Deeplearn-rs mnist graph

deeplearn_rs_mnist_graph.rs

let ctx = Rc::new(ga::Context::new());
let ref mut graph = Graph::new(ctx.clone());

let batch_size = 10;

//////////////////////////
// Layer 1

// Input: batch_size samples of rows*columns inputs
let input = graph.add_variable(vec![batch_size, rows*columns], false, 0.0);

// Biased fully connected layer with 300 neurons
let (l1_fcb, _, _) = layers::dense_biased(graph, input, 300,
                                          init::Normal(0.001, 0.005),  // Weights initializer
                                          init::Normal(0.001, 0.005)); // Bias initializer
let l1_out = layers::activation(graph, Relu(l1_fcb));

//////////////////////////
// Layer 2

// Biased fully connected layer with 10 neurons
let (l2_fcb, _, _) = layers::dense_biased(graph, l1_out, 10,
                                          init::Normal(0.001, 0.005),  // Weights initializer
                                          init::Normal(0.001, 0.005)); // Bias initializer
let l2_out = layers::activation(graph, Relu(l2_fcb));

//////////////////////////
// Loss

let (loss_out, train_out) = layers::mse(graph, l2_out);
let loss_d = graph.add_gradient(loss_out); // Create a gradient to apply to the loss function