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---
library_name: keras
tags:
- text-generation
---
## Model description
This repo contains the model which showcases the learning capabilities of LSTM using a simple example. A single-layer LSTM is made to learn to add two numbers, provided as strings. The model has been trained for adding two numbers where each number can have maximum of 5 digits.
*Example:*
Input: "535+61"
Output: "596"
Full credits to [Smerity](https://twitter.com/Smerity) and others for this work.
## Intended uses & limitations
More information needed
## Training and evaluation data
The data consists of generation of two random 5 digit numbers as input and their sum as output. These numbers (_and their sum)_ are encoded and fed as input to LSTM. The full data creation code is available within the [example](https://keras.io/examples/nlp/addition_rnn/).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- optimizer: {'name': 'Adam', 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
- num_epochs: 30
## Training Metrics
| Epochs | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy |
|--- |--- |--- |--- |--- |
| 1| 0.071| 0.977| 0.12| 0.957|
| 2| 0.063| 0.98| 0.102| 0.963|
| 3| 0.061| 0.98| 0.094| 0.967|
| 4| 0.051| 0.983| 0.091| 0.969|
| 5| 0.052| 0.983| 0.093| 0.964|
| 6| 0.05| 0.983| 0.073| 0.975|
| 7| 0.039| 0.988| 0.069| 0.976|
| 8| 0.054| 0.983| 0.103| 0.965|
| 9| 0.04| 0.987| 0.063| 0.977|
| 10| 0.033| 0.99| 0.141| 0.953|
| 11| 0.037| 0.989| 0.083| 0.971|
| 12| 0.04| 0.987| 0.069| 0.976|
| 13| 0.027| 0.992| 0.053| 0.98|
| 14| 0.03| 0.991| 0.071| 0.974|
| 15| 0.03| 0.991| 0.061| 0.979|
| 16| 0.029| 0.991| 0.048| 0.982|
| 17| 0.037| 0.989| 0.091| 0.97|
| 18| 0.023| 0.993| 0.039| 0.987|
| 19| 0.028| 0.991| 0.058| 0.981|
| 20| 0.022| 0.994| 0.057| 0.98|
| 21| 0.023| 0.993| 0.038| 0.987|
| 22| 0.034| 0.99| 0.054| 0.982|
| 23| 0.026| 0.993| 0.12| 0.959|
| 24| 0.027| 0.992| 0.034| 0.989|
| 25| 0.022| 0.993| 0.047| 0.984|
| 26| 0.02| 0.994| 0.062| 0.978|
| 27| 0.024| 0.993| 0.043| 0.985|
| 28| 0.019| 0.994| 0.057| 0.979|
| 29| 0.017| 0.995| 0.054| 0.982|
| 30| 0.021| 0.994| 0.033| 0.989|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |