code stringlengths 3 6.57k |
|---|
os.path.splitext(os.path.basename(sys.argv[1]) |
write_palette(outfile, palette) |
write_image(outfile, img, palette) |
write_descriptor(outfile, name) |
BaseWrapper(object) |
methods (e.g., `epochs`, `batch_size`) |
fitting (predicting) |
__init__(self, build_fn=None, **sk_params) |
self.check_params(sk_params) |
check_params(self, params) |
legal_params_fns.append(self.__call__) |
elif (not isinstance(self.build_fn, types.FunctionType) |
isinstance(self.build_fn, types.MethodType) |
legal_params_fns.append(self.build_fn.__call__) |
legal_params_fns.append(self.build_fn) |
has_arg(fn, params_name) |
ValueError('{} is not a legal parameter'.format(params_name) |
get_params(self, **params) |
ignored (exists for API compatibility) |
self.sk_params.copy() |
res.update({'build_fn': self.build_fn}) |
set_params(self, **params) |
self.check_params(params) |
self.sk_params.update(params) |
fit(self, x, y, **kwargs) |
self.__call__(**self.filter_sk_params(self.__call__) |
elif (not isinstance(self.build_fn, types.FunctionType) |
isinstance(self.build_fn, types.MethodType) |
self.filter_sk_params(self.build_fn.__call__) |
self.build_fn(**self.filter_sk_params(self.build_fn) |
if (losses.is_categorical_crossentropy(self.model.loss) |
len(y.shape) |
to_categorical(y) |
copy.deepcopy(self.filter_sk_params(Sequential.fit) |
fit_args.update(kwargs) |
self.model.fit(x, y, **fit_args) |
filter_sk_params(self, fn, override=None) |
self.sk_params.items() |
has_arg(fn, name) |
res.update({name: value}) |
res.update(override) |
keras_export('keras.wrappers.scikit_learn.KerasClassifier') |
KerasClassifier(BaseWrapper) |
fit(self, x, y, **kwargs) |
np.array(y) |
len(y.shape) |
np.arange(y.shape[1]) |
elif (len(y.shape) |
len(y.shape) |
np.unique(y) |
np.searchsorted(self.classes_, y) |
ValueError('Invalid shape for y: ' + str(y.shape) |
len(self.classes_) |
super(KerasClassifier, self) |
fit(x, y, **kwargs) |
predict(self, x, **kwargs) |
self.filter_sk_params(Sequential.predict_classes, kwargs) |
self.model.predict_classes(x, **kwargs) |
predict_proba(self, x, **kwargs) |
self.filter_sk_params(Sequential.predict_proba, kwargs) |
self.model.predict(x, **kwargs) |
np.hstack([1 - probs, probs]) |
score(self, x, y, **kwargs) |
compile() |
np.searchsorted(self.classes_, y) |
self.filter_sk_params(Sequential.evaluate, kwargs) |
hasattr(loss_name, '__name__') |
len(y.shape) |
to_categorical(y) |
self.model.evaluate(x, y, **kwargs) |
isinstance(outputs, list) |
zip(self.model.metrics_names, outputs) |
model.compile() |
keras_export('keras.wrappers.scikit_learn.KerasRegressor') |
KerasRegressor(BaseWrapper) |
predict(self, x, **kwargs) |
self.filter_sk_params(Sequential.predict, kwargs) |
np.squeeze(self.model.predict(x, **kwargs) |
score(self, x, y, **kwargs) |
self.filter_sk_params(Sequential.evaluate, kwargs) |
self.model.evaluate(x, y, **kwargs) |
isinstance(loss, list) |
ModelTests(TestCase) |
test_create_user_with_email_successful(self) |
get_user_model() |
user.set_password(password) |
self.assertEqual(user.email, email) |
self.assertTrue(user.check_password(password) |
test_user_email_is_normalised(self) |
get_user_model() |
objects.create_user(email, 'test123') |
self.assertEqual(user.email, email.lower() |
test_create_user_invalid_email(self) |
self.assertRaises(ValueError) |
get_user_model() |
objects.create_user(None, 'test123') |
test_create_new_super_user(self) |
get_user_model() |
self.assertTrue(user.is_superuser) |
self.assertTrue(user.is_staff) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.