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TensorFlow 1 version
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View source on GitHub
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Depthwise separable 1D convolution.
tf.keras.layers.SeparableConv1D(
filters, kernel_size, strides=1, padding='valid', data_format=None,
dilation_rate=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.
Arguments | |
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filters
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Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). |
kernel_size
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A single integer specifying the spatial dimensions of the filters. |
strides
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A single integer specifying the strides
of the convolution.
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", "same", or "causal" (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. "causal" results in causal
(dilated) convolutions, e.g. output[t] does not depend on input[t+1:].
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data_format
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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, length, channels) while channels_first corresponds to
inputs with shape (batch_size, channels, length).
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dilation_rate
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A single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any dilation_rate value != 1 is
incompatible with specifying any stride value != 1.
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depth_multiplier
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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.
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activation
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Activation function to use.
If you don't specify anything, no activation is applied (
see keras.activations).
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use_bias
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Boolean, whether the layer uses a bias. |
depthwise_initializer
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An initializer for the depthwise convolution kernel (
see keras.initializers).
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pointwise_initializer
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An initializer for the pointwise convolution kernel (
see keras.initializers).
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bias_initializer
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An initializer for the bias vector. If None, the default
initializer will be used (see keras.initializers).
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depthwise_regularizer
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Optional regularizer for the depthwise
convolution kernel (see keras.regularizers).
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pointwise_regularizer
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Optional regularizer for the pointwise
convolution kernel (see keras.regularizers).
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bias_regularizer
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Optional regularizer for the bias vector (
see keras.regularizers).
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activity_regularizer
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Optional regularizer function for the output (
see keras.regularizers).
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depthwise_constraint
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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 (
see keras.constraints).
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pointwise_constraint
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Optional projection function to be applied to the
pointwise kernel after being updated by an Optimizer (
see keras.constraints).
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bias_constraint
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Optional projection function to be applied to the
bias after being updated by an Optimizer (
see keras.constraints).
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trainable
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Boolean, if True the weights of this layer will be marked as
trainable (and listed in layer.trainable_weights).
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Input shape:
3D tensor with shape:
(batch_size, channels, steps) if data_format='channels_first'
or 5D tensor with shape:
(batch_size, steps, channels) if data_format='channels_last'.
Output shape:
3D tensor with shape:
(batch_size, filters, new_steps) if data_format='channels_first'
or 3D tensor with shape:
(batch_size, new_steps, filters) if data_format='channels_last'.
new_steps value might have changed due to padding or strides.
Returns | |
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A tensor of rank 3 representing
activation(separableconv1d(inputs, kernel) + bias).
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Raises | |
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ValueError
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when both strides > 1 and dilation_rate > 1.
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