https://github.com/titu1994/tf_neural_deconvolution
Neural Deconvolutions in Tensorflow
https://github.com/titu1994/tf_neural_deconvolution
keras tensorflow tensorflow2
Last synced: 2 days ago
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Neural Deconvolutions in Tensorflow
- Host: GitHub
- URL: https://github.com/titu1994/tf_neural_deconvolution
- Owner: titu1994
- License: mit
- Created: 2020-05-05T04:46:56.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-18T00:10:51.000Z (over 5 years ago)
- Last Synced: 2025-03-25T05:34:09.906Z (7 months ago)
- Topics: keras, tensorflow, tensorflow2
- Language: Python
- Homepage:
- Size: 93.8 KB
- Stars: 12
- Watchers: 3
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Deconvolutions (Tensorflow)
Tensorflow implementation of the `FastDeconv2D` and `ChannelDeconv` layers from the paper [Network Deconvolution](https://openreview.net/forum?id=rkeu30EtvS) by Ye et al. Code ported from the repository - https://github.com/yechengxi/deconvolution/.
Tensorflow implementation also support mixed precision training, allowing larger training sizes with no reduction in accuracy (found in `tf_deconv_mixed_prec.py`).
# Usage
Simply download the `tf_deconv.py` script and import `ChannelDeconv2D` and `FastDeconv2D` layers. Mixed precision support can be found in equivalent classes inside `tf_deconv_mixed_prec.py`.
A baseline model has been provided in `models/vgg.py` to try out the architecture. `FastDeconv2D` can replace most Conv2D layer operations.
## Important Note
-----------------
It is crucial to initialize your models properly to obtain correct performance.
1) All `FastDeconv2D` kernels are initialized by default using `he_uniform`, and their bias by `BiasHeUniform`.
2) Final `Dense` layer `kernel_initializer` should be `he_uniform` and `bias_initializer` should be `BiasHeUniform`.
--------
```python
import tensorflow as tf
from tf_deconv import FastDeconv2D, ChannelDeconv2D, BiasHeUniform
kernel_size = 3
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
class VGG(tf.keras.Model):
def __init__(self, vgg_name, num_classes=10):
super(VGG, self).__init__()
assert vgg_name in cfg.keys(), "Choose VGG model from {}".format(cfg.keys())
self.features = self._make_layers(cfg[vgg_name])
self.channel_deconv = ChannelDeconv2D(block=512)
self.classifier = tf.keras.layers.Dense(num_classes, activation='softmax',
kernel_initializer='he_uniform',
bias_initializer=BiasHeUniform(),
)
def call(self, x, training=None, mask=None):
out = self.features(x, training=training)
out = self.channel_deconv(out, training=training)
out = self.classifier(out)
return out
def _make_layers(self, cfg):
layers = []
in_channels = 3
for x in cfg:
if x == 'M':
layers.append(tf.keras.layers.MaxPool2D())
else:
if in_channels == 3:
deconv = FastDeconv2D(in_channels, x, kernel_size=(kernel_size, kernel_size), padding='same',
freeze=True, n_iter=15, block=64, activation='relu')
else:
deconv = FastDeconv2D(in_channels, x, kernel_size=(kernel_size, kernel_size), padding='same',
block=64, activation='relu')
layers.append(deconv)
in_channels = x
layers.append(tf.keras.layers.GlobalAveragePooling2D())
return tf.keras.Sequential(layers)
```
# Dependencies
- Tensorflow 2.1+