How to visualize a tensor summary in tensorboard

Question

I'm trying to visualize a tensor summary in tensorboard. However I can't see the tensor summary at all in the board. Here is my code:

        out = tf.strided_slice(logits, begin=[self.args.uttWindowSize-1, 0], end=[-self.args.uttWindowSize+1, self.args.numClasses],
                               strides=[1, 1], name='softmax_truncated')
        tf.summary.tensor_summary('softmax_input', out)

where out is a multi-dimensional tensor. I guess there must be something wrong with my code. Probably I used the tensor_summary function incorrectly.

Answer 1

What you do is you create a summary op, but you don't invoke it and don't write the summary (see documentation). To actually create a summary you need to do the following:

# Create a summary operation
summary_op = tf.summary.tensor_summary('softmax_input', out)

# Create the summary
summary_str = sess.run(summary_op)

# Create a summary writer
writer = tf.train.SummaryWriter(...)

# Write the summary
writer.add_summary(summary_str)

Explicitly writing a summary (last two lines) is only necessary if you don't have a higher level helper like a Supervisor. Otherwise you invoke

sv.summary_computed(sess, summary_str)

and the Supervisor will handle it.

More info, also see: How to manually create a tf.Summary()

Answer 2

Hopefully a workaround which achieves what you want. ..

If you wish to view the tensor values, you can convert them using as_string, then use summary.text. The values will appear in the tensorboard text tab.

Not tried with 3D tensors, but feel free to slice according to needs.

code snippet, which includes use of inserting a print statement to get console output as well.

predictions = tf.argmax(reshaped_logits, 1)
txtPredictions = tf.Print(tf.as_string(predictions),[tf.as_string(predictions)], message='predictions', name='txtPredictions')
txtPredictions_op = tf.summary.text('predictions', txtPredictions)

Answer 3

Not sure whether this is kinda obvious, but you could use something like

def make_tensor_summary(tensor, name='defaultTensorName'):
    for i in range(tensor.get_shape()[0]:
        for j in range(tensor.get_shape()[1]:
            tf.summary.scalar(Name + str(i) + '_' + str(j), tensor[i, j])

in case you know it is a 'matrix-shaped' Tensor in advance.