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escription of the architecture of your networks, Plots of the loss on the training set through epochs of training, Accuracy of the final trained networks on the test set

escription of the architecture of your networks, Plots of the loss on the training set through epochs of training, Accuracy of the final trained networks on the test set

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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  • Description of the architecture of your networks,
  • Plots of the loss on the training set through epochs of training, Accuracy of the final trained networks on the test set
### Part A: PyTorch - **Use PyTorch `nn.Sequential` container ([PyTorch Documentation](https://pytorch.org/docs/stable/nn.html)) to build a Convolutional Neural Network (CNN) for the 10-class MNIST problem.** - Use Convolutional and Pooling layers, and explore using different parameters (e.g., number of filters). - Follow up the convolutional part of the network with a standard feedforward dense layer (`nn.Linear` + ReLU activation). - Explore the effect of using Batch normalization.
Transcribed Image Text:### Part A: PyTorch - **Use PyTorch `nn.Sequential` container ([PyTorch Documentation](https://pytorch.org/docs/stable/nn.html)) to build a Convolutional Neural Network (CNN) for the 10-class MNIST problem.** - Use Convolutional and Pooling layers, and explore using different parameters (e.g., number of filters). - Follow up the convolutional part of the network with a standard feedforward dense layer (`nn.Linear` + ReLU activation). - Explore the effect of using Batch normalization.
**Part B: TensorFlow** - Use **Functional API** from TensorFlow Keras ([TensorFlow Functional API Guide](https://www.tensorflow.org/guide/keras/functional)) to build a CNN network similar to what you have built in Part A.
Transcribed Image Text:**Part B: TensorFlow** - Use **Functional API** from TensorFlow Keras ([TensorFlow Functional API Guide](https://www.tensorflow.org/guide/keras/functional)) to build a CNN network similar to what you have built in Part A.
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