Why my tensorboard is showing a discontinued output? - python-3.x

Question

I am running a neural network with logging of training accuracy,Validation accuracy and validation loss. here is my code snippet.
def show_progress(epoch, feed_dict_train, feed_dict_validate, val_loss):
acc = session.run(accuracy, feed_dict=feed_dict_train)
val_acc = session.run(accuracy, feed_dict=feed_dict_validate)
msg = "Training Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}, Validation Loss: {3:.3f}"
print(msg.format(epoch + 1, acc, val_acc, val_loss))
return acc,val_acc
total_iterations = 0
#writer=tf.summary.FileWriter(options.tensorboard,session)
saver = tf.train.Saver()
def train(num_iteration):
global total_iterations
writer=tf.summary.FileWriter(options.tensorboard,session.graph)
#global writer
for i in range(total_iterations,
total_iterations + num_iteration):
x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size)
x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(batch_size)
feed_dict_tr = {x: x_batch,
y_true: y_true_batch}
feed_dict_val = {x: x_valid_batch,
y_true: y_valid_batch}
session.run(optimizer, feed_dict=feed_dict_tr)
if i % 10 == 0:
val_loss = session.run(cost, feed_dict=feed_dict_val)
epoch = int(i /10)
accu,valid_accu=show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss)
#getting values for visualising inside the tensorboard
tf.summary.scalar("training_accuracy",accu)
tf.summary.scalar("Validation_accuracy",valid_accu)
tf.summary.scalar("Validation_loss",val_loss)
#tf.summary.scalar("epoch",epoch)
#merging all the values (serializing)
merged=tf.summary.merge_all()
summary=session.run(merged)
#adding them to the events directory
writer.add_summary(summary,epoch)
saver.save(session, options.save)
total_iterations += num_iteration
train(num_iteration=10)
Now I am getting a tensor board output, as for each epoch the accuracy,validation accuracy and validation loss as separate plots with single points.
For each epoch I am getting these three plots again with another point.
I want to get a continuous points for these three plots so as it forms a line graph.

Answer 1

Each of your call to tf.summary.scalar() will create a node in the computation graph. Specifically, in your code, the calls are inside the training loop and therefore metrics at different epochs get written to different plots.
tf.summary.scalar("training_accuracy", accu)
tf.summary.scalar("Validation_accuracy", valid_accu)
tf.summary.scalar("Validation_loss", val_loss)
What you can do is to define the summary ops before the loop with placeholders. Then, in the eval loop, you can feed these tensor with real values.
# Define a placeholder and wire it to the summary op.
accu_tensor = tf.placeholder(tf.float32)
tf.summary.scalar("training_accuracy", accu_tensor)
summary_op = tf.summary.merge_all()
# Create a session after defining ops.
sess = tf.Session()
writer = tf.summary.FileWriter(<some-directory>, sess.graph)
for i in range(total_iterations,
total_iterations + num_iteration):
# run training ops to get values for accu
# ...
# run the summary op with a feed_dict to feed the value.
summaries = sess.run(summary_op, feed_dict={accu_tensor: accu})
writer.add_summary(summaries, epoch)