SeparableConv2D layer

[source]

SeparableConv2D class

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
)

Depthwise separable 2D convolution.

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

• filters: Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
• 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 the 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.
• 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 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.
• 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.
• 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: An initializer for the depthwise convolution kernel (see keras.initializers). If None, then the default initializer ('glorot_uniform') will be used.
• pointwise_initializer: An initializer for the pointwise convolution kernel (see keras.initializers). If None, then the default initializer ('glorot_uniform') will be used.
• bias_initializer: An initializer for the bias vector. If None, the default initializer ('zeros') will be used (see keras.initializers).
• depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix (see keras.regularizers).
• pointwise_regularizer: Regularizer function applied to the pointwise 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).
• pointwise_constraint: Constraint function applied to the pointwise 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, 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).

Raises

• ValueError: if padding is "causal".