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
3.4k Jan 7, 2023
6 Jul 29, 2022
1 Dec 17, 2021
1.4k Dec 22, 2022
16 Dec 17, 2022
83 Nov 29, 2022
1.1k Dec 12, 2022
25 Sep 06, 2022
19 Dec 01, 2022
285 Jan 04, 2023
132 Dec 24, 2022
132 Dec 30, 2022
88 Dec 25, 2022
209 Jan 05, 2023
2 Mar 26, 2022
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1 Jan 17, 2022
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4 Aug 23, 2022