Verilog-NN : Variational Aueo Encoder : VAE

 

module VAE (
  input clk,
  input [15:0] x,
  output [15:0] y
);

// Import required libraries and modules

// Define inputs and outputs

// Create encoding layer

// Create decoding layer

// Create loss function

// Train the model

// Test the model

endmodule

 

https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464

The mostly complete chart of Neural Networks, explained

 

module VAE (
  input clk,
  input rst,
  input [15:0] x, // input data
  output reg [15:0] z // output encoded data
);

// Parameters
parameter N = 16; // number of input units
parameter M = 8; // number of hidden units

// Variables
reg [15:0] h; // hidden layer

// Weights
wire [N-1:0] W_enc; // encoding weights
wire [M-1:0] b_enc; // encoding biases
wire [M-1:0] W_dec; // decoding weights
wire [N-1:0] b_dec; // decoding biases

// Encoding layer
assign h = W_enc * x + b_enc;

// Latent space
assign z = mean(h);

// Decoding layer
assign y = W_dec * h + b_dec;

// Loss function
always @(*) begin
  // compute reconstruction loss
  // ...
end

endmodule

 

 

https://github.com/kvfrans/variational-autoencoder

GitHub - kvfrans/variational-autoencoder: generate MNIST using a Variational Autoencoder