
## Model description

This model has couple of Dense layers.

## Intended uses & limitations

It's intended to demonstrate capabilities of Hub for Keras on my blog post!

## Training and evaluation data

It's trained on dummy data. 

Above information is filled manually.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

 ## Training Metrics
| Epochs | Train Loss | Validation Loss |
 |--- |--- |--- |
| 1| 0.102|  0.094| 
| 2| 0.094|  0.092| 
| 3| 0.092|  0.091| 
| 4| 0.091|  0.09| 
| 5| 0.09|  0.089| 
 ## Model Plot

<details>
<summary>View Model Plot</summary>

![Model Image](./model.png)

</details>
{"library_name": "keras", "tags": ["object-detection"]}
keras tensorboard object-detection region:us
TAGS
#keras #tensorboard #object-detection #region-us 

Model description
-----------------


This model has couple of Dense layers.


Intended uses & limitations
---------------------------


It's intended to demonstrate capabilities of Hub for Keras on my blog post!


Training and evaluation data
----------------------------


It's trained on dummy data.


Above information is filled manually.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 0.001, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


Training Metrics
----------------


Epochs: 1, Train Loss: 0.102, Validation Loss: 0.094
Epochs: 2, Train Loss: 0.094, Validation Loss: 0.092
Epochs: 3, Train Loss: 0.092, Validation Loss: 0.091
Epochs: 4, Train Loss: 0.091, Validation Loss: 0.09
Epochs: 5, Train Loss: 0.09, Validation Loss: 0.089


Model Plot
----------



View Model Plot
!Model Image



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 0.001, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


Training Metrics
----------------


Epochs: 1, Train Loss: 0.102, Validation Loss: 0.094
Epochs: 2, Train Loss: 0.094, Validation Loss: 0.092
Epochs: 3, Train Loss: 0.092, Validation Loss: 0.091
Epochs: 4, Train Loss: 0.091, Validation Loss: 0.09
Epochs: 5, Train Loss: 0.09, Validation Loss: 0.089


Model Plot
----------



View Model Plot
!Model Image
TAGS
#keras #tensorboard #object-detection #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 0.001, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


Training Metrics
----------------


Epochs: 1, Train Loss: 0.102, Validation Loss: 0.094
Epochs: 2, Train Loss: 0.094, Validation Loss: 0.092
Epochs: 3, Train Loss: 0.092, Validation Loss: 0.091
Epochs: 4, Train Loss: 0.091, Validation Loss: 0.09
Epochs: 5, Train Loss: 0.09, Validation Loss: 0.089


Model Plot
----------



View Model Plot
!Model Image
passage: TAGS
#keras #tensorboard #object-detection #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 0.001, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


Training Metrics
----------------


Epochs: 1, Train Loss: 0.102, Validation Loss: 0.094
Epochs: 2, Train Loss: 0.094, Validation Loss: 0.092
Epochs: 3, Train Loss: 0.092, Validation Loss: 0.091
Epochs: 4, Train Loss: 0.091, Validation Loss: 0.09
Epochs: 5, Train Loss: 0.09, Validation Loss: 0.089


Model Plot
----------



View Model Plot
!Model Image