def reduce_window_shape_tuple(operand_shape, window_dimensions, window_strides,
padding):
window_dimensions = (1,) + window_dimensions + (1,)
window_strides = (1,) + window_strides + (1,)
pads = padtype_to_pads(operand_shape, window_dimensions, window_strides, padding)
operand_padded = np.add(operand_shape, np.add(*zip(*pads)))
t = np.floor_divide(
np.subtract(operand_padded, window_dimensions), window_strides) + 1
return tuple(t)
def testDatasetInterop(self):
values = [1, 2, 3, 4, 5, 6]
values_as_array = np.asarray(values)
# Tensor dataset
dataset = tf.data.Dataset.from_tensors(values_as_array)
for value, value_from_dataset in zip([values_as_array], dataset):
self.assertIsInstance(value_from_dataset, np.ndarray)
self.assertAllEqual(value_from_dataset, value)
# Tensor slice dataset
dataset = tf.data.Dataset.from_tensor_slices(values_as_array)
for value, value_from_dataset in zip(values, dataset):
self.assertIsInstance(value_from_dataset, np.ndarray)
self.assertAllEqual(value_from_dataset, value)
# # TODO(nareshmodi): as_numpy_iterator() doesn't work.
# items = list(dataset.as_numpy_iterator())
# Map over a dataset.
dataset = dataset.map(lambda x: np.add(x, 1))
for value, value_from_dataset in zip(values, dataset):
self.assertIsInstance(value_from_dataset, np.ndarray)
self.assertAllEqual(value_from_dataset, value + 1)
# Batch a dataset.
dataset = tf.data.Dataset.from_tensor_slices(values_as_array).batch(2)
for value, value_from_dataset in zip([[1, 2], [3, 4], [5, 6]],
dataset):
self.assertIsInstance(value_from_dataset, np.ndarray)
self.assertAllEqual(value_from_dataset, value)