How to Evaluate Machine Learning Models using TensorBoard with TensorFlow

Machine learning is an iterative process. You start by training a model on your data, evaluating its performance, and then tweaking and tuning until you arrive at something that meets your objectives. Evaluation is a crucial phase of this workflow – without measuring how well your models are doing, you‘d be flying blind, unable to identify issues and make informed improvements.

Fortunately, there are powerful tools available to help shine a light on model performance. In the world of TensorFlow, one of the most widely used is TensorBoard. Let‘s take a deep dive into what TensorBoard is, how it works, and walk through an example of evaluating and improving a model with it.

What is TensorBoard?

TensorBoard is a web-based visualization toolkit that comes packaged with TensorFlow. Its main goal is to provide measurements and visualizations needed during the machine learning workflow. It allows you to track and visualize metrics like loss and accuracy, view histograms of weights and biases, inspect the computational graph, and much more.

One of the standout features of TensorBoard is its ability to visually represent complex concepts like high-dimensional embeddings and model architectures. This allows you to not only see the raw numbers, but to really introspect your models and understand what‘s going on under the hood.

Setting Up TensorBoard

Before we jump into an example, let‘s cover the prerequisites and setup process for using TensorBoard. Firstly, you‘ll need to have TensorFlow installed (version 1.15 or higher). If you‘re using a standard TensorFlow installation, TensorBoard will already be included. You can verify your TensorFlow version with:

import tensorflow as tf
print(tf.version)

Next, we need to decide where TensorBoard will write log files to. It‘s considered best practice to have a dedicated logs directory for each experiment. We can use Python‘s os module to create one:

import os
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
os.makedirs(log_dir)

The strftime portion generates a unique subdirectory name based on the current timestamp. This is handy for organizing multiple runs.

Creating and Training a Model

Now that we‘ve set up TensorBoard, let‘s create a model to evaluate. We‘ll use the classic MNIST dataset of handwritten digits. Our goal will be to train a model to correctly classify images of digits 0-9.

First, we load and preprocess the data:

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

Dividing by 255 normalizes the pixel values from [0, 255] to [0, 1], which tends to make training converge faster.

Next, we define a simple feedforward neural network using the Keras Sequential API:

def create_model():
return tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation=‘relu‘),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=‘softmax‘)
])

Our model architecture consists of:

1. A Flatten layer to convert the 2D pixel array to a 1D feature vector
2. A Dense layer with 128 units and ReLU activation
3. A 20% Dropout layer for regularization
4. A final Dense layer with 10 units and softmax activation to output class probabilities

We instantiate the model and compile it with categorical cross-entropy loss and Adam optimization:

model = create_model()
model.compile(optimizer=‘adam‘,
loss=‘sparse_categorical_crossentropy‘,
metrics=[‘accuracy‘])

Finally, we‘re ready to train the model. We‘ll use the TensorBoard callback to log metrics during training:

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

model.fit(x=x_train,
y=y_train,
epochs=5,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])

After 5 epochs, we get a respectable validation accuracy of around 97%. But we‘re not done yet – let‘s see how we can use TensorBoard to evaluate our model further.

Evaluating with TensorBoard

To fire up TensorBoard, we simply point it to the log directory we specified during training:

%tensorboard –logdir logs/fit

This will spin up the TensorBoard server and provide a link to open it in a new browser tab. Once TensorBoard loads, we‘re greeted with an overview dashboard containing high-level information and system metrics.

Let‘s start diving into model evaluation by clicking the Scalars tab. Here we can see graph plots of our training metrics over time, including loss and accuracy for both the training and validation sets.

Right away, a few things jump out. Our training loss is consistently decreasing with each epoch, which is a good sign that the model is converging. The training and validation accuracy are also steadily rising, indicating that the model is generalizing well to new data.

However, we can spot a small gap between training and validation accuracy, with training around 1-2% higher by the final epoch. This could be a hint of slight overfitting – the model is performing better on data it‘s seen before compared to completely new samples.

To get a more granular view, we can click on any of the individual scalars to zoom in and get exact point values. We can also adjust chart settings to compare multiple runs, change the smoothing, and more.

Next, let‘s check out the Graphs tab to visualize our model architecture. Here we can see the complete dataflow graph of our Keras Sequential model, with each layer represented as a node.

We can pan and zoom around the graph, as well as click on individual nodes to inspect their details like input and output shapes, number of parameters, and more. This is incredibly useful for debugging and understanding how data flows through your model.

But TensorBoard offers even more ways to introspect your model. Clicking on the Distributions and Histograms tabs, we can view the distribution of weights, biases, and activations for each layer.

The visualizations update in real-time as the model trains, allowing you to monitor things like whether a layer‘s weights are diverging or if activations are saturating. These can offer clues for how to tweak your architecture and hyperparameters.

Improving the Model

Armed with insights from TensorBoard, how can we go about improving our MNIST model? Let‘s consider a few options:

1. Adjusting hyperparameters: Based on our scalars plots, we saw potential signs of overfitting, with validation performance lagging behind training. One thing to try would be increasing the Dropout rate to add more regularization. We could also experiment with lowering the learning rate or using learning rate decay.

2. Modifying architecture: From our graphs visualization, we can see that our model is quite simple, with only 2 Dense layers. There could be room to make it more expressive by adding width (more units) or depth (more layers). However, we‘d want to be cautious about making the model too large and complex relative to the dataset size.

3. Expanding the dataset: Another avenue to explore would be augmenting our training data. Techniques like random rotations, shifts, and zooms can synthetically boost the number of examples the model sees and help combat overfitting. We‘d then retrain and evaluate in TensorBoard to analyze the impact.

Let‘s give one of these a shot. We‘ll increase the Dropout rate from 20% to 50% and train for 5 more epochs:

model = create_model()
model.compile(optimizer=‘adam‘,
loss=‘sparse_categorical_crossentropy‘,
metrics=[‘accuracy‘])

model.fit(x=x_train,
y=y_train,
epochs=10,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])

Pulling up TensorBoard again, we can compare our two runs side-by-side. Lo and behold, cranking up the regularization helped close the accuracy gap between train and validation!

We‘ve successfully used TensorBoard to evaluate our model, derive insights, and make a targeted improvement. And we‘re just scratching the surface of what‘s possible – further experimentation could yield even better results.

TensorBoard vs. Other Tools

TensorBoard is undoubtedly a powerful toolkit, but it‘s not the only option for model evaluation and inspection. Another popular tool in the TensorFlow ecosystem is TensorFlow Model Analysis (TFMA).

While TensorBoard and TFMA have some overlap in functionality, they are designed for different stages of the ML workflow. TensorBoard is primarily used during model development to track training progress and evaluate experiments. TFMA, on the other hand, is geared towards evaluating production models on large datasets and slicing performance across different segments.

So in practice, you would likely use both tools – TensorBoard while iterating on models, then TFMA for a more rigorous validation before pushing to production. They complement each other to provide a complete evaluation pipeline.

Conclusion and Next Steps

In this post, we‘ve taken a whirlwind tour of evaluating machine learning models with TensorBoard and TensorFlow. We walked through training a model on the MNIST dataset, visualizing its performance in TensorBoard, deriving insights, and making an improvement.

Some key takeaways:

• Evaluation is a critical phase of the ML workflow for measuring model quality and driving iterations
• TensorBoard provides rich visualizations of model metrics, architecture, weights, activations, and more
• Comparing train vs. validation performance and monitoring distributions can reveal problems like overfitting
• Insights from TensorBoard can inform targeted model improvements to hyperparameters, architecture, and data

If you‘re new to machine learning, I encourage you to try out TensorBoard on your own models. Experiment with different architectures and hyperparameters, and see how the evaluation insights guide your iterations.

For more experienced practitioners, consider integrating TensorBoard into your workflows for increased model transparency and reproducibility. Dive deeper into advanced features like embedding visualization and image summaries.

No matter your level, adopting a tool like TensorBoard is a major step towards more systematic, informed model evaluation. Your models (and users) will thank you for it!