
## Model Description

### Keras Implementation of Convolutional autoencoder for image denoising

This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.

Spaces Link:- https://huggingface.co/spaces/keras-io/conv_autoencoder

Keras Example Link:- https://keras.io/examples/vision/autoencoder/

## Intended uses & limitations
- The trained model can be used to remove noise from any grayscale image.
- Since this model is trained on MNIST Data added with random noise, so this model can be used only for images with shape 28 * 28.

## Training and evaluation data
- Original mnist train & test dataset were loaded from tensorflow datasets.
- Then Some noise was added to train & test images.
- Noisy images were used as input images and original clean images were used as output images for training.

## Training procedure
### Training hyperparameter 
The following hyperparameters were used during training:
- optimizer: 'adam'
- loss: 'binary_crossentropy'
- epochs: 100
- batch_size: 128
- ReLU was used as activation function in all layers except last layer where Sigmoid was used as activation function.

## Model Plot

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

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

</details>
{"license": "gpl-3.0"}
keras tensorboard license:gpl-3.0 has_space region:us
TAGS
#keras #tensorboard #license-gpl-3.0 #has_space #region-us 


## Model Description

### Keras Implementation of Convolutional autoencoder for image denoising

This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.

Spaces Link:- URL

Keras Example Link:- URL

## Intended uses & limitations
- The trained model can be used to remove noise from any grayscale image.
- Since this model is trained on MNIST Data added with random noise, so this model can be used only for images with shape 28 * 28.

## Training and evaluation data
- Original mnist train & test dataset were loaded from tensorflow datasets.
- Then Some noise was added to train & test images.
- Noisy images were used as input images and original clean images were used as output images for training.

## Training procedure
### Training hyperparameter 
The following hyperparameters were used during training:
- optimizer: 'adam'
- loss: 'binary_crossentropy'
- epochs: 100
- batch_size: 128
- ReLU was used as activation function in all layers except last layer where Sigmoid was used as activation function.

## Model Plot

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

!Model Image

</details>
## Model Description ### Keras Implementation of Convolutional autoencoder for image denoising

This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.

Spaces Link:- URL

Keras Example Link:- URL ## Intended uses & limitations
- The trained model can be used to remove noise from any grayscale image.
- Since this model is trained on MNIST Data added with random noise, so this model can be used only for images with shape 28 * 28. ## Training and evaluation data
- Original mnist train & test dataset were loaded from tensorflow datasets.
- Then Some noise was added to train & test images.
- Noisy images were used as input images and original clean images were used as output images for training. ## Training procedure ### Training hyperparameter 
The following hyperparameters were used during training:
- optimizer: 'adam'
- loss: 'binary_crossentropy'
- epochs: 100
- batch_size: 128
- ReLU was used as activation function in all layers except last layer where Sigmoid was used as activation function. ## Model Plot

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

!Model Image

</details>
TAGS
#keras #tensorboard #license-gpl-3.0 #has_space #region-us 
 ## Model Description ### Keras Implementation of Convolutional autoencoder for image denoising

This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.

Spaces Link:- URL

Keras Example Link:- URL ## Intended uses & limitations
- The trained model can be used to remove noise from any grayscale image.
- Since this model is trained on MNIST Data added with random noise, so this model can be used only for images with shape 28 * 28. ## Training and evaluation data
- Original mnist train & test dataset were loaded from tensorflow datasets.
- Then Some noise was added to train & test images.
- Noisy images were used as input images and original clean images were used as output images for training. ## Training procedure ### Training hyperparameter 
The following hyperparameters were used during training:
- optimizer: 'adam'
- loss: 'binary_crossentropy'
- epochs: 100
- batch_size: 128
- ReLU was used as activation function in all layers except last layer where Sigmoid was used as activation function. ## Model Plot

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

!Model Image

</details>
passage: TAGS
#keras #tensorboard #license-gpl-3.0 #has_space #region-us 
## Model Description### Keras Implementation of Convolutional autoencoder for image denoising

This repo contains the trained model of Convolutional autoencoder for image denoising on MNIST Dataset mixed with random noise.

Spaces Link:- URL

Keras Example Link:- URL## Intended uses & limitations
- The trained model can be used to remove noise from any grayscale image.
- Since this model is trained on MNIST Data added with random noise, so this model can be used only for images with shape 28 * 28.## Training and evaluation data
- Original mnist train & test dataset were loaded from tensorflow datasets.
- Then Some noise was added to train & test images.
- Noisy images were used as input images and original clean images were used as output images for training.## Training procedure### Training hyperparameter 
The following hyperparameters were used during training:
- optimizer: 'adam'
- loss: 'binary_crossentropy'
- epochs: 100
- batch_size: 128
- ReLU was used as activation function in all layers except last layer where Sigmoid was used as activation function.## Model Plot

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

!Model Image

</details>