Wrapper for a convolutional layer. The dimensions of the convolution operation are inferred from the shape of the input data. This shape must follow the pattern (batch_shape, x, [y, [z, ]], channel) where dimensions y and z are optional, and channel will be either 1 for grayscale images or generally 3 for colored ones.

conv(filters, kernel_size, padding = "same", max_pooling = NULL,
average_pooling = NULL, upsampling = NULL, activation = "linear")

## Arguments

filters Number of filters learned by the layer Integer or list of integers indicating the size of the weight matrices to be convolved with the image One of "valid" or "same" (case-insensitive). See layer_conv_2d for more details NULL or an integer indicating the reduction ratio for a max pooling operation after the convolution NULL or an integer indicating the reduction ratio for an average pooling operation after the convolution NULL or an integer indicating the augmentation ratio for an upsampling operation after the convolution Optional, string indicating activation function (linear by default)

## Value

A construct with class "ruta_network"

Other neural layers: dense, dropout, input, layer_keras, output, variational_block
# Sample convolutional autoencoder
conv(1, 3, activation = "sigmoid")