White-Box-Layer is a Python module for deep learning built on top of TensorFlow and is distributed under the MIT license.
The project was started in May 2021 by YeongHyeon Park.
This project does not limit for participation.
Contribute now!
Installation
Dependencies
whiteboxlayer requires:
- Numpy: 1.18.5
- Scipy: 1.4.1
- TensorFlow: 2.3.0
User installation
You can install the white-box-layer via simple command as below.
$ pip install whiteboxlayer
Development
We welcome new contributors of all experience levels. The white-box-layer community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We've included some basic information in this README.
Example
Example for Convolutional Neural Network
An example of constructing a convolutional neural network is covered. The relevant source code is additionally provided following links.
Define TensorFlow based module
class Neuralnet(tf.Module):
def __init__(self, **kwargs):
super(Neuralnet, self).__init__()
self.who_am_i = kwargs['who_am_i']
self.dim_h = kwargs['dim_h']
self.dim_w = kwargs['dim_w']
self.dim_c = kwargs['dim_c']
self.num_class = kwargs['num_class']
self.filters = kwargs['filters']
self.layer = wbl.Layers()
self.forward = tf.function(self.__call__)
@tf.function
def __call__(self, x, verbose=False):
logit = self.__nn(x=x, name=self.who_am_i, verbose=verbose)
y_hat = tf.nn.softmax(logit, name="y_hat")
return logit, y_hat
def __nn(self, x, name='neuralnet', verbose=True):
for idx, _ in enumerate(self.filters[:-1]):
if(idx == 0): continue
x = self.layer.conv2d(x=x, stride=1, \
filter_size=[3, 3, self.filters[idx-1], self.filters[idx]], \
activation='relu', name='%s-%dconv' %(name, idx), verbose=verbose)
x = self.layer.maxpool(x=x, ksize=2, strides=2, \
name='%s-%dmp' %(name, idx), verbose=verbose)
x = tf.reshape(x, shape=[x.shape[0], -1], name="flat")
x = self.layer.fully_connected(x=x, c_out=self.filters[-1], \
activation='relu', name="%s-clf0" %(name), verbose=verbose)
x = self.layer.fully_connected(x=x, c_out=self.num_class, \
activation=None, name="%s-clf1" %(name), verbose=verbose)
return x
Initializing module
model = Neuralnet(\
who_am_i="CNN", \
dim_h=28, dim_w=28, dim_c=1, \
num_class=10, \
filters=[1, 32, 64, 128])
dummy = tf.zeros((1, model.dim_h, model.dim_w, model.dim_c), dtype=tf.float32)
model.forward(x=dummy, verbose=True)
Results
Conv (CNN-1conv) (1, 28, 28, 1) -> (1, 28, 28, 32)
MaxPool (CNN-1mp) (1, 28, 28, 32) > (1, 14, 14, 32)
Conv (CNN-2conv) (1, 14, 14, 32) -> (1, 14, 14, 64)
MaxPool (CNN-2mp) (1, 14, 14, 64) > (1, 7, 7, 64)
FC (CNN-clf0) (1, 3136) -> (1, 128)
FC (CNN-clf1) (1, 128) -> (1, 10)
Conv (CNN-1conv) (1, 28, 28, 1) -> (1, 28, 28, 32)
MaxPool (CNN-1mp) (1, 28, 28, 32) > (1, 14, 14, 32)
Conv (CNN-2conv) (1, 14, 14, 32) -> (1, 14, 14, 64)
MaxPool (CNN-2mp) (1, 14, 14, 64) > (1, 7, 7, 64)
FC (CNN-clf0) (1, 3136) -> (1, 128)
FC (CNN-clf1) (1, 128) -> (1, 10)
2 Aug 21, 2022
9.6k Jan 2, 2023
6.2k Jan 1, 2023
1.8k Dec 28, 2022
437 Jan 3, 2023
127 Dec 12, 2022
5.7k Jan 9, 2023
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109 Dec 14, 2022
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51 Nov 09, 2022
23 Nov 29, 2022
4 May 26, 2022
17 Oct 11, 2022
153 Dec 02, 2022
293 Dec 22, 2022
41 Dec 23, 2022
728 Dec 28, 2022
856 Jan 06, 2023
20.5k Jan 08, 2023
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4 Feb 06, 2022
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323 Dec 29, 2022