于tf.summary.image，其输入的图片必须是4-D的，[batch_size, height, width, channels]．

参考网址

https://www.tensorflow.org/get_started/summaries_and_tensorboard —tensorflow官网教程