使用tensorboard可视化loss和acc的实例

try:
 image_summary = tf.image_summary
 scalar_summary = tf.scalar_summary
 histogram_summary = tf.histogram_summary
 merge_summary = tf.merge_summary
 SummaryWriter = tf.train.SummaryWriter
except:
 image_summary = tf.summary.image
 scalar_summary = tf.summary.scalar
 histogram_summary = tf.summary.histogram
 merge_summary = tf.summary.merge
 SummaryWriter = tf.summary.FileWriter

with tf.name_scope('loss'):
 loss = -tf.reduce_sum(y * tf.log(y_conv))
 loss_summary = scalar_summary('loss', loss)
 
with tf.name_scope('accuracy'):
 accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
 acc_summary = scalar_summary('accuracy', accuracy)

merged = merge_summary([loss_summary, acc_summary])

writer = SummaryWriter('save-cnn20/logs', sess.graph)

summary, _ = sess.run([merged, train_step], feed_dict={x:x_batch, y:y_batch})
counter += 1
writer.add_summary(summary, counter)

cd save
tensorboard --logdir=logs