TensorFlow 1 version | View source on GitHub |
Depthwise separable 2D convolution.
Inherits From: Conv2D
tf.keras.layers.DepthwiseConv2D(
kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1,
data_format=None, activation=None, use_bias=True,
depthwise_initializer='glorot_uniform', bias_initializer='zeros',
depthwise_regularizer=None, bias_regularizer=None, activity_regularizer=None,
depthwise_constraint=None, bias_constraint=None, **kwargs
)
Depthwise Separable convolutions consists in performing
just the first step in a depthwise spatial convolution
(which acts on each input channel separately).
The depth_multiplier
argument controls how many
output channels are generated per input channel in the depthwise step.
Arguments | |
---|---|
kernel_size
|
An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. |
strides
|
An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the height and width.
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).
|
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 filters_in * depth_multiplier .
|
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) .
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'.
|
activation
|
Activation function to use.
If you don't specify anything, no activation is applied
(ie. 'linear' activation: a(x) = x ).
|
use_bias
|
Boolean, whether the layer uses a bias vector. |
depthwise_initializer
|
Initializer for the depthwise kernel matrix. |
bias_initializer
|
Initializer for the bias vector. |
depthwise_regularizer
|
Regularizer function applied to the depthwise kernel matrix. |
bias_regularizer
|
Regularizer function applied to the bias vector. |
activity_regularizer
|
Regularizer function applied to the output of the layer (its 'activation'). |
depthwise_constraint
|
Constraint function applied to the depthwise kernel matrix. |
bias_constraint
|
Constraint function applied to the bias vector. |
Input shape:
4D tensor with shape:
[batch, channels, rows, cols]
if data_format='channels_first'
or 4D tensor with shape:
[batch, rows, cols, channels]
if data_format='channels_last'.
Output shape:
4D tensor with shape:
[batch, filters, new_rows, new_cols]
if data_format='channels_first'
or 4D tensor with shape:
[batch, new_rows, new_cols, filters]
if data_format='channels_last'.
rows
and cols
values might have changed due to padding.