DepthwiseConv2D layer

[source]

DepthwiseConv2D class

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
)

Depthwise 2D convolution.

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 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.

Arguments

• 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. Current implementation only supports equal length strides in row and column 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). When unspecified, uses image_data_format value found in your TF-Keras config file at ~/.keras/keras.json (if exists) else 'channels_last'. Defaults to '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.
• 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).

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, 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.

Returns

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

Raises

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