This guide will give a quick intro to training PyTorch models with torchbearer. We'll start by loading in some data and defining a model, then we'll train it for a few epochs and see how well it does.
Let's get using torchbearer. Here's some data from Cifar10 and a simple 3 layer strided CNN:
/_static/examples/quickstart.py
Note that we use torchbearers .DatasetValidationSplitter here to create a validation set (10% of the data). This is essential to avoid over-fitting to your test data.
Typically we would need a training loop and a series of calls to backward, step etc. Instead, with torchbearer, we can define our optimiser and some metrics (just 'acc' and 'loss' for now) and let it do the work.
/_static/examples/quickstart.py
Running the above produces the following output:
Files already downloaded and verified
Files already downloaded and verified
0/10(t): 100%|██████████| 352/352 [00:02<00:00, 163.98it/s, acc=0.4339, loss=1.5776, running_acc=0.5202, running_loss=1.3494]
0/10(v): 100%|██████████| 40/40 [00:00<00:00, 365.42it/s, val_acc=0.5266, val_loss=1.3208]
1/10(t): 100%|██████████| 352/352 [00:02<00:00, 171.36it/s, acc=0.5636, loss=1.2176, running_acc=0.5922, running_loss=1.1418]
1/10(v): 100%|██████████| 40/40 [00:00<00:00, 292.15it/s, val_acc=0.5888, val_loss=1.1657]
2/10(t): 100%|██████████| 352/352 [00:02<00:00, 124.04it/s, acc=0.6226, loss=1.0671, running_acc=0.6222, running_loss=1.0566]
2/10(v): 100%|██████████| 40/40 [00:00<00:00, 359.21it/s, val_acc=0.626, val_loss=1.0555]
3/10(t): 100%|██████████| 352/352 [00:02<00:00, 151.69it/s, acc=0.6587, loss=0.972, running_acc=0.6634, running_loss=0.9589]
3/10(v): 100%|██████████| 40/40 [00:00<00:00, 222.62it/s, val_acc=0.6414, val_loss=1.0064]
4/10(t): 100%|██████████| 352/352 [00:02<00:00, 131.49it/s, acc=0.6829, loss=0.9061, running_acc=0.6764, running_loss=0.918]
4/10(v): 100%|██████████| 40/40 [00:00<00:00, 346.88it/s, val_acc=0.6636, val_loss=0.9449]
5/10(t): 100%|██████████| 352/352 [00:02<00:00, 164.28it/s, acc=0.6988, loss=0.8563, running_acc=0.6919, running_loss=0.858]
5/10(v): 100%|██████████| 40/40 [00:00<00:00, 244.97it/s, val_acc=0.663, val_loss=0.9404]
6/10(t): 100%|██████████| 352/352 [00:02<00:00, 149.52it/s, acc=0.7169, loss=0.8131, running_acc=0.7095, running_loss=0.8421]
6/10(v): 100%|██████████| 40/40 [00:00<00:00, 329.26it/s, val_acc=0.6704, val_loss=0.9209]
7/10(t): 100%|██████████| 352/352 [00:02<00:00, 160.60it/s, acc=0.7302, loss=0.7756, running_acc=0.738, running_loss=0.767]
7/10(v): 100%|██████████| 40/40 [00:00<00:00, 349.86it/s, val_acc=0.6716, val_loss=0.9313]
8/10(t): 100%|██████████| 352/352 [00:02<00:00, 155.08it/s, acc=0.7412, loss=0.7444, running_acc=0.7347, running_loss=0.7547]
8/10(v): 100%|██████████| 40/40 [00:00<00:00, 350.05it/s, val_acc=0.673, val_loss=0.9324]
9/10(t): 100%|██████████| 352/352 [00:02<00:00, 165.28it/s, acc=0.7515, loss=0.715, running_acc=0.7352, running_loss=0.7492]
9/10(v): 100%|██████████| 40/40 [00:00<00:00, 310.76it/s, val_acc=0.6792, val_loss=0.9743]
0/1(e): 100%|██████████| 79/79 [00:00<00:00, 233.06it/s, test_acc=0.6673, test_loss=0.9741]The source code for the example is given below:
Download Python source code: quickstart.py </_static/examples/quickstart.py>