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
11 Dec 16, 2022
30 Dec 19, 2022
201 Dec 21, 2022
2 Jan 30, 2022
13 Dec 13, 2022
13 Nov 24, 2022
298 Dec 22, 2022
14 Dec 19, 2022
1.4k Dec 22, 2022
50 Aug 18, 2022
56 Dec 23, 2022
10 Aug 19, 2022
14 Oct 24, 2022
1.2k Jan 01, 2023
126 Dec 03, 2022
5.9k Jan 04, 2023
1 Oct 20, 2021
211 Dec 12, 2022
1 Jan 23, 2022
26 Oct 07, 2021