Training a simple neural network, with PyTorch data loading — JAX documentation (original) (raw)

Training a simple neural network, with PyTorch data loading#

Copyright 2018 The JAX Authors.

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

https://www.apache.org/licenses/LICENSE-2.0

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Let’s combine everything we showed in the quickstart to train a simple neural network. We will first specify and train a simple MLP on MNIST using JAX for the computation. We will use PyTorch’s data loading API to load images and labels (because it’s pretty great, and the world doesn’t need yet another data loading library).

Of course, you can use JAX with any API that is compatible with NumPy to make specifying the model a bit more plug-and-play. Here, just for explanatory purposes, we won’t use any neural network libraries or special APIs for building our model.

import jax.numpy as jnp from jax import grad, jit, vmap from jax import random

Hyperparameters#

Let’s get a few bookkeeping items out of the way.

A helper function to randomly initialize weights and biases

for a dense neural network layer

def random_layer_params(m, n, key, scale=1e-2): w_key, b_key = random.split(key) return scale * random.normal(w_key, (n, m)), scale * random.normal(b_key, (n,))

Initialize all layers for a fully-connected neural network with sizes "sizes"

def init_network_params(sizes, key): keys = random.split(key, len(sizes)) return [random_layer_params(m, n, k) for m, n, k in zip(sizes[:-1], sizes[1:], keys)]

layer_sizes = [784, 512, 512, 10] step_size = 0.01 num_epochs = 8 batch_size = 128 n_targets = 10 params = init_network_params(layer_sizes, random.key(0))

Auto-batching predictions#

Let us first define our prediction function. Note that we’re defining this for a single image example. We’re going to use JAX’s vmap function to automatically handle mini-batches, with no performance penalty.

from jax.scipy.special import logsumexp

def relu(x): return jnp.maximum(0, x)

def predict(params, image):

per-example predictions

activations = image for w, b in params[:-1]: outputs = jnp.dot(w, activations) + b activations = relu(outputs)

final_w, final_b = params[-1] logits = jnp.dot(final_w, activations) + final_b return logits - logsumexp(logits)

Let’s check that our prediction function only works on single images.

This works on single examples

random_flattened_image = random.normal(random.key(1), (28 * 28,)) preds = predict(params, random_flattened_image) print(preds.shape)

Doesn't work with a batch

random_flattened_images = random.normal(random.key(1), (10, 28 * 28)) try: preds = predict(params, random_flattened_images) except TypeError: print('Invalid shapes!')

Let's upgrade it to handle batches using vmap

Make a batched version of the predict function

batched_predict = vmap(predict, in_axes=(None, 0))

batched_predict has the same call signature as predict

batched_preds = batched_predict(params, random_flattened_images) print(batched_preds.shape)

At this point, we have all the ingredients we need to define our neural network and train it. We’ve built an auto-batched version of predict, which we should be able to use in a loss function. We should be able to use grad to take the derivative of the loss with respect to the neural network parameters. Last, we should be able to use jit to speed up everything.

Utility and loss functions#

def one_hot(x, k, dtype=jnp.float32): """Create a one-hot encoding of x of size k.""" return jnp.array(x[:, None] == jnp.arange(k), dtype)

def accuracy(params, images, targets): target_class = jnp.argmax(targets, axis=1) predicted_class = jnp.argmax(batched_predict(params, images), axis=1) return jnp.mean(predicted_class == target_class)

def loss(params, images, targets): preds = batched_predict(params, images) return -jnp.mean(preds * targets)

@jit def update(params, x, y): grads = grad(loss)(params, x, y) return [(w - step_size * dw, b - step_size * db) for (w, b), (dw, db) in zip(params, grads)]

Data loading with PyTorch#

JAX is laser-focused on program transformations and accelerator-backed NumPy, so we don’t include data loading or munging in the JAX library. There are already a lot of great data loaders out there, so let’s just use them instead of reinventing anything. We’ll grab PyTorch’s data loader, and make a tiny shim to make it work with NumPy arrays.

!pip install torch torchvision

Requirement already satisfied: torch in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (2.4.1) Requirement already satisfied: torchvision in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (0.19.1) Requirement already satisfied: filelock in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torch) (3.16.0) Requirement already satisfied: typing-extensions>=4.8.0 in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torch) (4.12.2) Requirement already satisfied: sympy in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torch) (1.13.2) Requirement already satisfied: networkx in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torch) (3.3) Requirement already satisfied: jinja2 in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torch) (3.1.4) Requirement already satisfied: fsspec in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torch) (2024.9.0) Requirement already satisfied: setuptools in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torch) (73.0.1) Requirement already satisfied: numpy in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torchvision) (1.26.4) Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from torchvision) (10.4.0) Requirement already satisfied: MarkupSafe>=2.0 in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from jinja2->torch) (2.1.5) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages (from sympy->torch) (1.3.0)

/home/m/.opt/miniforge3/envs/jax/lib/python3.12/pty.py:95: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock. pid, fd = os.forkpty()

import numpy as np from jax.tree_util import tree_map from torch.utils.data import DataLoader, default_collate from torchvision.datasets import MNIST

def numpy_collate(batch): """ Collate function specifies how to combine a list of data samples into a batch. default_collate creates pytorch tensors, then tree_map converts them into numpy arrays. """ return tree_map(np.asarray, default_collate(batch))

def flatten_and_cast(pic): """Convert PIL image to flat (1-dimensional) numpy array.""" return np.ravel(np.array(pic, dtype=jnp.float32))

Define our dataset, using torch datasets

mnist_dataset = MNIST('/tmp/mnist/', download=True, transform=flatten_and_cast)

Create pytorch data loader with custom collate function

training_generator = DataLoader(mnist_dataset, batch_size=batch_size, collate_fn=numpy_collate)

Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz Failed to download (trying next): HTTP Error 404: Not Found

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to /tmp/mnist/MNIST/raw/train-images-idx3-ubyte.gz Extracting /tmp/mnist/MNIST/raw/train-images-idx3-ubyte.gz to /tmp/mnist/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz Failed to download (trying next): HTTP Error 404: Not Found

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to /tmp/mnist/MNIST/raw/train-labels-idx1-ubyte.gz Extracting /tmp/mnist/MNIST/raw/train-labels-idx1-ubyte.gz to /tmp/mnist/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz Failed to download (trying next): HTTP Error 404: Not Found

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to /tmp/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz Extracting /tmp/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to /tmp/mnist/MNIST/raw

Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz Failed to download (trying next): HTTP Error 404: Not Found

Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to /tmp/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz Extracting /tmp/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to /tmp/mnist/MNIST/raw

100.0% 100.0% 100.0% 100.0%

Get the full train dataset (for checking accuracy while training)

train_images = np.array(mnist_dataset.train_data).reshape(len(mnist_dataset.train_data), -1) train_labels = one_hot(np.array(mnist_dataset.train_labels), n_targets)

Get full test dataset

mnist_dataset_test = MNIST('/tmp/mnist/', download=True, train=False) test_images = jnp.array(mnist_dataset_test.test_data.numpy().reshape(len(mnist_dataset_test.test_data), -1), dtype=jnp.float32) test_labels = one_hot(np.array(mnist_dataset_test.test_labels), n_targets)

/home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages/torchvision/datasets/mnist.py:76: UserWarning: train_data has been renamed data warnings.warn("train_data has been renamed data") /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages/torchvision/datasets/mnist.py:66: UserWarning: train_labels has been renamed targets warnings.warn("train_labels has been renamed targets") /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages/torchvision/datasets/mnist.py:81: UserWarning: test_data has been renamed data warnings.warn("test_data has been renamed data") /home/m/.opt/miniforge3/envs/jax/lib/python3.12/site-packages/torchvision/datasets/mnist.py:71: UserWarning: test_labels has been renamed targets warnings.warn("test_labels has been renamed targets")

Training loop#

import time

for epoch in range(num_epochs): start_time = time.time() for x, y in training_generator: y = one_hot(y, n_targets) params = update(params, x, y) epoch_time = time.time() - start_time

train_acc = accuracy(params, train_images, train_labels) test_acc = accuracy(params, test_images, test_labels) print("Epoch {} in {:0.2f} sec".format(epoch, epoch_time)) print("Training set accuracy {}".format(train_acc)) print("Test set accuracy {}".format(test_acc))

Epoch 0 in 5.53 sec Training set accuracy 0.9156666994094849 Test set accuracy 0.9199000000953674 Epoch 1 in 1.13 sec Training set accuracy 0.9370499849319458 Test set accuracy 0.9383999705314636 Epoch 2 in 1.12 sec Training set accuracy 0.9490833282470703 Test set accuracy 0.9467999935150146 Epoch 3 in 1.21 sec Training set accuracy 0.9568833708763123 Test set accuracy 0.9532999992370605 Epoch 4 in 1.17 sec Training set accuracy 0.9631666541099548 Test set accuracy 0.9574999809265137 Epoch 5 in 1.17 sec Training set accuracy 0.9675000309944153 Test set accuracy 0.9615999460220337 Epoch 6 in 1.11 sec Training set accuracy 0.9709500074386597 Test set accuracy 0.9652999639511108 Epoch 7 in 1.17 sec Training set accuracy 0.9736999869346619 Test set accuracy 0.967199981212616

We’ve now used the whole of the JAX API: grad for derivatives, jit for speedups and vmap for auto-vectorization. We used NumPy to specify all of our computation, and borrowed the great data loaders from PyTorch, and ran the whole thing on the GPU.