no gpu :-(

<script>
      // Notice there is no 'import' statement. 'tf' is available on the index-page
      // because of the script tag above.
      var noise=[];
      for (var i = 0; i < images.length; i++) {
        noise.push(Math.random());
      }
      var predictionInput=[];
      for (var i = 0; i < (870/6)*(1236/6); i++) {
        predictionInput.push(Math.random());
      }

      var c;
      var ctx;
      const xs = tf.tensor2d(noise,[images.length/((870/6)*(1236/6)), (870/6)*(1236/6)]);
      const ys = tf.tensor2d(images, [images.length/((870/6)*(1236/6)), (870/6)*(1236/6)]);
      var time;
      var epochs;
      var epochPos=0;
      var model;
      var m = model;
      var save
      var stepSize = 0.1;
      window.onload=()=>{


          var c = document.querySelector("#canvas")
          var ctx = c.getContext("2d");



          // Define a model for linear regression.
          model = tf.sequential();
          model.add(tf.layers.dense({units: 256, inputDim:(870/6)*(1236/6), activation:"relu"}));
          model.add(tf.layers.dense({units: 512, activation:"relu"}));
          model.add(tf.layers.dense({units: 512, activation:"relu"}));
          model.add(tf.layers.dense({units: 256, activation:"relu"}));
          model.add(tf.layers.dense({units: (870/6)*(1236/6), activation:"elu"}));

          // Prepare the model for training: Specify the loss and the optimizer.
          model.compile({loss: 'meanSquaredError', optimizer: tf.train.adam(stepSize)});

          // Generate some synthetic data for training.
          //const xs = tf.tensor2d(images.slice(0,images.length-((870/6)/6*(1236/6)/6)), [68, (870/6)/6*(1236/6)/6]);
          //console.log(xs);
          //xs.print()
          //const ys = tf.tensor2d(images.slice((870/6)/6*(1236/6)/6,images.length), [68, (870/6)/6*(1236/6)/6]);
          //ys.print()
        document.querySelector("#steps").onchange=(e) => {
            stepSize = e.target.value;
            model.compile({loss: 'meanSquaredError', optimizer: tf.train.adam(stepSize)});
        };
        document.querySelector("#save").onclick=async() => {
            save = await model.save('downloads://my-model-1');
        };
        document.querySelector("#begin").onclick=()=>{
            console.log(tf.getBackend());
            epochs = document.querySelector("#epochs").value

            // Train the model using the data.

            train(model).then((res) => {
            //m = res.model;
            epochPos+=epochs;
            console.log(res.history.loss);
            // Use the model to do inference on a data point the model hasn't seen before:
            // Open the browser devtools to see the output
            var prediction = model.predict(tf.tensor2d(predictionInput, [1,(870/6)*(1236/6)]));
            //console.log(prediction);
            for(i=0;i<prediction.size;i++)
            {
                ctx.beginPath();
                var color = "rgb("+(prediction.get(0,i)*255-(prediction.get(0,i)*255%1))+","+(prediction.get(0,i)*255-(prediction.get(0,i)*255%1))+","+(prediction.get(0,i)*255-(prediction.get(0,i)*255%1))+")"
                //console.log(color);
                ctx.strokeStyle=color;
                //console.log(i%((870/6))+","+((i-(i%((870/6))))/((870/6))))
                ctx.moveTo(i%((870/6)), (i-(i%((870/6))))/((870/6)));
                ctx.lineTo(i%((870/6))+1, (i-(i%((870/6))))/((870/6)));
                ctx.stroke();
            }

            });
        }
      }
      async function train(mod){
        //console.log(epochs)
        return await mod.fit(xs, ys, {epochs: epochs,shuffle:true,initialEpoch:epochPos,callbacks:{
            onEpochBegin:()=>{time = new Date();},
            onEpochEnd:(num,logs)=>{
                console.log("Time: "+Math.abs(new Date() - time))
                console.log("Epochs: "+num+" of "+epochs+" ("+num/epochs*100+"%)")
                console.log("Loss: "+ logs.loss)
                console.log("Tensors: "+tf.memory().numTensors)
            }
        }});
        //await model.save('downloads://my-model-1');
    }
    </script>