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---
library_name: keras
license: mit
---
## Model description
A very simple model that converts an image into a number!
### the hepler function
(requirements: `numpy Pillow`)
```python
import numpy as np
from PIL import Image
def predict(model, img):
pil_image = img
pil_image = pil_image.resize((64, 64))
image_array = np.array(pil_image) / 255.0
image_array = np.expand_dims(image_array, axis=0)
input_shape = (64, 64, pil_image.mode == 'RGB' and 3 or 1)
decimal_prediction = model.predict(image_array)[0][0]
return decimal_prediction
```
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | False |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |