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transformer >99.98% test accuracy in ~30s (#230)

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* transformer

* BS might divide len(Y_test)

* outoput when accuracy is high

* more readeable

* fixed loss in serious_mnist for new API
This commit is contained in:
Marcel Bischoff
2021-01-02 10:45:09 -05:00
committed by GitHub
parent ebd72ff437
commit 42b4761025
3 changed files with 24 additions and 16 deletions
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12
extra/training.py
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@@ -40,17 +40,17 @@ def train(model, X_train, Y_train, optim, steps, BS=128, lossfn=sparse_categoric
accuracies.append(accuracy)
t.set_description("loss %.2f accuracy %.2f" % (loss, accuracy))
def evaluate(model, X_test, Y_test, num_classes=None, BS=128):
def evaluate(model, X_test, Y_test, num_classes=None, BS=128, return_predict=False):
Tensor.training = False
def numpy_eval(num_classes):
Y_test_preds_out = np.zeros(list(Y_test.shape)+[num_classes])
for i in trange(len(Y_test)//BS, disable=os.getenv('CI') is not None):
for i in trange((len(Y_test)-1)//BS+1, disable=os.getenv('CI') is not None):
Y_test_preds_out[i*BS:(i+1)*BS] = model.forward(Tensor(X_test[i*BS:(i+1)*BS])).cpu().data
Y_test_preds = np.argmax(Y_test_preds_out, axis=-1)
return (Y_test == Y_test_preds).mean()
return (Y_test == Y_test_preds).mean(), Y_test_preds
if num_classes is None: num_classes = Y_test.max().astype(int)+1
accuracy = numpy_eval(num_classes)
print("test set accuracy is %f" % accuracy)
return accuracy
acc, Y_test_pred = numpy_eval(num_classes)
print("test set accuracy is %f" % acc)
return (acc, Y_test_pred) if return_predict else acc