tf.keras.layers.DepthwiseConv2D

2D depthwise convolution layer.

Inherits From: Layer, Operation

tf.keras.layers.DepthwiseConv2D(
    kernel_size,
    strides=(1, 1),
    padding='valid',
    depth_multiplier=1,
    data_format=None,
    dilation_rate=(1, 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
)

Used in the notebooks

Used in the guide

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 2D convolution, depthwise convolution does not mix information across different input channels.

The depth_multiplier argument determines how many filters 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_sizeint or tuple/list of 2 integer, specifying the size of the depthwise convolution window.
stridesint or tuple/list of 2 integer, specifying the stride length of the depthwise convolution. strides > 1 is incompatible with dilation_rate > 1.
paddingstring, either "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input. When padding="same" and strides=1, the output has the same size as the input.
depth_multiplierThe number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to input_channel * depth_multiplier.
data_formatstring, either "channels_last" 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".
dilation_rateint or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution.
activationActivation function. If None, no activation is applied.
use_biasbool, if True, bias will be added to the output.
depthwise_initializerInitializer for the convolution kernel. If None, the default initializer ("glorot_uniform") will be used.
bias_initializerInitializer for the bias vector. If None, the default initializer ("zeros") will be used.
depthwise_regularizerOptional regularizer for the convolution kernel.
bias_regularizerOptional regularizer for the bias vector.
activity_regularizerOptional regularizer function for the output.
depthwise_constraintOptional projection function to be applied to the kernel after being updated by an Optimizer (e.g. used to implement 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.
bias_constraintOptional projection function to be applied to the bias after being updated by an Optimizer.

Input shape:

  • If data_format="channels_last": A 4D tensor with shape: (batch_size, height, width, channels)
  • If data_format="channels_first": A 4D tensor with shape: (batch_size, channels, height, width)

Output shape:

  • If data_format="channels_last": A 4D tensor with shape: (batch_size, new_height, new_width, channels * depth_multiplier)
  • If data_format="channels_first": A 4D tensor with shape: (batch_size, channels * depth_multiplier, new_height, new_width)

A 4D tensor representing activation(depthwise_conv2d(inputs, kernel) + bias).

ValueErrorwhen both strides > 1 and dilation_rate > 1.

Example:

x = np.random.rand(4, 10, 10, 12)
y = keras.layers.DepthwiseConv2D(3, 3, activation='relu')(x)
print(y.shape)
(4, 8, 8, 36)

inputRetrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

outputRetrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
configA Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

View source

symbolic_call(
    *args, **kwargs
)