Pytorch tools and utilities (Trainers, data generators, functions, and more...)
One of the most common tasks you will do in pytorch is define training loops, which is a hassle because they are almost always the same. Torchest has trainers you can use for your projects
from torchest.trainer import SimpleTrainer
# previous definition of model and data preparation
# train_dataloader = DataLoader(train_data)
# dev_dataloader = DataLoader(dev_data)
# test_dataloader = DataLoader(test_data)
# model = nn.Sequential(...)
...
"""
Prepare Trainer
"""
loss_fn = nn.CrossEntropyLoss()
learning_rate= 1e-2
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
trainer = SimpleTrainer(model, loss_fn, optimizer)
"""
Train
"""
epochs = 500
trainer.train(data_train=train_dataloader, data_dev=dev_dataloader, data_test=test_dataloader, epochs=epochs)Training loop progress
31%|██████████████████▍ | 156/500 [00:02<00:05, 58.01epoch/s, train_accuracy=76.2, train_cost=0.238290]
Trainer also saves the cost for the train, dev and test passes. You can simply call trainer.plot_costs() to display the graph with the costs
Trainers support wandb to visualize loss and acurracy as well as to save model versions. if you want to enable you have to set 2 parameters in your trainer initialization, name and wandb
trainer = SimpleTrainer(model, loss_fn, optimizer, wandb_project_name="MyProject", wandb=True)On the first run you will have to enter your API key, which you can obtain at https://wandb.ai/settings
This will create a 2d matrix with points in a dataplot. Ideal for testing non-linearity in your network
from torchest.datagen import spiral_datagen
class_num = 3
X, Y = spiral_datagen(450, class_num) # 450 elements per class
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