Learn TensorBoard Visualization Step by Step | Beginner’s Guide

Discover how to effectively use TensorBoard visualization to understand and optimize your machine learning models. Step by step guide for beginners.

1. Build the Graph

Starting from the Input Layer

In this section, we set up the input layer by defining placeholders for input data (xs and ys). These placeholders will be used to feed the input features and labels during the training process.

# Include xs and ys to form a large layer named 'inputs'
with tf.name_scope('inputs'):
    # Name xs as x_input
    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
    # Name ys as y_input
    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')

Neural Layer Function

Next, we define a function (add_layer) to add a neural layer to our model. This function creates a layer with weights, biases, and an activation function if specified.

# Define a function to add a neural layer
def add_layer(inputs, in_size, out_size, activation_function=None):
    # Define the framework named 'layer'
    with tf.name_scope('layers'):
        # Define Weights in the framework; specify name W…