Datasets:
ArXiv:
License:
File size: 4,240 Bytes
6822471 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
{
"name": "27_Image_Generation_DCGAN_MNIST_DL",
"query": "I need to create a system for image generation using a DCGAN model with the MNIST`dataset. Load the MNIST dataset in `src/data_loader.py` and implement the DCGAN model in `src/model.py`. The system should ensure the use of the correct DCGAN architecture, save the generated images to `results/figures/`, monitor the model training by recording training loss under `results/metrics/` and generated images under `results/figures/`, and perform a hyperparameter search on the generation parameters such as noise vector dimensions and learning rate in `src/train.py` to improve performance. Additionally, create and save a GIF animation of the generated images to `results/figures/generated_images.gif`, present the training process and results in a well-structured Jupyter Notebook, and convert the Notebook into a polished PDF report saved as `results/training_report.pdf`. The DCGAN model architecture should be clearly documented in the Notebook to avoid confusion with other GAN variants.",
"tags": [
"Computer Vision",
"Generative Models"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"MNIST\" dataset is loaded in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [],
"criteria": "The \"DCGAN\" model, not a standard GAN, is implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
0,
1
],
"criteria": "Generated images are saved to the specified folder `results/figures/`.",
"category": "Save Trained Model",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
0,
1
],
"criteria": "The model training is monitored by recording training loss saved under `results/metrics/`",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
0,
1
],
"criteria": "A hyperparemeter search method to search parameters such as noise vector dimensions and learning rate is implemented in `src/train.py` to improve model performance.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
1,
2,
3,
4
],
"criteria": "A GIF animation of generated images is created and saved as `results/figures/generated_images.gif`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
1,
2,
3,
4
],
"criteria": "The training process and results are presented in a Jupyter Notebook, and converted to a PDF report, and saved as `results/training_report.pdf`.",
"category": "Visualization",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The DCGAN model architecture should be clearly documented in the Notebook to avoid confusion with other GAN variants.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The PDF report should be well-structured, with clear sections for model architecture, training process, results, and future improvements.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false,
"hint": "Saving figures is mentioned twice, i.e., once in requirement 2 and once in requirement 3."
} |