Convolutional layers are used with convolutional neural networks (CNNs)

Conv*D

There are three different convolutional layer dimensions:

• Conv1D: Ex temporal convolutions
• Conv2D: Ex spatial convolutions over images
• Conv3D: Ex spatial convolutions over volumes

tf.keras.layers.Conv2D(
    filters,
    kernel_size,
    strides=(1, 1),
    padding="valid",
    data_format=None,
    dilation_rate=(1, 1),
    groups=1,
    activation=None,
    use_bias=True,
    kernel_initializer="glorot_uniform",
    bias_initializer="zeros",
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    **kwargs
)

• Filters: Number of output filers
• Kernel Size: Height and width of the 2D convolutional window. Tuple of ints
• Strides: Specifies the strides (movement of the window) along the height and width.
• Padding:
• 'valid': No padding. Output shape will be smaller than input shape
• 'same': Adds padding. Output shape (height/width, not channels/filters) will be the same as the input
• data_format
• 'channels_last' (default): Expects (batch_size, height, width, channels) as input
• 'channels_first': Expects (batch_size, channels, height, width) as input

Input Shape: batch_shape + (channels, rows, cols) if data format is channels first

Output Shape: batch_shape + (filters, new_rows, new_cols) if data format is channels first. Rows and columns may change due to padding.

SeperableConv*D

Similar to Conv*D layers, but channels are kept separate at first and then mixed at the end. The 2D version is similar to an Inception Block.

• SeparableConv1D
• SeparableConv2D

DepthwiseConv2D

Performs the first half of SeperableConv2D, where channels are kept separate.

Conv*DTranspose (Deconvolution)

"Undoes" a convolutional layer. Is generally used to increase the dimensionality (rows and columns) while decreasing the channel number.

• Conv2DTranspose
• Conv3DTranspose

tf.keras.layers.Conv2DTranspose(
    filters,
    kernel_size,
    strides=(1, 1),
    padding="valid",
    output_padding=None,
    data_format=None,
    dilation_rate=(1, 1),
    activation=None,
    use_bias=True,
    kernel_initializer="glorot_uniform",
    bias_initializer="zeros",
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    **kwargs
)