Move Python scripts for building examples into tfjs-examples

deploy.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# Copyright 2018 Google Inc. All Rights Reserved.
+# Copyright 2018 Google LLC. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

iris/build-resources.sh

@@ -0,0 +1,61 @@
+#!/usr/bin/env bash
+
+# Copyright 2018 Google LLC. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+# Builds resources for the Iris demo.
+# Note this is not necessary to run the demo, because we already provide hosted
+# pre-built resources.
+# Usage example: do this from the 'iris' directory:
+# ./build-resources.sh
+
+set -e
+
+DEMO_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+TRAIN_EPOCHS=100
+while true; do
+ if [[ "$1" == "--epochs" ]]; then
+ TRAIN_EPOCHS=$2
+ shift 2
+ elif [[ -z "$1" ]]; then
+ break
+ else
+ echo "ERROR: Unrecognized argument: $1"
+ exit 1
+ fi
+done
+
+RESOURCES_ROOT="${DEMO_DIR}/dist/resources"
+rm -rf "${RESOURCES_ROOT}"
+mkdir -p "${RESOURCES_ROOT}"
+
+# Run Python script to generate the pretrained model and weights files.
+# Make sure you install the tensorflowjs pip package first.
+
+python "${DEMO_DIR}/python/iris.py" \
+ --epochs "${TRAIN_EPOCHS}" \
+ --artifacts_dir "${RESOURCES_ROOT}"
+
+cd ${DEMO_DIR}
+yarn
+yarn build
+
+echo
+echo "-----------------------------------------------------------"
+echo "Resources written to ${RESOURCES_ROOT}."
+echo "You can now run the demo with 'yarn watch'."
+echo "-----------------------------------------------------------"
+echo

iris/index.html

@@ -117,7 +117,8 @@ <h1>TensorFlow.js Layers: Iris Demo</h1>
  </div>
 
  <div class="create-model">
- <button id="load-pretrained">Load pretrained model</button>
+ <button id="load-pretrained-remote" style="display:none">Load hosted pretrained model</button>
+ <button id="load-pretrained-local" style="display:none">Load local pretrained model</button>
  </div>
 
  <div>

iris/index.js

@@ -17,28 +17,12 @@
 
 import * as tf from '@tensorflow/tfjs';
 
-import {getIrisData, IRIS_CLASSES, IRIS_NUM_CLASSES} from './data';
-import {clearEvaluateTable, getManualInputData, loadTrainParametersFromUI, plotAccuracies, plotLosses, renderEvaluateTable, renderLogitsForManualInput, setManualInputWinnerMessage, status, wireUpEvaluateTableCallbacks} from './ui';
+import * as data from './data';
+import * as loader from './loader';
+import * as ui from './ui';
 
 let model;
 
-/**
- * Load pretrained model stored at a remote URL.
- *
- * @return An instance of `tf.Model` with model topology and weights loaded.
- */
-async function loadHostedPretrainedModel() {
- const HOSTED_MODEL_JSON_URL =
- 'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json';
- status('Loading pretrained model from ' + HOSTED_MODEL_JSON_URL);
- try {
- model = await tf.loadModel(HOSTED_MODEL_JSON_URL);
- status('Done loading pretrained model.');
- } catch (err) {
- status('Loading pretrained model failed.');
- }
-}
-
 /**
  * Train a `tf.Model` to recognize Iris flower type.
  *
@@ -53,9 +37,9 @@ async function loadHostedPretrainedModel() {
  * @returns The trained `tf.Model` instance.
  */
 async function trainModel(xTrain, yTrain, xTest, yTest) {
- status('Training model... Please wait.');
+ ui.status('Training model... Please wait.');
 
- const params = loadTrainParametersFromUI();
+ const params = ui.loadTrainParametersFromUI();
 
  // Define the topology of the model: two dense layers.
  const model = tf.sequential();
@@ -79,16 +63,16 @@ async function trainModel(xTrain, yTrain, xTest, yTest) {
  callbacks: {
  onEpochEnd: async (epoch, logs) => {
  // Plot the loss and accuracy values at the end of every training epoch.
- plotLosses(lossValues, epoch, logs.loss, logs.val_loss);
- plotAccuracies(accuracyValues, epoch, logs.acc, logs.val_acc);
+ ui.plotLosses(lossValues, epoch, logs.loss, logs.val_loss);
+ ui.plotAccuracies(accuracyValues, epoch, logs.acc, logs.val_acc);
 
  // Await web page DOM to refresh for the most recently plotted values.
  await tf.nextFrame();
  },
  }
  });
 
- status('Model training complete.');
+ ui.status('Model training complete.');
  return model;
 }
 
@@ -99,25 +83,25 @@ async function trainModel(xTrain, yTrain, xTest, yTest) {
  */
 async function predictOnManualInput(model) {
  if (model == null) {
- setManualInputWinnerMessage('ERROR: Please load or train model first.');
+ ui.setManualInputWinnerMessage('ERROR: Please load or train model first.');
  return;
  }
 
  // Use a `tf.tidy` scope to make sure that WebGL memory allocated for the
  // `predict` call is released at the end.
  tf.tidy(() => {
  // Prepare input data as a 2D `tf.Tensor`.
- const inputData = getManualInputData();
+ const inputData = ui.getManualInputData();
  const input = tf.tensor2d([inputData], [1, 4]);
 
  // Call `model.predict` to get the prediction output as probabilities for
  // the Iris flower categories.
 
  const predictOut = model.predict(input);
  const logits = Array.from(predictOut.dataSync());
- const winner = IRIS_CLASSES[predictOut.argMax(-1).dataSync()[0]];
- setManualInputWinnerMessage(winner);
- renderLogitsForManualInput(logits);
+ const winner = data.IRIS_CLASSES[predictOut.argMax(-1).dataSync()[0]];
+ ui.setManualInputWinnerMessage(winner);
+ ui.renderLogitsForManualInput(logits);
  });
 }
 
@@ -130,39 +114,62 @@ async function predictOnManualInput(model) {
  * [numTestExamples, 3].
  */
 async function evaluateModelOnTestData(model, xTest, yTest) {
- clearEvaluateTable();
+ ui.clearEvaluateTable();
 
  tf.tidy(() => {
  const xData = xTest.dataSync();
  const yTrue = yTest.argMax(-1).dataSync();
  const predictOut = model.predict(xTest);
  const yPred = predictOut.argMax(-1);
- renderEvaluateTable(xData, yTrue, yPred.dataSync(), predictOut.dataSync());
+ ui.renderEvaluateTable(
+ xData, yTrue, yPred.dataSync(), predictOut.dataSync());
  });
 
  predictOnManualInput(model);
 }
 
+const LOCAL_MODEL_JSON_URL = 'http://localhost:1235/resources/model.json';
+const HOSTED_MODEL_JSON_URL =
+ 'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json';
+
 /**
  * The main function of the Iris demo.
  */
 async function iris() {
- const [xTrain, yTrain, xTest, yTest] = getIrisData(0.15);
+ const [xTrain, yTrain, xTest, yTest] = data.getIrisData(0.15);
 
  document.getElementById('train-from-scratch')
  .addEventListener('click', async () => {
  model = await trainModel(xTrain, yTrain, xTest, yTest);
  evaluateModelOnTestData(model, xTest, yTest);
  });
 
- document.getElementById('load-pretrained')
- .addEventListener('click', async () => {
- clearEvaluateTable();
- await loadHostedPretrainedModel();
- predictOnManualInput(model);
- });
+ if (await loader.urlExists(HOSTED_MODEL_JSON_URL)) {
+ ui.status('Model available: ' + HOSTED_MODEL_JSON_URL);
+ const button = document.getElementById('load-pretrained-remote');
+ button.addEventListener('click', async () => {
+ ui.clearEvaluateTable();
+ model = await loader.loadHostedPretrainedModel(HOSTED_MODEL_JSON_URL);
+ predictOnManualInput(model);
+ });
+ // button.style.visibility = 'visible';
+ button.style.display = 'inline-block';
+ }
+
+ if (await loader.urlExists(LOCAL_MODEL_JSON_URL)) {
+ ui.status('Model available: ' + LOCAL_MODEL_JSON_URL);
+ const button = document.getElementById('load-pretrained-local');
+ button.addEventListener('click', async () => {
+ ui.clearEvaluateTable();
+ model = await loader.loadHostedPretrainedModel(LOCAL_MODEL_JSON_URL);
+ predictOnManualInput(model);
+ });
+ // button.style.visibility = 'visible';
+ button.style.display = 'inline-block';
+ }
 
- wireUpEvaluateTableCallbacks(() => predictOnManualInput(model));
+ ui.status('Standing by.');
+ ui.wireUpEvaluateTableCallbacks(() => predictOnManualInput(model));
 }
 
 iris();

iris/loader.js

@@ -0,0 +1,53 @@
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+
+import * as tf from '@tensorflow/tfjs';
+import * as ui from './ui';
+
+/**
+ * Test whether a given URL is retrievable.
+ */
+export async function urlExists(url) {
+ ui.status('Testing url ' + url);
+ try {
+ const response = await fetch(url, {method: 'HEAD'});
+ return response.ok;
+ } catch (err) {
+ return false;
+ }
+}
+
+/**
+ * Load pretrained model stored at a remote URL.
+ *
+ * @return An instance of `tf.Model` with model topology and weights loaded.
+ */
+export async function loadHostedPretrainedModel(url) {
+ ui.status('Loading pretrained model from ' + url);
+ try {
+ const model = await tf.loadModel(url);
+ ui.status('Done loading pretrained model.');
+ // We can't load a model twice due to
+ // https://github.com/tensorflow/tfjs/issues/34
+ // Therefore we remove the load buttons to avoid user confusion.
+ ui.disableLoadModelButtons();
+ return model;
+ } catch (err) {
+ console.error(err);
+ ui.status('Loading pretrained model failed.');
+ }
+}

iris/package.json

@@ -13,15 +13,16 @@
  "vega-embed": "^3.0.0"
  },
  "scripts": {
- "watch": "NODE_ENV=development parcel --no-hmr --open index.html ",
- "build": "NODE_ENV=production parcel build index.html  --no-minify --public-url ./"
+ "watch": "./serve.sh",
+ "build": "NODE_ENV=production parcel build index.html --no-minify --public-url /"
  },
  "devDependencies": {
  "babel-plugin-transform-runtime": "~6.23.0",
  "babel-polyfill": "~6.26.0",
  "babel-preset-env": "~1.6.1",
  "clang-format": "~1.2.2",
- "parcel-bundler": "~1.6.2"
+ "http-server": "~0.10.0",
+ "parcel-bundler": "~1.7.0"
  },
  "babel": {
  "presets": [

iris/python/__init__.py

@@ -0,0 +1,18 @@
+# Copyright 2018 Google LLC. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function