Pico

Pico is a numpy-based "pico" neural network framework, with torch-like coding style and auto-grad implementation.

• Flexible Tensor
• Auto-grad mechanism
• Compact codes
• High performace and efficiency

Requirements

Install requirements of pico via:

pip install -r requirements.txt

Preparing the MNIST images further requires to install torch and torchvision, but they're not required for the basic functions.

Usage

The coding style of Pico is just almost the same as pytorch. See mnist.py for MNIST classification example.

Here is a mini-example:

>>> import pico
>>> import pico.functional as F
>>> import numpy as np

# create a Tensor, initialized with a numpy array, set requires_grad=True to calculate gradient
>>> x = pico.Tensor(np.random.randn(7, 5), requires_grad=True)
# create the target data
>>> target = pico.Tensor(np.random.randint(0, 5, (7,)))
# compute cross entropy loss
>>> loss = F.CrossEntropyLoss(x, target)
>>> loss
Tensor(1.7836267107242398, requires_grad=True)
# call backward of loss
>>> loss.backward()
>>> print(x.grad)
[[ 0.18704112  0.07444789  0.10220077  0.07920804  0.0466561 ]
 [ 0.0495909   0.0595846   0.36349058  0.3803355   0.04203391]
 [ 0.59336148 -0.05008445  0.1262403   0.04342903  0.1949402 ]
 [ 0.16987114  0.21257814  0.00249838  0.10804468  0.35199102]
 [ 0.09629747  0.03482448  0.43659541  0.20108366  0.08404939]
 [-0.1193856   0.02339847  0.09020922  0.21635362  0.16244956]
 [ 0.24815381  0.04523672  0.0740008   0.17944171  0.05384478]]

MNIST

Data prepare: Firstly install torch module for loading data.

pip install torch==1.10.2 torchvision==0.11.3

Then run command

python prepare_data.py

Waiting the script to finish.

Training:

python3 mnist.py

Pico module file list

pico
├── __init__.py
├── base.py
├── functional.py
├── nn.py
├── optimizer.py
└── utils.py