Nets

simple_img_conv_pool

paddle.v2.fluid.nets.simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act, param_attr=None, pool_type='max')

img_conv_group

paddle.v2.fluid.nets.img_conv_group(input, conv_num_filter, pool_size, conv_padding=1, conv_filter_size=3, conv_act=None, param_attr=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=None, pool_stride=1, pool_type=None)

Image Convolution Group, Used for vgg net.

sequence_conv_pool

paddle.v2.fluid.nets.sequence_conv_pool(input, num_filters, filter_size, param_attr=None, act='sigmoid', pool_type='max')

glu

paddle.v2.fluid.nets.glu(input, dim=-1)

The gated linear unit composed by split, sigmoid activation and elementwise multiplication. Specifically, Split the input into two equal sized parts \(a\) and \(b\) along the given dimension and then compute as following:

\[{GLU}(a, b)= a \otimes \sigma(b)\]

Refer to Language Modeling with Gated Convolutional Networks.

参数:

• input (Variable) – The input variable which is a Tensor or LoDTensor.
• dim (int) – The dimension along which to split. If \(dim < 0\), the dimension to split along is \(rank(input) + dim\).

返回:

The Tensor variable with half the size of input.

返回类型:

Variable

Examples

# x is a Tensor variable with shape [3, 6, 9]
fluid.nets.glu(input=x, dim=1)  # shape of output: [3, 3, 9]