TensorFlow 1.x Deep Learning Cookbook

How to do it...

1. The first step in using TensorBoard is to identify which OPs summaries you would like to have. In the case of DNNs, it is customary to know how the loss term (objective function) varies with time. In the case of Adaptive learning rate, the learning rate itself varies with time. We can get the summary of the term we require with the help of   tf.summary.scalar OPs. Suppose, the variable loss defines the error term and we want to know how it varies with time, then we can do this as follows:

loss = tf... tf.summary.scalar('loss', loss) 

2. You can also visualize the distribution of gradients, weights, or even output of a particular layer using tf.summary.histogram:

output_tensor  = tf.matmul(input_tensor, weights) + biases