How to get the loss values and accuracy of each training epoch using SageMaker

This answer is based on a question asked in Reditt: Here is a research article on how to get the loss values and accuracy of each training epoch using SageMaker Experiment Service and draw a chart for visualization:

Introduction

Amazon SageMaker Experiment Service is a managed service that helps you track, manage, and analyze your machine learning experiments. It provides a centralized place to store your experiment metadata, such as the hyperparameters you used, the metrics you collected, and the models you trained.

The benefit of using SageMaker Experiment Service is that it makes it easy to track the loss values and accuracy of each training epoch. This information can visualize the training progress and to identify any areas where the model may be underfitting or over-fitting.

Getting Started

To get started, you will need to create an Amazon SageMaker account and a SageMaker Experiment. You can do this by following the instructions in the SageMaker Experiment Service documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html.

Once you have created a SageMaker Experiment, you can start tracking your experiments. To do this, you will need to specify the hyperparameters you used, the metrics you collected, and the models you trained.

Tracking Loss and Accuracy

To track the loss values and accuracy of each training epoch, you can use the sagemaker.metrics.LoggingMetric class. This class provides a way to log metrics to a SageMaker Experiment.

To use the sagemaker.metrics.LoggingMetric class, you will need to create an instance of the class and specify the name of the metric and the value. For example, the following code logs the loss value of each training epoch to a SageMaker Experiment:

import sagemaker.metrics

metric = sagemaker.metrics.LoggingMetric(name="loss")
for epoch in range(epochs):
    # Train the model for one epoch
    loss = model.train()

    # Log the loss value
    metric.log(loss)

Visualizing the Training Progress

Once you have logged the loss values and accuracy of each training epoch, you can visualize the training progress using a chart. To do this, you can use the Matplotlib: https://matplotlib.org/ library.

The following code creates a chart that visualizes the training progress of a model:

import matplotlib.pyplot as plt
import numpy as np

# Load the loss values
loss_values = np.load("loss_values.npy")

# Create the chart
plt.plot(loss_values)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Training Progress")
plt.show()

This code will create a chart that shows the loss values for each training epoch. The chart will help you visualize the training progress and to identify any areas where the model may be underfitting or overfitting.

Conclusion

In this article, we have shown how to get the loss values and accuracy of each training epoch using SageMaker Experiment Service and draw a chart for visualization. This information can be used to track the training progress and to identify any areas where the model may be underfitting or overfitting.

Hi everyone, I’m currently research the AI/ML model using sagemaker, i built a grocery recommendation based on customer rate behavior as a lab. I have some problem using sagemaker experiment service, i can’t get the loss values and accuracy of each training epoch so that i can draw a chart for the visualization. Anyone has ideas about it, please share. Thank you. https://preview.redd.it/fzd8mz942hjb1.png?width=1853&format=png&auto=webp&s=6d75630acc3940c8fb4e4460b8a0eba8e9407b45 https://preview.redd.it/x2hpvjza2hjb1.png?width=927&format=png&auto=webp&s=f1c30944870df2d8fea182e3a6d8c70a80e60a7c submitted by /u/Open_Juice_2972 [comments]