WIP : README Documentation - Plot IMG Aspect Ration changed

isaac
Pavan Mandava 5 years ago
parent d31d2c5e7b
commit a89075a5a3

@ -71,7 +71,7 @@ eval.metrics.f1_score(y_true, y_pred, labels, average)
[Link](/eval/metrics.py) to the metrics source code. [Link](/eval/metrics.py) to the metrics source code.
### Results ### Results
<img src="/plots/perceptron/confusion_matrix_plot.png?raw=true" width="500" height = "375" alt = "Confusion Matrix Plot" /> <img src="/plots/perceptron/confusion_matrix_plot.png?raw=true" width="600" height = "450" alt = "Confusion Matrix Plot" />
### 2) Feedforward Neural Network (using PyTorch) ### 2) Feedforward Neural Network (using PyTorch)
A feed-forward neural network classifier with a single hidden layer containing 9 units. While a feed-forward neural network is clearly not the ideal architecture for sequential text data, it was of interest to add a sort of second baseline and examine the added gains (if any) relative to a single perceptron. The input to the feedforward network remained the same; only the final model was suitable for more complex inputs such as word embeddings. A feed-forward neural network classifier with a single hidden layer containing 9 units. While a feed-forward neural network is clearly not the ideal architecture for sequential text data, it was of interest to add a sort of second baseline and examine the added gains (if any) relative to a single perceptron. The input to the feedforward network remained the same; only the final model was suitable for more complex inputs such as word embeddings.
@ -112,6 +112,6 @@ TODO
TODO TODO
### Results ### Results
<img src="/plots/bilstm_model/confusion_matrix_plot.png?raw=true" width="500" height = "375" alt = "Confusion Matrix Plot" /> <img src="/plots/bilstm_model/confusion_matrix_plot.png?raw=true" width="600" height = "450" alt = "Confusion Matrix Plot" />
## References ## References
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