Section 1.1: Setting Up the Input Layer

In this section, we will establish the input layer by creating placeholders for our data inputs (denoted as xs and ys). These placeholders are crucial for feeding input features and labels throughout the training phase.

# Define xs and ys as part of a larger input layer called 'inputs'

with tf.name_scope('inputs'):

# Assign the name 'x_input' to xs

xs = tf.placeholder(tf.float32, [None, 1], name='x_input')

# Assign the name 'y_input' to ys

ys = tf.placeholder(tf.float32, [None, 1], name='y_input')

Section 1.2: Creating Neural Layers

Next, we will define a function (add_layer) that allows us to incorporate a neural layer into our model. This function will generate a layer complete with weights, biases, and an optional activation function.

# Function to add a neural layer

def add_layer(inputs, in_size, out_size, activation_function=None):

# Establish a framework named 'layer'

with tf.name_scope('layers'):

# Define weights and assign the name W

with tf.name_scope('weights'):

Weights = tf.Variable(tf.random_uniform([in_size, out_size]), name='W')

# Define biases

with tf.name_scope('biases'):

biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)

# Calculate Wx_plus_b

with tf.name_scope('Wx_plus_b'):

Wx_plus_b = tf.matmul(inputs, Weights) + biases

if activation_function is None:

outputs = Wx_plus_b

else:

outputs = activation_function(Wx_plus_b)

return outputs

Section 1.3: Defining Layers in the Network

We now utilize the add_layer function to create two layers within our neural network.

# Create the hidden layer

l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

# Create the output layer

prediction = add_layer(l1, 10, 1, activation_function=None)

Section 1.4: Loss Function Implementation

To train our model effectively, we need to establish a loss function. For this purpose, we will use the mean squared error.

# Calculate the loss

with tf.name_scope('loss'):

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))

Section 1.5: Training the Model

To enhance our model's performance, we will set up a training step using the gradient descent optimizer.

# Optimize the model

with tf.name_scope('train'):

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 0.1 is the learning rate

Section 1.6: Configuring TensorBoard

Before we begin training, we create a TensorFlow session and a FileWriter to save the graph for TensorBoard visualization.

sess = tf.Session()

writer = tf.summary.FileWriter("logs/", sess.graph)

To launch TensorBoard, navigate to the project's root directory in the PyCharm Terminal and execute:

tensorboard --logdir=logs

Then, open the provided link in Google Chrome!

Section 1.7: Adding Variable Summaries

Next, we will enhance the add_layer function to include variable summaries for weights and biases.

# Modify add_layer to include a layer number parameter

def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):

layer_name = 'layer%s' % n_layer

with tf.name_scope(layer_name):

# Weights summary

with tf.name_scope('weights'):

Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')

tf.summary.histogram(layer_name + '/weights', Weights)

# Biases summary

with tf.name_scope('biases'):

biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')

tf.summary.histogram(layer_name + '/biases', biases)

with tf.name_scope('Wx_plus_b'):

Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)

if activation_function is None:

outputs = Wx_plus_b

else:

outputs = activation_function(Wx_plus_b)

tf.summary.histogram(layer_name + '/outputs', outputs)

return outputs

Section 1.8: Modifying the Layers

With the new parameter added to add_layer, we will adjust our previous layer definitions accordingly.

# Hidden layer

l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)

# Output layer

prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)

Section 1.9: Loss Summary Setup

We will now set up a summary for the loss function to visualize it in TensorBoard.

# Loss summary

with tf.name_scope('loss'):

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))

tf.summary.scalar('loss', loss) # Compatible with TensorFlow >= 0.12

Section 1.10: Merging All Summaries

Now, we will merge all the training graphs for a comprehensive view.

# Optimize the model

with tf.name_scope('train'):

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 0.1 is the learning rate

# Initialize the session

sess = tf.Session()

merged = tf.summary.merge_all()

writer = tf.summary.FileWriter("logs/", sess.graph)

sess.run(tf.global_variables_initializer())

Section 2.1: Troubleshooting Visualization

If you encounter issues with visualization, enter 'localhost:your_port_number' in your browser. If no images are displayed, ensure that you have executed the command:

tensorboard --logdir=logs

Note: Do not include single quotes around 'logs' in this command.

Section 2.2: Complete Code Example

The complete code includes all the discussed elements and modifications for effective implementation.

The first video titled "Complete TensorBoard Guide - TensorFlow Tutorial 17" provides a thorough overview of TensorBoard features and its applications.

The second video titled "What is TensorBoard and how to launch it in a browser?" explains how to start TensorBoard and navigate its interface.