neural attention filter
Project description
Neural Filter
Functions
neuralfilter.generate(x, force=False)
- x: Array with resolution $(H, W, C)$
The dimension $C$ is recommended as 1. - force: 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 'generate'.
- x: Array with resolution $(N, H, W, C)$
The dimension $C$ is recommended as 1. - force: 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
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()
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