neuralflow-cpu 0.1.0

Deep learning framework built with numpy

NeuralFlow

Deep learning framework built with numpy (cupy)
This version supports only cpu

install

$ git clone git clone https://github.com/augustinLib/neuralflow.git

or

# cpu-only
$ pip install neuralflow-cpu

# gpu (cuda 11.x)
$ pip install neuralflow

Quick guide

you can build model like this,

from neuralflow.model import Model, DenseLayer, ConvLayer, MaxPoolingLayer
from neuralflow.function_class import ReLU

model = Model(
    DenseLayer(784, 50),
    ReLU(),
    DenseLayer(50, 10)
)

conv_model = Model(
    ConvLayer(input_channel = 1, output_channel = 30, kernel_size = 5, stride = 1, padding=0),
    ReLU(),
    MaxPoolingLayer(kernel_size=2, stride=2),
    DenseLayer(4320, 100),
    ReLU(),
    DenseLayer(100, 10)
)

and the training proceeds as follows.

from neuralflow.function_class import ReLU, CrossEntropyLoss
from neuralflow.optimizer import Adam

critic = CrossEntropyLoss()
optim = Adam()
pred = model(x)
loss = critic(pred, y)
model.backward(critic)
optim.update(model)

you can also train model with trainer

from neuralflow.trainer import ClassificationTrainer
from neuralflow.data import DataLoader

dataloader = DataLoader(train_data)
trainer = ClassificationTrainer(model,
                                critic,
                                optim,
                                epochs,
                                init_lr = 0.001)
trainer.train(dataloader)

when using gpu, set it as follows.

# using gpu
from neuralflow import config
config.GPU = True

# using cpu
from neuralflow import config
config.GPU = False

Structure

• neuralflow
  • __init__.py
  • data.py
  • function.py
  • function_class.py
  • model.py
  • optimizer.py
  • trainer.py
  • utils.py
  • nlp
    • __init__.py
    • utils.py
    • model.py
  • epoch_notice
    • send_message.py
    • token_generator.py
• dataset
• test
• README.md
• .gitignore