tf.keras.layers.DepthwiseConv1D

Depthwise 1D convolution.

Inherits From: Layer, Module

tf.keras.layers.DepthwiseConv1D(
    kernel_size,
    strides=1,
    padding='valid',
    depth_multiplier=1,
    data_format=None,
    dilation_rate=1,
    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 convolution is a type of convolution in which each input channel is convolved with a different kernel (called a depthwise kernel). You can understand depthwise convolution as the first step in a depthwise separable convolution.

It is implemented via the following steps:

  • Split the input into individual channels.
  • Convolve each channel with an individual depthwise kernel with depth_multiplier output channels.
  • Concatenate the convolved outputs along the channels axis.

Unlike a regular 1D convolution, depthwise convolution does not mix information across different input channels.

The depth_multiplier argument determines how many filter are applied to one input channel. As such, it controls the amount of output channels that are generated per input channel in the depthwise step.

kernel_size An integer, specifying the height and width of the 1D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
strides An integer, 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 with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
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_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 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.
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). If None, the default initializer ('glorot_uniform') will be used.
bias_initializer Initializer for the bias vector (see keras.initializers). If None, the default initializer ('zeros') will be used.
depthwise_regularizer Regularizer function applied to the depthwise 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).
bias_constraint Constraint function applied to the bias vector (see keras.constraints).

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'.

4D tensor with shape: [batch_size, channels * depth_multiplier, new_rows, new_cols] if data_format='channels_first' or 4D tensor with shape: [batch_size, new_rows, new_cols, channels * depth_multiplier] if data_format='channels_last'. rows and cols values might have changed due to padding.

A tensor of rank 4 representing activation(depthwiseconv2d(inputs, kernel) + bias).

ValueError if padding is "causal".
ValueError when both strides > 1 and dilation_rate > 1.

Methods

convolution_op

View source

convolution_op(
    inputs, kernel
)