def default_generator(
self,
dataset: dc.data.Dataset,
epochs: int = 1,
mode: str = 'fit',
deterministic: bool = True,
pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
empty: np.ndarray = np.array([])
for epoch in range(epochs):
for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
batch_size=self.batch_size,
deterministic=deterministic,
pad_batches=pad_batches):
if y_b is not None:
y_b = y_b.reshape(-1, self.n_tasks, 1)
if X_b is not None:
if mode == 'fit':
for transformer in self.fit_transformers:
X_b, _, _, _ = transformer.transform_array(
X_b, empty, empty, empty)
if mode == 'predict':
dropout = np.array(0.0)
else:
dropout = np.array(1.0)
yield ([X_b, dropout], [y_b], [w_b])
def default_generator(
self,
dataset: dc.data.Dataset,
epochs: int = 1,
mode: str = 'fit',
deterministic: bool = True,
pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
for epoch in range(epochs):
for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
batch_size=self.batch_size,
deterministic=deterministic,
pad_batches=pad_batches):
yield ([X_b], [y_b], [w_b])
def default_generator(
self,
dataset: dc.data.Dataset,
epochs: int = 1,
mode: str = 'fit',
deterministic: bool = True,
pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
for epoch in range(epochs):
for (X_b, y_b, w_b,
ids_b) in dataset.iterbatches(batch_size=self.batch_size,
deterministic=deterministic,
pad_batches=pad_batches):
if y_b is not None:
y_b = to_one_hot(y_b.flatten(), self.n_classes).reshape(
-1, self.n_tasks, self.n_classes)
yield ([X_b], [y_b], [w_b])
def default_generator(
self,
dataset: dc.data.Dataset,
epochs: int = 1,
mode: str = 'fit',
deterministic: bool = True,
pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
for epoch in range(epochs):
for (X_b, y_b, w_b,
ids_b) in dataset.iterbatches(batch_size=self.batch_size,
deterministic=deterministic,
pad_batches=pad_batches):
if mode == 'predict':
dropout = np.array(0.0)
else:
dropout = np.array(1.0)
yield ([X_b, dropout], [y_b], [w_b])
