How would you modify resnet model to improve its performance

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Reference no: EM133362719

Machine Learning

Project

Image Classification Using ResNet

In this project, you will learn how to use ResNet to classify images from the CIFAR-10 dataset.

Residual Networks, or ResNets, are a type of deep neural network architecture that was introduced in 2015. They are known for their ability to train very deep neural networks, which was previously a challenge due to vanishing gradients. ResNets use skip connections to allow information to flow directly from one layer to another, bypassing intermediate layers. This helps to avoid the vanishing gradient problem and allows for the training of very deep networks.

CIFAR-10: CIFAR-10 is a dataset of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The classes are airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. The dataset is commonly used as a benchmark for image classification algorithms and has been used extensively in deep learning research.

You will be provided with a Python script that loads and train a dataset of images, and you will need to complete the script by adding a few code lines to evaluate the ResNet model.Follow the following instructions step-by-step.

1. First, open your web browser and navigate to the Google Colab website

2. To save your work in Google Colab, you can either use the keyboard shortcut "Ctrl + S" or click on "File" in the menu bar and then select "Save" or "Save a copy in Drive". If you choose "Save a copy in Drive", a copy of the notebook will be saved to your Google Drive account. If you choose "Save", the notebook will be saved in the Colab environment.If you want to download the notebook to your local machine, you can click on "File" in the menu bar and then select "Download .ipynb". This will download the notebook file in Jupyter notebook format to your local machine.

3. Open the provided code file name "Final_project" in Google Colab.

4. Run the code line by line until you reach the model evaluation section.

Now, it's time to evaluate the model on the test data.

Question 1) Plot the training and validation accuracy curves over the 10 epochs of training. You can use the history.history attribute which contains information about the training history of the model, including the values of the training and validation metrics at each epoch.

Question 2) How does the validation accuracy compare to the training accuracy, and what does this suggest about the model's ability to generalize to new data?

Question 3) What is the test accuracy of the ResNet model on the CIFAR-10 dataset, and how does it compare to the validation accuracy?

Question 4) Show five random example images from the test set with their predicted labels.

Question 5) How would you modify the ResNet model to improve its performance on the CIFAR-10 dataset, and why do you think these modifications might be effective?

Reference no: EM133362719

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