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
156 Dec 19, 2022
109 Dec 27, 2022
252 Dec 11, 2022
1 Jan 30, 2022
172 Dec 30, 2022
98 Nov 15, 2022
1 Dec 03, 2021
23 Dec 21, 2022
9 Jun 10, 2022
59 Dec 13, 2022
3 Dec 29, 2021
42 Dec 15, 2022
28 Jan 08, 2023
23 Jun 09, 2022
357 Dec 11, 2022
202 Dec 27, 2022
9 May 25, 2022
3 Dec 09, 2022
164 Dec 30, 2022