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@staticmethod
tf.data.Dataset.from_tensor_slices(
tensors
)
For more information, see tf.data.Dataset.from_tensor_slices.
class mindspore.dataset.NumpySlicesDataset(
data,
column_names=None,
num_samples=None,
num_parallel_workers=1,
shuffle=None,
sampler=None,
num_shards=None,
shard_id=None
)
For more information, see mindspore.dataset.NumpySlicesDataset.
TensorFlow: A static method that creates Dataset with the specified tf.Tensor.
MindSpore: A dataset class that creates Dataset with the specified list , tuple , dict or numpy.ndarray.
# The following implements NumpySlicesDataset with MindSpore.
import numpy as np
import mindspore.dataset as ds
data = np.array([[1, 2], [3, 4], [5, 6]])
dataset = ds.NumpySlicesDataset(data=data, column_names=["data"], shuffle=False)
for item in dataset.create_dict_iterator():
print(item["data"])
# [1 2]
# [3 4]
# [5 6]
# The following implements from_tensor_slices with TensorFlow.
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
data = tf.constant([[1, 2], [3, 4], [5, 6]])
dataset = tf.data.Dataset.from_tensor_slices(data)
for value in dataset:
print(value)
# [1 2]
# [3 4]
# [5 6]
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