我的TensorBoard图表将我的TensorFlow代码的连续运行视为同一运行的一部分。例如，如果我首先运行我的代码（如下所示），其中FLAGS.epochs == 10，然后重新运行它，设置FLAGS.epochs == 40，我会得到
这会在第一次运行结束时“循环回”开始第二次运行。
有没有办法将我的代码的多次运行视为不同的日志，例如可以进行比较或单独查看？
from __future__ import (absolute_import, print_function, division, unicode_literals)import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# Basic model parameters as external flags.flags = tf.app.flagsFLAGS = flags.FLAGSflags.DEFINE_float('epochs', 40, 'Epochs to run')flags.DEFINE_integer('mb_size', 40, 'Mini-batch size. Must divide evenly into the dataset sizes.')flags.DEFINE_float('learning_rate', 0.15, 'Initial learning rate.')flags.DEFINE_float('regularization_weight', 0.1 / 1000, 'Regularization lambda.')flags.DEFINE_string('data_dir', './data', 'Directory to hold training and test data.')flags.DEFINE_string('train_dir', './_tmp/train', 'Directory to log training (and the network def).')flags.DEFINE_string('test_dir', './_tmp/test', 'Directory to log testing.')def variable_summaries(var, name): with tf.name_scope("summaries"): mean = tf.reduce_mean(var) tf.scalar_summary('mean/' + name, mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean))) tf.scalar_summary('sttdev/' + name, stddev) tf.scalar_summary('max/' + name, tf.reduce_max(var)) tf.scalar_summary('min/' + name, tf.reduce_min(var)) tf.histogram_summary(name, var)def nn_layer(input_tensor, input_dim, output_dim, neuron_fn, layer_name): with tf.name_scope(layer_name): # This Variable will hold the state of the weights for the layer with tf.name_scope("weights"): weights = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1)) variable_summaries(weights, layer_name + '/weights') with tf.name_scope("biases"): biases = tf.Variable(tf.constant(0.1, shape=[output_dim])) variable_summaries(biases, layer_name + '/biases') with tf.name_scope('activations'): with tf.name_scope('weighted_inputs'): weighted_inputs = tf.matmul(input_tensor, weights) + biases tf.histogram_summary(layer_name + '/weighted_inputs', weighted_inputs) output = neuron_fn(weighted_inputs) tf.histogram_summary(layer_name + '/output', output) return output, weights # Collect datamnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)# Inputs and outputsx = tf.placeholder(tf.float32, shape=[None, 784])y_ = tf.placeholder(tf.float32, shape=[None, 10])# Network structureo1, W1 = nn_layer(x, 784, 30, tf.nn.sigmoid, 'hidden_layer')y, W2 = nn_layer(o1, 30, 10, tf.nn.softmax, 'output_layer')with tf.name_scope('accuracy'): with tf.name_scope('loss'): cost = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) loss = cost + FLAGS.regularization_weight * (tf.nn.l2_loss(W1) + tf.nn.l2_loss(W2)) 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.scalar_summary('accuracy', accuracy) tf.scalar_summary('loss', loss)train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(loss)# Loggingtrain_writer = tf.train.SummaryWriter(FLAGS.train_dir, tf.get_default_graph())test_writer = tf.train.SummaryWriter(FLAGS.test_dir)merged = tf.merge_all_summaries()with tf.Session() as sess: sess.run(tf.initialize_all_variables()) for ep in range(FLAGS.epochs): for mb in range(int(len(mnist.train.images)/FLAGS.mb_size)): batch_xs, batch_ys = mnist.train.next_batch(FLAGS.mb_size) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) summary = sess.run(merged, feed_dict={x: mnist.test.images, y_: mnist.test.labels}) test_writer.add_summary(summary, ep+1)回答：
from fs.osfs import OSFSfolder = OSFS(FLAGS.test_dir)test_n = len(list(n for n in folder.listdir() if n.startswith('test')))this_test = FLAGS.test_dir+"/test" + str(test_n+1)test_writer = tf.train.SummaryWriter(this_test)你可以使用类似这样的代码来对你的运行进行编号。
