Exemple #1
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runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    logdir=logdir,
    num_epochs=num_epochs,
    check=True,
    load_best_on_end=True,
)

# # Inference

# In[ ]:

runner_out = runner.predict_loader(loader=loaders["valid"])

# # Predictions visualization

# In[ ]:

import matplotlib.pyplot as plt

plt.style.use("ggplot")

# In[ ]:

sigmoid = lambda x: 1 / (1 + np.exp(-x))

for i, (input, output) in enumerate(zip(valid_data, runner_out)):
    image, mask = input
Exemple #2
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runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    logdir=logdir,
    num_epochs=num_epochs,
    load_best_on_end=True,
)

# # Setup 9 - predict_loader

# In[ ]:

runner_out = runner.predict_loader(
    model=model,
    loader=loaders["valid"],
)

# In[ ]:

next(runner_out)[runner.output_key].shape

# # Setup 10 - predict batch

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features, targets = next(iter(loaders["valid"]))

# In[ ]:

features.shape