nkreeger

root=/{pwd}/docker/mesa
project=/{pwd}/docker/mesa/workspace

nkreeger

FROM debian:stretch-backports
LABEL maintainer="Nick Kreeger <kreeger@google.com>"

# Fix locale in debian
RUN apt-get update && apt-get install -y locales && rm -rf /var/lib/apt/lists/* \
    && localedef -i en_US -c -f UTF-8 -A /usr/share/locale/locale.alias en_US.UTF-8
    ENV LANG en_US.utf8

# Add arm arch for build packages
RUN dpkg --add-architecture armhf

nkreeger

version: '2'
services:
    rpi_env:
      build:
        context: .
        dockerfile: Dockerfile
      image: rpi_env:cpu
      env_file:
        - .env
      # devices:

nkreeger

diff --git a/scripts/install.js b/scripts/install.js
index 6587991..3af5a6f 100644
--- a/scripts/install.js
+++ b/scripts/install.js
@@ -37,10 +37,11 @@ const CPU_DARWIN = 'libtensorflow_r1_10_darwin.tar.gz';
 const CPU_LINUX = 'libtensorflow_r1_10_linux_cpu.tar.gz';
 const GPU_LINUX = 'libtensorflow_r1_10_linux_gpu.tar.gz';
 const CPU_WINDOWS = 'libtensorflow_r1_10_windows_cpu.zip';
+const GPU_WINDOWS = 'libtensorflow_r1_10_windows_gpu.zip';
 

nkreeger

function updateHeatmap() {
  rects.data(generateHeatmapData());
  rects
    .attr('x', (coord) => { return scaleX(coord.x) * CONSTANTS.HEATMAP_SIZE; })
    .attr('y', (coord) => { return scaleY(coord.y) * CONSTANTS.HEATMAP_SIZE; })
    .attr('width', CONSTANTS.HEATMAP_SIZE)
    .attr('height', CONSTANTS.HEATMAP_SIZE)
    .style('fill', (coord) => {
      if (coord.strike) {
        return strikeColorScale(coord.value);

nkreeger

function predictZone() {
  const predictions = model.predictOnBatch(predictionMatrix.data);
  const values = predictions.dataSync();

  // Sort each value so the higher prediction is the first element in the array:
  const results = [];
  let index = 0;
  for (let i = 0; i < values.length; i++) {
    let list = [];
    list.push({value: values[index++], strike: 0});

nkreeger

// Trains and reports loss+accuracy for one batch of training data:
async function trainBatch(index) {
  const history = await model.fit(batches[index].x, batches[index].y, {
    epochs: 1,
    shuffle: false,
    validationData: [batches[index].x, batches[index].y],
    batchSize: CONSTANTS.BATCH_SIZE
  });

  // Don't block the UI frame by using tf.nextFrame()

nkreeger

const data = [];
csvData.forEach((values) => {
  // 'logit' data uses the 5 fields:
  const x = [];
  x.push(parseFloat(values.px));
  x.push(parseFloat(values.pz));
  x.push(parseFloat(values.sz_top));
  x.push(parseFloat(values.sz_bot));
  x.push(parseFloat(values.left_handed_batter));
  // The label is simply 'is strike' or 'is ball':

nkreeger

const model = tf.sequential();

// Two fully connected layers with dropout between each:
model.add(tf.layers.dense({units: 24, activation: 'relu', inputShape: [5]}));
model.add(tf.layers.dropout({rate: 0.01}));
model.add(tf.layers.dense({units: 16, activation: 'relu'}));
model.add(tf.layers.dropout({rate: 0.01}));

// Only two classes: "strike" and "ball":
model.add(tf.layers.dense({units: 2, activation: 'softmax'}));

nkreeger