add alpha_dropout in nn.functional and nn.layer, test=develop #26365
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Thanks for your contribution! |
iclementine
reviewed
Aug 19, 2020
| keep_mask) | ||
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| #apply mask | ||
| alpha_p = layers.fill_constant(shape=[1], dtype=dtype, value=alpha_p) |
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这些 tensor * scalar 的情形其实用 scale 可能会更快。
iclementine
reviewed
Aug 19, 2020
python/paddle/nn/layer/common.py
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| class AlphaDropout(layers.Layer): | ||
| """ | ||
| :alias_main: paddle.nn.AlphaDropout |
iclementine
reviewed
Aug 19, 2020
python/paddle/nn/layer/common.py
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| [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) | ||
| `paddle.nn.functional.alpha_dropout` | ||
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| In dygraph mode, please use ``eval()`` to indicate whether it is in test phrase or not. |
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to switch to evaluation mode, where dropout is disabled.
willthefrog
requested changes
Aug 20, 2020
| @@ -456,6 +460,80 @@ def _is_list_or_turple_(data): | |||
| return out | |||
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| def alpha_dropout(x, p=0.5, training=True, name=None): | |||
| """ | |||
| alpha_dropout function. | |||
| x (Tensor): The input tensor. The data type is float32 or float64. | ||
| p (float): Probability of setting units to zero. Default 0.5. | ||
| training (bool): A flag indicating whether it is in train phrase or not. Default True. | ||
| name (str|None): A name for this layer(optional). If set None, the layer |
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use template phrase for name.
| name (str|None): A name for this layer(optional). If set None, the layer | ||
| will be named automatically. | ||
| Returns: | ||
| A Tensor representing the dropout, has same shape and data type with `x`. |
| x = np.array([[-1, 1], [-1, 1]]).astype('float32') | ||
| x = paddle.to_tensor(x) | ||
| y_train = paddle.nn.functional.alpha_dropout(x, 0.5) | ||
| y_test = paddle.nn.functional.alpha_dropout(x, 0.5, training=False) #test |
| # [[-0.10721093, 1.6655989 ], [-0.7791938, -0.7791938]] (randomly) | ||
| print(y_test.numpy()) | ||
| """ | ||
| if not isinstance(p, (float, int)): |
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docstring only says float
python/paddle/nn/layer/common.py
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| def __init__(self, p=0.5, name=None): | ||
| super(AlphaDropout, self).__init__() | ||
| self.p = p | ||
| self.training = _dygraph_tracer()._train_mode |
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Layer now has train()/eval(), this can be removed
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add alpha_dropout and AlphaDropout; |
willthefrog
reviewed
Aug 22, 2020
| @@ -80,42 +80,34 @@ def interpolate(input, | |||
| 'trilinear' : Trilinear interpolation | |||
| 'nearest' : Nearest neighbor interpolation | |||
| 'bicubic' : Bicubic interpolation | |||
| Linear interpolation is the method of using a line connecting two known quantities | |||
| For details of linear interpolation, please refer to Wikipedia: | ||
| https://en.wikipedia.org/wiki/Linear_interpolation. | ||
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| [4 0 6]] | ||
| (3) What about ``axis=[0, 1]`` ? This means the dropout is performed in all axes of x, | ||
| which is the same case as default setting ``axis=None`` . | ||
| (4) You may note that logically `axis=None` means the dropout is performed in no axis of x, |
| When x is a 4d tensor with shape `NCHW`, we can set ``axis=[0,1]`` and the dropout will be performed | ||
| in channel `N` and `C`, `H` and `W` is tied, i.e. | ||
| paddle.nn.dropout(x, p, axis=[0,1]) | ||
| This is something we called dropout2d. Please refer to ``paddle.nn.functional.dropout2d`` |
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remove This is something we called dropout2d.
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PR types
New features
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APIs
Describe
add apis of alpha_dropout:
(1) paddle.nn.functional.alpha_dropout(x, p=0.5, training=True, name = None)
(2) paddle.nn.AlphaDropout(p=0.5, name = None)