tf.keras.layers.SeparableConv2D
Depthwise separable 2D convolution.
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
)
Separable convolutions consist of first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes the resulting output channels. The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step.
Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.
| Arguments | |
|---|---|
filters |
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). |
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). "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_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, 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 |
An integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides 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 filters_in * depth_multiplier. |
activation |
Activation function to use. If you don't specify anything, no activation is applied ( see keras.activations). |
use_bias |
Boolean, whether the layer uses a bias vector. |
depthwise_initializer |
Initializer for the depthwise kernel matrix ( see keras.initializers). |
pointwise_initializer |
Initializer for the pointwise kernel matrix ( see keras.initializers). |
bias_initializer |
Initializer for the bias vector ( see keras.initializers). |
depthwise_regularizer |
Regularizer function applied to the depthwise kernel matrix (see keras.regularizers). |
pointwise_regularizer |
Regularizer function applied to the pointwise kernel matrix (see keras.regularizers). |
bias_regularizer |
Regularizer function applied to the bias vector ( see keras.regularizers). |
activity_regularizer |
Regularizer function applied to the output of the layer (its "activation") ( see keras.regularizers). |
depthwise_constraint |
Constraint function applied to the depthwise kernel matrix ( see keras.constraints). |
pointwise_constraint |
Constraint function applied to the pointwise kernel matrix ( see keras.constraints). |
bias_constraint |
Constraint function applied to the bias vector ( see keras.constraints). |
Input shape:
4D tensor with shape: (batch_size, channels, rows, cols) if data_format='channels_first' or 4D tensor with shape: (batch_size, rows, cols, channels) if data_format='channels_last'.
Output shape:
4D tensor with shape: (batch_size, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (batch_size, new_rows, new_cols, filters) if data_format='channels_last'. rows and cols values might have changed due to padding.
| Returns | |
|---|---|
A tensor of rank 4 representing activation(separableconv2d(inputs, kernel) + bias). |
| Raises | |
|---|---|
ValueError |
if padding is "causal". |
ValueError |
when both strides > 1 and dilation_rate > 1. |
© 2020 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/versions/r2.4/api_docs/python/tf/keras/layers/SeparableConv2D