def analyse():
# Load results from disk
errs_pred, errs_base = cPickle.load(open(SAVE_ROOT + 'predictions.pkl', 'rb'))
# Print results
print_results('Baseline errors', errs_base)
print_results('Prediction errors', errs_pred)
# Plot best predictions, perform layer removal
MODEL.load(SAVE_PATH)
indices, _, _ = save_sequences(MODEL, TEST_PREPROCESSOR, SAVE_ROOT + 'qual/')
test_layer_subsets(MODEL, TEST_PREPROCESSOR, indices, SAVE_ROOT + 'lremoval/')
def test():
# Load model from disk and perform testing
MODEL.load(SAVE_PATH)
errs_pred, errs_base = MODEL.test(
x=TEST_PREPROCESSOR,
batch_size=BATCH_SIZE,
metric=('mse', 'psnr', 'dssim'),
)
# Save and print baseline/prediction errors
cPickle.dump((errs_pred, errs_base), open(SAVE_ROOT + 'predictions.pkl', 'wb'), cPickle.HIGHEST_PROTOCOL)
print_results('Baseline errors', errs_base)
print_results('Prediction errors', errs_pred)