grandlantern 0.0.17

This is the small library for deep learning.

This is Grand Lantern neural network library рџЏ®

The main purpose of this library is to understand - what is happening under the hood of popular deep learning frameworks. Also, it can be used to build your own ideas - no special knowledge for thst you need!

This library is written only using numpy (maybe cupy in future), you can see every calculation in neural network!

How to install в›ЏпёЏ

Just put this command to terminal

pip install grandlantern

Get started рџљЂ

1. Import library

import grandlantern

2. Define Data Iterator

from grandlantern.dataiterators import DatasetIterator, TableDataset

batch_size = 100
my_dataset_iterator = DatasetIterator(dataset=TableDataset(), batch_size=batch_size)

3. Choose optimizer

from grandlantern.optimizers import SGD

my_optimizer = SGD(learning_rate=0.01)

4. Build model

from grandlantern import model
from grandlantern.metrics import CrossEntropy, Accuracy

NN = model(n_epochs=100,
           dataset_iterator=my_dataset_iterator,
           loss_function=CrossEntropy(),
           metric_function=Accuracy(),
           optimizer=my_optimizer)

5. Add layers

from grandlantern.layers import LinearLayer
from grandlantern.layers import Sigmoid, ReLU, SoftMax

NN.add_layer(LinearLayer(n_neurons=100, activation=Sigmoid(), biased=True))
NN.add_layer(LinearLayer(n_neurons=50, activation=ReLU(), biased=True))
NN.add_layer(LinearLayer(n_neurons=10, activation=SoftMax(), biased=True))

Also layers can be added using attribute model.layers:

from grandlantern.layers import LinearLayer
from grandlantern.layers import Sigmoid, ReLU, SoftMax

NN.layers = 
[
    LinearLayer(n_neurons=100, activation=Sigmoid(), biased=True),
    LinearLayer(n_neurons=50, activation=ReLU(), biased=True)
    LinearLayer(n_neurons=10, activation=SoftMax(), biased=True)
]

To look at the model structure the command print can be used:

print(NN)

6. Train model

Inputs for training must be numpy arrays. There is also option to validate model on test data while training (X_test and y_test are optional).

NN.fit(X_train, y_train, X_test, y_test)

7. Use model

Input for prediction must be numpy array.

y_pred = NN.predict(X_test)

What is supported now вњ…

Layers

• Linear Layer (LinearLayer)
• Image Convolutional Layer (Conv2DLayer)
• Batch Normalization Layer (BatchNormLayer)
• Recurrent Layer (RecurrentLayer)
• RNN Layer (RNNLayer)
• Flatten Layer (FlattenLayer)

Optimizers

• SGD
• NAG
• Adagrad
• Adam

Datasets and Iterators

• Dataset Iterator with shuffle
• Table Dataset
• Image Dataset
• Sequence Dataset

Regularization

• L1, L2, Elastic Net
• Dropout Layer (DropOutLayer)

What will be done рџ“ќ

New Layers

• Bidirectional RNN
• LSTM, GRU layers
• Attention layers
• Seq2seq model
• Transformers
• Pooling layers

GPU Accelaration using cupy

Some more optimizers

Preprocessing modules