tf.layers.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)

tf.layers.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)

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 output of filters in the convolution).
• kernel_size: a tuple or list of N positive 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 N positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
• padding: one of "valid" or "same" (case-insensitive).
• data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shapedata_format = 'NWHC' (batch, width, height, channels) while channels_first corresponds to inputs with shape (batch, channels, width, height).
• 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 any dilation_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 to num_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, if True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.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.

Defined in tensorflow/python/layers/convolutional.py.