mindspore.dataset.transforms.Concatenate
- class mindspore.dataset.transforms.Concatenate(axis=0, prepend=None, append=None)[source]
Concatenate data with input array along given axis, only 1D data is supported.
- Parameters
axis (int, optional) – The axis along which the arrays will be concatenated. Default:
0
.prepend (numpy.ndarray, optional) – NumPy array to be prepended to the input array. Default:
None
, not to prepend array.append (numpy.ndarray, optional) – NumPy array to be appended to the input array. Default:
None
, not to append array.
- Raises
- Supported Platforms:
CPU
Examples
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.transforms as transforms >>> >>> # Use the transform in dataset pipeline mode >>> # concatenate string >>> prepend_tensor = np.array(["dw", "df"]) >>> append_tensor = np.array(["dwsdf", "df"]) >>> concatenate_op = transforms.Concatenate(0, prepend_tensor, append_tensor) >>> data = [["This","is","a","string"]] >>> numpy_slices_dataset = ds.NumpySlicesDataset(data) >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=concatenate_op) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["column_0"].shape, item["column_0"].dtype) (8,) <U6 >>> >>> # Use the transform in eager mode >>> data = np.array([1, 2, 3]) >>> prepend_tensor = np.array([10, 20]) >>> append_tensor = np.array([100]) >>> output = transforms.Concatenate(0, prepend_tensor, append_tensor)(data) >>> print(output.shape, output.dtype) (6,) int64