neuralfilter

Name	neuralfilter JSON
Version	0.1.9 JSON
	download
home_page
Summary	neural attention filter
upload_time	2023-01-29 05:17:14
maintainer
docs_url	None
author	YeongHyeon Park
requires_python	>=3
license
keywords	neural-filter
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            Neural Filter
=====

## Functions
#### neuralfilter.generate(x, force=False)
- <strong>x</strong>: Array with resolution $(H, W, C)$  
The dimension $C$ is recommended as 1.  
- <strong>force</strong>: If you want to force this operation when the dimension $C$ is higher than 1,  set 'force' as True.  

#### neuralfilter.batch_generate(x, force=False)
- The batch processing mode of '<a href='https://github.com/YeongHyeon/NeuralFilter#neuralfiltergeneratex-forcefalse'>generate</a>'.  
- <strong>x</strong>: Array with resolution $(N, H, W, C)$  
The dimension $C$ is recommended as 1.  
- <strong>force</strong>: If you want to force this operation when the dimension $C$ is higher than 1,  set 'force' as True.  

batch_generate(x, force=False)



## Usage
``` python
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import neuralfilter

(x_tr, y_tr), (x_te, y_te) = tf.keras.datasets.mnist.load_data()

idx = 1
a = neuralfilter.generate(np.expand_dims(x_tr[idx], axis=-1))

plt.figure(figsize=(9, 3), dpi=100)
plt.subplot(1, 3, 1)
plt.imshow(x_tr[idx], cmap='gray')
plt.xticks([], [])
plt.yticks([], [])

plt.subplot(1, 3, 2)
plt.imshow(a, cmap='jet')
plt.xticks([], [])
plt.yticks([], [])

plt.subplot(1, 3, 3)
plt.imshow(x_tr[idx], cmap='gray')
plt.imshow(a, cmap='jet', alpha=0.5)
plt.xticks([], [])
plt.yticks([], [])

plt.tight_layout()
plt.savefig('sample.png')
plt.show()
```

<img src="https://github.com/YeongHyeon/NeuralFilter/blob/main/figures/sample.png" width="500">



            

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    "description": "Neural Filter\n=====\n\n## Functions\n#### neuralfilter.generate(x, force=False)\n- <strong>x</strong>: Array with resolution $(H, W, C)$  \nThe dimension $C$ is recommended as 1.  \n- <strong>force</strong>: If you want to force this operation when the dimension $C$ is higher than 1,  set 'force' as True.  \n\n#### neuralfilter.batch_generate(x, force=False)\n- The batch processing mode of '<a href='https://github.com/YeongHyeon/NeuralFilter#neuralfiltergeneratex-forcefalse'>generate</a>'.  \n- <strong>x</strong>: Array with resolution $(N, H, W, C)$  \nThe dimension $C$ is recommended as 1.  \n- <strong>force</strong>: If you want to force this operation when the dimension $C$ is higher than 1,  set 'force' as True.  \n\nbatch_generate(x, force=False)\n\n\n\n## Usage\n``` python\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport neuralfilter\n\n(x_tr, y_tr), (x_te, y_te) = tf.keras.datasets.mnist.load_data()\n\nidx = 1\na = neuralfilter.generate(np.expand_dims(x_tr[idx], axis=-1))\n\nplt.figure(figsize=(9, 3), dpi=100)\nplt.subplot(1, 3, 1)\nplt.imshow(x_tr[idx], cmap='gray')\nplt.xticks([], [])\nplt.yticks([], [])\n\nplt.subplot(1, 3, 2)\nplt.imshow(a, cmap='jet')\nplt.xticks([], [])\nplt.yticks([], [])\n\nplt.subplot(1, 3, 3)\nplt.imshow(x_tr[idx], cmap='gray')\nplt.imshow(a, cmap='jet', alpha=0.5)\nplt.xticks([], [])\nplt.yticks([], [])\n\nplt.tight_layout()\nplt.savefig('sample.png')\nplt.show()\n```\n\n<img src=\"https://github.com/YeongHyeon/NeuralFilter/blob/main/figures/sample.png\" width=\"500\">\n\n\n",
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