tf.layers.separable_conv2d
tf.layers.separable_conv2d
tf.layers.separable_conv2d
separable_conv2d( inputs, filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer=None, pointwise_initializer=None, bias_initializer=tf.zeros_initializer(), depthwise_regularizer=None, pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, trainable=True, name=None, reuse=None )
Defined in tensorflow/python/layers/convolutional.py
.
Functional interface for the depthwise separable 2D convolution layer.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. If use_bias
is True and a bias initializer is provided, it adds a bias vector to the output. It then optionally applies an activation function to produce the final output.
Arguments:
-
inputs
: Input tensor. -
filters
: Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). -
kernel_size
: A tuple or list of 2 integers specifying the spatial dimensions of of the filters. Can be a single integer to specify the same value for all spatial dimensions. -
strides
: A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying anystride
value != 1 is incompatible with specifying anydilation_rate
value != 1. -
padding
: One of"valid"
or"same"
(case-insensitive). -
data_format
: A string, one ofchannels_last
(default) orchannels_first
. The ordering of the dimensions in the inputs.channels_last
corresponds to inputs with shape(batch, height, width, channels)
whilechannels_first
corresponds to inputs with shape(batch, channels, height, width)
. -
dilation_rate
: An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying anydilation_rate
value != 1 is incompatible with specifying any stride value != 1. -
depth_multiplier
: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal tonum_filters_in * depth_multiplier
. -
activation
: Activation function. Set it to None to maintain a linear activation. -
use_bias
: Boolean, whether the layer uses a bias. -
depthwise_initializer
: An initializer for the depthwise convolution kernel. -
pointwise_initializer
: An initializer for the pointwise convolution kernel. -
bias_initializer
: An initializer for the bias vector. If None, no bias will be applied. -
depthwise_regularizer
: Optional regularizer for the depthwise convolution kernel. -
pointwise_regularizer
: Optional regularizer for the pointwise convolution kernel. -
bias_regularizer
: Optional regularizer for the bias vector. -
activity_regularizer
: Regularizer function for the output. -
trainable
: Boolean, ifTrue
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(seetf.Variable
). -
name
: A string, the name of the layer. -
reuse
: Boolean, whether to reuse the weights of a previous layer by the same name.
Returns:
Output tensor.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/layers/separable_conv2d