How to show training and predicted values on Tensorboard using python

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I have tried something like this: 

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer())

    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter('logs', sess.graph)
    for iteration in range(int(n_epochs*train_set_size/batch_size)):
        x_batch, y_batch = get_next_batch(batch_size) # fetch the next training batch 

        sess.run(training_op, feed_dict={X: x_batch, y: y_batch}) 

        if iteration % int(1*train_set_size/batch_size) == 0:
            mse_train = loss.eval(feed_dict={X: x_train, y: y_train}) 
            mse_valid = loss.eval(feed_dict={X: x_valid, y: y_valid}) 
            mse_test = loss.eval(feed_dict={X: x_test, y: y_test})
            y_train_pred,summary1,outimage = sess.run([outputs,merged,out_img_sum], feed_dict={X: x_train,y:y_train})
            y_valid_pred,summary2 = sess.run([outputs,merged], feed_dict={X: x_valid,y:y_valid})
            y_test_pred,summary3 = sess.run([outputs,merged], feed_dict={X: x_test,y:y_test})
            writer.add_summary(summary1, iteration*batch_size/train_set_size)

I am willing to show the y_train and y_train_pred values on the tensorboard. How I can do that? These are like the arrays and I am not getting a way out to show these values comparison on Tensorboard. Please help me.

tensorboard tensorflow python-3.x python
1 Answers

Update:

Yes, you can plot along x-axis. The reason why you get the wrong images on tensorboard is because int(iteration*float(batch_size)/train_set_size) always return the same value(0.0001804630682330861 according to you). I created a similar code below like your situation(since I don't have your data). And it works very well.

import tensorflow as tf
import numpy as np

summary_writer = tf.summary.FileWriter('/tmp/test')

for iteration in range(5):
    y_train_preds = np.random.rand(10)
    summary = tf.Summary()
    for idx, value in enumerate(y_train_preds):
        summary.value.add(tag='y_train', simple_value=value)
        summary_writer.add_summary(summary, iteration*len(y_train_preds)+idx)

summary_writer.close()

Output on tensorboard

enter image description here

The only point need to notice is that make sure global step in add_summary() should increase every time.

May be you can try the following: I've updated your code for you to try as well

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer())

    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter('logs', sess.graph)
    for iteration in range(int(n_epochs*train_set_size/batch_size)):
        x_batch, y_batch = get_next_batch(batch_size) # fetch the next training batch 

        sess.run(training_op, feed_dict={X: x_batch, y: y_batch}) 

        if iteration % int(1*train_set_size/batch_size) == 0:
            summary = tf.Summary()
            mse_train = loss.eval(feed_dict={X: x_train, y: y_train}) 
            mse_valid = loss.eval(feed_dict={X: x_valid, y: y_valid}) 
            mse_test = loss.eval(feed_dict={X: x_test, y: y_test})
            y_train_pred,summary1,outimage = sess.run([outputs,merged,out_img_sum], feed_dict={X: x_train,y:y_train})
            y_valid_pred,summary2 = sess.run([outputs,merged], feed_dict={X: x_valid,y:y_valid})
            y_test_pred,summary3 = sess.run([outputs,merged], feed_dict={X: x_test,y:y_test})
            for value in y_train:
                summary.value.add(tag='y_train', simple_value=value)
            for idx, value in enumerate(y_train_pred):
                summary.value.add(tag='y_train_pred', simple_value=value)
                writer.add_summary(summary, iteration*len(y_train_pred)+idx)
            writer.add_summary(summary1, int(iteration*float(batch_size)/train_set_size))

Reference post: tensorboard with numpy array

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