pip install neograd
https://pypi.org/project/neograd/
https://neograd.readthedocs.io/
https://colab.research.google.com/drive/1D4JgBwKgnNQ8Q5DpninB6rdFUidRbjwM?usp=sharing https://colab.research.google.com/drive/184916aB5alIyM_xCa0qWnZAL35fDa43L?usp=sharing
I firmly believe that in order to understand something completely, you have to build it on your own from scratch. I used to do gradient calculation analytically, and thought that autograd was some kind of magic. So this was initially built to understand autograd but later on its scope was extended. You might be wondering, there are already many frameworks like TensorFlow and PyTorch that are very popular, and why did I have to create another one? The answer is that these have very complex codebases that are difficult to grasp. So I intend that this repository be used as an educational tool in order to understand how things work under the hood in these giant frameworks, with code that is intuitive and easily readable.
autograd offers automatic differentiation, implemented for the most commonly required operations for vectors of any dimension, with broadcasting capabilities
import neograd as ng
a = ng.tensor(3, requires_grad=True)
b = ng.tensor([1,2,3], requires_grad=True)
c = a+b
c.backward([1,1,1])
print(a.grad)
print(b.grad)If you wanted a custom operation to have autograd capabilities, those can be defined with very simple interface each having a forward method and a backward method
class Custom(Operation):
def forward(self):
pass
def backward(self):
passDebug your models/functions with Gradient Checking, to ensure that the gradients are getting propagated correctly
Create your own custom layers, optimizers, loss functions which provides more flexibility to create anything you desire
PyTorch's API is one of the best and one the most elegant API designs, so we've leveraged the same
nn contains some of the most commonly used optimizers, activations and loss functions required to train a Neural Network
Trained a model already? Then save the weights onto a file and load them whenever required or save the entire model, onto a file
Let's say you're training a model and your computer runs out of juice and if you'd waited until training was finished, to save the weights, then you'd lose all the weights. To prevent this, checkpoint your model with various sessions to save the weights during regular intervals with additional supporting data
import neograd as ng
from neograd import nn
import numpy as np
from neograd.nn.loss import BCE
from neograd.nn.optim import Adam
from neograd.autograd.utils import grad_check
from sklearn.datasets import make_circles
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
# load dataset (binary classification problem)
X, y = make_circles(n_samples=1000, noise=0.05, random_state=100)
X_train, X_test, y_train, y_test = train_test_split(X,y)
num_train = 750 # number of train examples
num_test = 250 # number of test examples
num_iter = 50 # number of training iterations
# convert data into tensors
X_train, X_test = ng.tensor(X_train[:num_train,:]), ng.tensor(X_test[:num_test,:])
y_train, y_test = ng.tensor(y_train[:num_train].reshape(num_train,1)), ng.tensor(y_test[:num_test].reshape(num_test,1))
# define the structure of your neural net
class NN(nn.Model):
def __init__(self):
self.stack = nn.Sequential(
nn.Linear(2,100),
nn.ReLU(),
nn.Linear(100,1),
nn.Sigmoid()
)
def forward(self, inputs):
return self.stack(inputs)
model = NN() # initialize a model
loss_fn = BCE() # initialize a loss function (Binary Cross Entropy)
optim = Adam(model.parameters(), 0.05) # initialize an optimizer
# training loop
for i in range(num_iter):
optim.zero_grad() # zero out the gradients in the tensors
outputs = model(X_train) # get the outputs by passing the training data to your model
loss = loss_fn(outputs, y_train) # calculate the loss
loss.backward() # initiate the backward pass to calculate the gradients
optim.step() # update the parameters
print(f"iter {i+1}/{num_iter}\nloss: {loss}\n")
with model.eval(): # put the model in evaluation mode
test_outputs = model(X_test) # get the outputs of the model on test data
preds = np.where(test_outputs.data>=0.5, 1, 0) # make predictions
print(classification_report(y_test.data.astype(int).flatten(), preds.flatten()))
print(accuracy_score(y_test.data.astype(int).flatten(), preds.flatten()))
grad_check(model, X_train, y_train, loss_fn) # perform gradient checking in your model- Andrej Karpathy's micrograd
Natively only supports scalar values for computation, whereas we support scalars, vectors, matrices all compatible with NumPy broadcasting - George Hotz's tinygrad
Has an obligation to be under 1000 lines of code leading to cramped up code, therefore our implementation is so much more readable and easily understandable. Also, no dealing with C/C++ code used in tinygrad for GPU acceleration - pytorch, tensorflow, etc
Large messy codebases written mostly in C/C++ for efficiency making it impossible to find you're way around and understand stuff. We've a pure Python implementation making it easy to get started and understand what's going on under the hood
- A big thank you to Andrej Karpathy for his CS231n lecture on Backpropagation which was instrumental in helping me gain a good grasp of the basic mechanisms of autograd
- Thanks to Terance Parr and Jeremy Howard for their paper The Matrix Calculus You Need For Deep Learning which helped me get rid of my fear for matrix calculus, that is beautifully written starting from the very fundamentals and slowly transitioning into advanced topics