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68 lines
2.4 KiB
68 lines
2.4 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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from .framework import Variable, in_dygraph_mode
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from .layer_helper import LayerHelper
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__all__ = ['one_hot']
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def one_hot(input, depth, allow_out_of_range=False):
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"""
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This layer creates the one-hot representations for input indices.
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Args:
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input(Variable): Input indices represent locations, which takes value 1.0
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in indices, while all other locations take value 0.
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depth(scalar): An interger defining the depth of the one-hot dimension.
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allow_out_of_range(bool): A bool value indicating whether the input
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indices could be out of range [0, depth). When input indices are
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out of range, exceptions is raised if allow_out_of_range is False,
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or zero-filling representations is created if it is set True
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Returns:
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Variable: The one-hot representations of input.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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one_hot_label = fluid.input.one_hot(input=label, depth=10)
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"""
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helper = LayerHelper("one_hot_v2", **locals())
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one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
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if in_dygraph_mode():
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inputs = {'X': input}
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attrs = {'depth': depth}
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else:
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if not isinstance(depth, Variable):
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# user attribute
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inputs = {'X': input}
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attrs = {'depth': depth}
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else:
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depth.stop_gradient = True
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inputs = {'X': input, 'depth_tensor': depth}
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attrs = {}
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helper.append_op(
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type="one_hot_v2",
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inputs=inputs,
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attrs=attrs,
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outputs={'Out': one_hot_out},
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stop_gradient=True)
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return one_hot_out
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