How can Tensorflow be used to visualize training results using Python?

TensorFlow training results can be effectively visualized using Python with the matplotlib library. This visualization helps identify training patterns, overfitting, and model performance trends during the training process.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

We will use the Keras Sequential API, which is helpful in building a sequential model that works with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.

A neural network that contains at least one convolutional layer is known as a Convolutional Neural Network (CNN). We can use the Convolutional Neural Network to build learning models.

Setting Up the Environment

We are using Google Colaboratory to run the code. Google Colab provides free access to GPUs and requires zero configuration, built on top of Jupyter Notebook.

Required Imports

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

Visualizing Training Results

After training a model, you can extract the training history and visualize accuracy and loss metrics ?

print("Calculating the accuracy")
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

print("Calculating the loss")
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(epochs)
print("The results are being visualized")

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

Code credit − https://www.tensorflow.org/tutorials/images/classification

Output

Calculating the accuracy
Calculating the loss
The results are being visualized

Understanding the Results

• The plots show that training accuracy and validation accuracy are not in sync, indicating potential overfitting.

• The model achieves only about 60% accuracy on the validation dataset while training accuracy continues to improve.

• Training accuracy increases linearly over time, but validation accuracy plateaus around 60% during training.

• When training examples are limited, the model learns from noise or unwanted details, negatively impacting performance on new data.

• This overfitting prevents the model from generalizing well to new datasets.

Key Metrics to Monitor

• Training vs Validation Accuracy: Should follow similar trends without large gaps

• Training vs Validation Loss: Both should decrease together; divergence indicates overfitting

• Convergence: Metrics should stabilize as training progresses

Conclusion

Visualizing TensorFlow training results with matplotlib helps identify overfitting and model performance issues. Monitor both accuracy and loss curves to ensure your model generalizes well to new data and adjust training strategies accordingly.