tf.compat.v1.layers.separable_conv2d

Functional interface for the depthwise separable 2D convolution layer.

tf.compat.v1.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.compat.v1.zeros_initializer(),
    depthwise_regularizer=None,
    pointwise_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    depthwise_constraint=None,
    pointwise_constraint=None,
    bias_constraint=None,
    trainable=True,
    name=None,
    reuse=None
)

Migrate to TF2

This API is a legacy api that is only compatible with eager execution and tf.function if you combine it with tf.compat.v1.keras.utils.track_tf1_style_variables

Please refer to tf.layers model mapping section of the migration guide to learn how to use your TensorFlow v1 model in TF2 with Keras.

The corresponding TensorFlow v2 layer is tf.keras.layers.SeparableConv2D.

Structural Mapping to Native TF2

None of the supported arguments have changed name.

Before:

 y = tf.compat.v1.layers.separable_conv2d(x, filters=3, kernel_size=3)

After:

To migrate code using TF1 functional layers use the Keras Functional API:

 x = tf.keras.Input((28, 28, 1))
 y = tf.keras.layers.SeparableConv2D(filters=3, kernels_size=3)(x)
 model = tf.keras.Model(x, y)

Description

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.

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 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 any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding One of "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
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 shape (batch, height, width, channels) while channels_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 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, the default initializer will be used.
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 Optional regularizer function for the output.
depthwise_constraint Optional projection function to be applied to the depthwise kernel after being updated by an Optimizer (e.g. used for norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.
pointwise_constraint Optional projection function to be applied to the pointwise kernel after being updated by an Optimizer.
bias_constraint Optional projection function to be applied to the bias after being updated by an Optimizer.
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.

Output tensor.

ValueError if eager execution is enabled.