tf.keras.layers.SeparableConv2D

2D separable convolution layer.

Inherits From: Layer, Operation

tf.keras.layers.SeparableConv2D(
    filters,
    kernel_size,
    strides=(1, 1),
    padding='valid',
    data_format=None,
    dilation_rate=(1, 1),
    depth_multiplier=1,
    activation=None,
    use_bias=True,
    depthwise_initializer='glorot_uniform',
    pointwise_initializer='glorot_uniform',
    bias_initializer='zeros',
    depthwise_regularizer=None,
    pointwise_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    depthwise_constraint=None,
    pointwise_constraint=None,
    bias_constraint=None,
    **kwargs
)

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.

filters int, the dimensionality of the output space (i.e. the number of filters in the pointwise convolution).
kernel_size int or tuple/list of 2 integers, specifying the size of the depthwise convolution window.
strides int or tuple/list of 2 integers, specifying the stride length of the depthwise convolution. If only one int is specified, the same stride size will be used for all dimensions. strides > 1 is incompatible with dilation_rate > 1.
padding string, either "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input. When padding="same" and strides=1, the output has the same size as the input.
data_format string, either "channels_last" 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). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
dilation_rate int or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. If only one int is specified, the same dilation rate will be used for all dimensions.
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 input_channel * depth_multiplier.
activation Activation function. If None, no activation is applied.
use_bias bool, if True, bias will be added to the output.
depthwise_initializer An initializer for the depthwise convolution kernel. If None, then the default initializer ("glorot_uniform") will be used.
pointwise_initializer An initializer for the pointwise convolution kernel. If None, then the default initializer ("glorot_uniform") will be used.
bias_initializer An initializer for the bias vector. If None, the default initializer ('"zeros"') 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).
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.

Input shape:

  • If data_format="channels_last": A 4D tensor with shape: (batch_size, height, width, channels)
  • If data_format="channels_first": A 4D tensor with shape: (batch_size, channels, height, width)

Output shape:

  • If data_format="channels_last": A 4D tensor with shape: (batch_size, new_height, new_width, filters)
  • If data_format="channels_first": A 4D tensor with shape: (batch_size, filters, new_height, new_width)

A 4D tensor representing activation(separable_conv2d(inputs, kernel) + bias).

Example:

x = np.random.rand(4, 10, 10, 12)
y = keras.layers.SeparableConv2D(3, 4, 3, 2, activation='relu')(x)
print(y.shape)
(4, 4, 4, 4)

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)