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TensorFlow 1 version
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View source on GitHub
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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 | |
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filters
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Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). |
kernel_size
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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
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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.
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padding
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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.
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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".
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dilation_rate
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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.
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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.
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activation
|
Activation function to use.
If you don't specify anything, no activation is applied (
see keras.activations).
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use_bias
|
Boolean, whether the layer uses a bias vector. |
depthwise_initializer
|
Initializer for the depthwise kernel matrix (
see keras.initializers).
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pointwise_initializer
|
Initializer for the pointwise kernel matrix (
see keras.initializers).
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bias_initializer
|
Initializer for the bias vector (
see keras.initializers).
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depthwise_regularizer
|
Regularizer function applied to
the depthwise kernel matrix (see keras.regularizers).
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pointwise_regularizer
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Regularizer function applied to
the pointwise kernel matrix (see keras.regularizers).
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bias_regularizer
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Regularizer function applied to the bias vector (
see keras.regularizers).
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activity_regularizer
|
Regularizer function applied to
the output of the layer (its "activation") (
see keras.regularizers).
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depthwise_constraint
|
Constraint function applied to
the depthwise kernel matrix (
see keras.constraints).
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pointwise_constraint
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Constraint function applied to
the pointwise kernel matrix (
see keras.constraints).
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bias_constraint
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Constraint function applied to the bias vector (
see keras.constraints).
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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).
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Raises | |
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ValueError
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if padding is "causal".
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ValueError
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when both strides > 1 and dilation_rate > 1.
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