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# Jnumpy Jacob's numpy library for machine learning ## Getting started 1. Install from `pip` or clone locally: ```bash $ pip install jnumpy ``` 2. Import the `jnumpy` module. ```python import jnumpy as jnp ``` ## Example ```python from jnumpy import * def NN(X_T): """Build a simple neural network.""" H_T = X_T for W_T, B_T in zip(layer_weights, layer_biases): Z_T = H_T @ W_T Z_T = Z_T + B_T return Sigm(Z_T) def loss_fn(ypred_T, ytrue_T): """Compute the mean squared error.""" err_T = (ypred_T - ytrue_T)**2 # [B, C] loss_T = ReduceSum(ReduceSum(err_T, axis=1), axis=0) # [] # training pipeline opt = SGD(lr=1.0) train_batch_size = min(32, Xtrain.shape[0]) num_train_batches = Xtrain.shape[0] // train_batch_size test_batch_size = min(32, Xtest.shape[0]) num_test_batches = Xtest.shape[0] // test_batch_size # training loop for epoch in range(rounds): opt.lr = 0.9 ** epoch # cool learning rate # minibatch training train_loss = 0.0 for i in range(num_train_batches): # get minibatch Xbatch = Xtrain[i*train_batch_size:(i+1)*train_batch_size] ybatch = Ytrain[i*train_batch_size:(i+1)*train_batch_size] X_T = Var(val=Xbatch, trainable=False) # [B, N] ytrue_T = Var(val=ybatch, trainable=False) # [B, C] # compute loss ypred_T = NN(X_T) # [B, C] loss_T = loss_fn(ypred_T, ytrue_T) # [] opt.minimize(loss_T) # update weights train_loss += loss_T.val # accumulate loss # print training stats print(f'Epoch {epoch+1}/{rounds} train loss: {train_loss/num_train_batches}') ``` ## Limitations and Future Work Version 2.0 is under development (see the dev branch) and will feature: - static execution graphs - a keras-style neural network API with `fit`, `evaluate`, and `predict` and premade layers - richer collections of optimizers, metrics, and losses - more examples Also maybe for the future: - custom backends (i.e.: tensorflow or pytorch instead of numpy) ## License All code in this repository is licensed under the MIT license. No restrictions, but no warranties. See the [LICENSE](LICENSE) file for details. ## Contributing This is a small project. I don't plan to grow it unless necesary. You are welcome to fork and contribute or email me `jacob` [dot] `valdez` [at] `limboid` [dot] `ai` if you would like to take over.