Local Response Normalization Layer. This layer performs a type of
"lateral inhibition" by normalizing over local input regions.
This operator implements the Local Response Normalization Layer.
This layer performs a type of "lateral inhibition" by normalizing over local input regions.
For more information, please refer to `ImageNet Classification with Deep Convolutional Neural Networks <https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_
input (Variable): The input tensor of this layer, and the dimension of input tensor must be 4.
n (int, default 5): The number of channels to sum over.
k (float, default 1.0): An offset (usually positive to avoid dividing by 0).
alpha (float, default 1e-4): The scaling parameter.
beta (float, default 0.75): The exponent.
name (str, default None): A name for this operation.
Raises:
ValueError: If rank of the input tensor is not 4.
input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W], where N is the batch size, C is the input channel, H is Height, W is weight. The data type is float32. The rank of this tensor must be 4, otherwise it will raise ValueError.
n (int, optional): The number of channels to sum over. Default: 5
k (float, optional): An offset, positive. Default: 1.0
alpha (float, optional): The scaling parameter, positive. Default:1e-4
beta (float, optional): The exponent, positive. Default:0.75
name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
A tensor variable storing the transformation result.
Variable: A tensor variable storing the transformation result with the same shape and data type as input.
Region of interest pooling (also known as RoI pooling) is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7*7).
The operator has three steps:
1. Dividing each region proposal into equal-sized sections with the pooled_width and pooled_height;
2. Finding the largest value in each section;
3. Copying these max values to the output buffer.
For more information, please refer to https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
Args:
input (Variable): ${x_comment}
rois (Variable): ROIs (Regions of Interest) to pool over.It should be
a 2-D LoDTensor of shape (num_rois, 4), the lod level
is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is
the top left coordinates, and (x2, y2) is the bottom
input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W], where N is the batch size, C is the input channel, H is Height, W is weight. The data type is float32 or float64.
rois (Variable): ROIs (Regions of Interest) to pool over. 2D-LoDTensor with the shape of [num_rois,4], the lod level is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates.
pooled_height (int, optional): The pooled output height, data type is int32. Default: 1
pooled_width (int, optional): The pooled output height, data type is int32. Default: 1
spatial_scale (float, optional): Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0
Returns:
Variable: ${out_comment}.
Variable: The pooled feature, 4D-Tensor with the shape of [num_rois, C, pooled_height, pooled_width].
input (Variable): This input is a probability computed by the previous operator.
The first dimension is batch size, and the last dimension is 1.
label (Variable): The groud truth whose first dimension is batch size
and last dimension is 1.
delta (float): The parameter of huber loss, which controls
the range of outliers
input (Variable): Predicted data, 2D-Tensor with the shape of [batch_size, 1]. The data type should be float32 or float64.
label (Variable): Ground truth label, 2D-Tensor with the shape of [batch_size, 1]. The data type should be float32 or float64.
delta (float): The threshold for Huber loss, which is used to control the balance between the linear error and square error. The data type should be float32.
Returns:
huber\_loss (Variable): The huber loss with shape [batch_size, 1].
Variable: The huber loss, a tensor with the same shape and data type as input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
.. code-block:: python
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
threshold(float): The threshold parameter of HardSwish operator. Default:threshold=6.0
scale(float): The scale parameter of HardSwish operator. Default:scale=6.0
offset(float): The offset parameter of HardSwish operator. Default:offset=3.0
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
This operator implements the hard_swish activation function.
Hard_swish is proposed in MobileNetV3, and performs better in computational stability and efficiency compared to swish function.
For more details please refer to: https://arxiv.org/pdf/1905.02244.pdf
Returns:
Variable: The output tensor with the same shape as input.
The formula is as follows:
Examples:
.. math::
.. code-block:: python
out = \\frac{x * (min(max(0, x+offset), threshold))}{scale}
import paddle.fluid as fluid
x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
y = fluid.layers.hard_swish(x)
In the above equation:
``threshold`` and ``scale`` should be positive, ``offset`` can be positive or negative. It is recommended to use default parameters.
Args:
x (Variable): Input feature, multi-dimensional Tensor. The data type should be float32 or float64.
threshold (float, optional): The threshold in Relu function. Default: 6.0
scale (float, optional): The scale factor. Default: 6.0
offset (float, optional): The offset factor. Default: 3.0
name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: The output tensor with the same shape and data type as input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
DATATYPE='float32'
x_data = np.array([i for i in range(1,5)]).reshape([1,1,4]).astype(DATATYPE)
x = fluid.data(name="x", shape=[None,1,4], dtype=DATATYPE)
