Welcome to NumPyNet package’s documentation!
Implementation in pure Numpy of neural networks models. NumPyNet supports a syntax very close to the Keras one but it is written using only Numpy functions: in this way it is very light and fast to install and use/modify.
Overview
NumPyNet is born as educational framework for the study of Neural Network models. It is written trying to balance code readability and computational performances and it is enriched with a large documentation to better understand the functionality of each script. The library is written in pure Python and the only external library used is Numpy (a base package for the scientific research).
Despite all common libraries are correlated by a wide documentation is often difficult for novel users to move around the many hyper-links and papers cited in them. NumPyNet tries to overcome this problem with a minimal mathematical documentation associated to each script and a wide range of comments inside the code.
An other “problem” to take in count is related to performances. Libraries like Tensorflow are certainly efficient from a computational point-of-view and the numerous wrappers (like Keras library) guarantee an extremely simple user interface. On the other hand, the deeper functionalities of the code and the implementation strategies used are unavoidably hidden behind tons of code lines. In this way the user can perform complex computational tasks using the library as black-box package. NumPyNet wants to overcome this problem using simple Python codes, with extremely readability also for novel users, to better understand the symmetry between mathematical formulas and code.
Usage example
First of all we have to import the main modules of the NumPyNet package as
from NumPyNet.network import Network
from NumPyNet.layers.connected_layer import Connected_layer
from NumPyNet.layers.convolutional_layer import Convolutional_layer
from NumPyNet.layers.maxpool_layer import Maxpool_layer
from NumPyNet.layers.softmax_layer import Softmax_layer
from NumPyNet.layers.batchnorm_layer import BatchNorm_layer
from NumPyNet.optimizer import Adam
Now we can try to create a very simple model able to classify the well known MNIST-digit dataset. The MNIST dataset can be extracted from the sklearn library as
from sklearn import datasets
from sklearn.model_selection import train_test_split
digits = datasets.load_digits()
X, y = digits.images, digits.target
X = np.asarray([np.dstack((x, x, x)) for x in X])
X = X.transpose(0, 2, 3, 1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.33, random_state=42)
Now we have to create our model. We can use a syntax very close to the Keras one and simply define a model object adding a series of layers
model = Network(batch=batch, input_shape=X_train.shape[1:])
model.add(Convolutional_layer(size=3, filters=32, stride=1, pad=True, activation='Relu'))
model.add(BatchNorm_layer())
model.add(Maxpool_layer(size=2, stride=1, padding=True))
model.add(Connected_layer(outputs=100, activation='Relu'))
model.add(BatchNorm_layer())
model.add(Connected_layer(outputs=num_classes, activation='Linear'))
model.add(Softmax_layer(spatial=True, groups=1, temperature=1.))
model.compile(optimizer=Adam(), metrics=[accuracy])
model.summary()
The model automatically creates an InputLayer if it is not explicitly provided, but pay attention to the right input_shape values!
Before feeding our model we have to convert the image dataset into categorical variables. To this purpose we can use the simple utilities of the NumPyNet package.
from NumPyNet.utils import to_categorical
from NumPyNet.utils import from_categorical
from NumPyNet.metrics import mean_accuracy_score
# normalization to [0, 1]
X_train *= 1. / 255.
X_test *= 1. / 255.
n_train = X_train.shape[0]
n_test = X_test.shape[0]
# transform y to array of dimension 10 and in 4 dimension
y_train = to_categorical(y_train).reshape(n_train, 1, 1, -1)
y_test = to_categorical(y_test).reshape(n_test, 1, 1, -1)
Now you can run your fit function to train the model as
model.fit(X=X_train, y=y_train, max_iter=100)
and evaluate the results on the testing set
loss, out = model.evaluate(X=X_test, truth=y_test, verbose=True)
truth = from_categorical(y_test)
predicted = from_categorical(out)
accuracy = mean_accuracy_score(truth, predicted)
print('\nLoss Score: {:.3f}'.format(loss))
print('Accuracy Score: {:.3f}'.format(accuracy))
You should see something like this
layer filters size input output
0 input 128 x 8 x 3 x 8 -> 128 x 8 x 3 x 8
1 conv 32 3 x 3 / 1 128 x 8 x 3 x 8 -> 128 x 8 x 3 x 32 0.000 BFLOPs
2 batchnorm 8 x 3 x 32 image
3 max 2 x 2 / 1 128 x 8 x 3 x 32 -> 128 x 7 x 2 x 32
4 connected 128 x 7 x 2 x 32 -> 128 x 100
5 batchnorm 1 x 1 x 100 image
6 connected 128 x 1 x 1 x 100 -> 128 x 10
7 softmax x entropy 128 x 1 x 1 x 10
Epoch 1/10
512/512 |██████████████████████████████████████████████████| (0.7 sec/iter) loss: 26.676 accuracy: 0.826
Epoch 2/10
512/512 |██████████████████████████████████████████████████| (0.6 sec/iter) loss: 22.547 accuracy: 0.914
Epoch 3/10
512/512 |██████████████████████████████████████████████████| (0.7 sec/iter) loss: 21.333 accuracy: 0.943
Epoch 4/10
512/512 |██████████████████████████████████████████████████| (0.6 sec/iter) loss: 20.832 accuracy: 0.963
Epoch 5/10
512/512 |██████████████████████████████████████████████████| (0.5 sec/iter) loss: 20.529 accuracy: 0.975
Epoch 6/10
512/512 |██████████████████████████████████████████████████| (0.3 sec/iter) loss: 20.322 accuracy: 0.977
Epoch 7/10
512/512 |██████████████████████████████████████████████████| (0.3 sec/iter) loss: 20.164 accuracy: 0.986
Epoch 8/10
512/512 |██████████████████████████████████████████████████| (0.3 sec/iter) loss: 20.050 accuracy: 0.992
Epoch 9/10
512/512 |██████████████████████████████████████████████████| (0.3 sec/iter) loss: 19.955 accuracy: 0.994
Epoch 10/10
512/512 |██████████████████████████████████████████████████| (0.3 sec/iter) loss: 19.875 accuracy: 0.996
Training on 10 epochs took 21.6 sec
300/300 |██████████████████████████████████████████████████| (0.0 sec/iter) loss: 10.472
Prediction on 300 samples took 0.1 sec
Loss Score: 2.610
Accuracy Score: 0.937
Obviously the execution time can vary according to your available resources! You can find a full list of example scripts here