caisq/breaking_API_changes.md

## breaking_API_changes.md

      
    Raw
  

              breaking_API_changes.md
            
          
    1. Model-loading function name changes

Rationale: Indicate type of model being loaded more explicitly in code.
For  models converted from Keras and models saved from TensorFlow.js itself:

// Before: 
await tf.loadModel('http://server/model.json');

// After:
await tf.loadLayersModel('http://server/model.json');
For models converted from TensorFlow SavedModels, Session Bundles, and Frozen Models:

// Before:
await tf.loadFrozenModel('http://server/model.pb', 'http://server/weights-manifest.json');

// After:
await tf.loadGraphModel('http://server/model.json');
// Also note the fact that models converted from TensorFlow now uses a single file
// to hold the model topology and the manifest of weights.
2. Model-loading function argument consolidation

Rationale: Consolidate the growing list of optional arguments of the function.
// Before:
const strict = false;
await tf.loadModel('http://server/model.json', strict);

// After:
await tf.loadLayersModel('http://server/model.json', {strict: false});
3. tf.io.browserHTTPRequest() argument consolidation

Rationale: Consolidate the growing list of optional arguments of the function.
const requestInit = {credentials: 'include'};
const fetchFunc = myCustomFetchFunc;

// Before:
await tf.loadModel(tf.io.browserHTTPRequest(
    'http://server/model.json', requestInit, null, fetchFunc));

// After:
await tf.loadLayersModel(tf.io.browserHTTPRequest('http://server/model.json', {
  requestInit,
  fetchFunc,
  onProgress: fraction => console.log(`Model loading is ${fraction * 100}% done`)
}));
// Also note the function name change and the new `onProgress` field.
4. Layers API model: name change

Rationale: Reflect the exact type of model; avoid confusion with models from TensorFlow GraphDef-based models.
// Before:
console.log(myModel instanceof tf.Model);

// After:
console.log(myModel instanceof tf.LayersModel);
5. Tensor.get() is removed; Use Tensor.array() instead.

Rationale: Repeated get() call is a common mistake that harms performance.
const x = tf.randomNormal([3, 3]);

// Before:
console.log(x.get(2, 1));

// After:
const nestedArray = await x.array();
console.log(nestedArray[2][1]):
6. Tensor.buffer() is now async

Rationale: Encourage async usage to reduce the likelihood of blocking UI.
const x = tf.randomNormal([3, 3]);

// Before:
const myBuffer = x.buffer();

// After:
const myBuffer = await x.buffer();
// or
const myBuffer = x.bufferSync();
7. Put fromPixels() and toPixels() under the tf.browser namespace

Rationale: Make it clear that these functions don't work in Node.js.
// Before:
const imageTensor = tf.fromPixels(canvasElement1);
await tf.toPixels(imageTensor, canvasElement2);

// After:
const imageTensor = tf.browser.fromPixels(canvasEleemnt1);
await tf.browser.toPixels(imageTensor, canvasElement2);
8. tf.batchNormalization() is replaced by tf.batchNorm(); Adjust argument order

Rationale: To be consistent with TensorFlow (Python) and more logical.
// Before:
tf.batchNormalization(x, mean, variance, varianceEpsilon, scale, offset);

// After:
tf.batchNorm(x, mean, variance, offset, scale, varianceEpisilon);
9. Error message argument of tf.util.assert() is now a function that returns a string

Rationale: Avoid unnecessary overhead of string creation.
// Before:
tf.util.assert(x.rank >= 3, `x is required to be 3D or higher, but is ${x.rank}D`);

// After:
tf.util.assert(x.rank >= 3, () => `x is required to be 3D or higher, but is ${x.rank}D`);
10. tf.data.Dataset.forEach() is renamed to tf.data.Dataset.forEachSync()

Rationale: Make it clear that the method is async. Avoid confusion with the built-in forEach construct.
// Before:
await myDataset.forEach(item => console.log(item));

// After:
await myDataset.forEachAsync(item => console.log(item));
11. tf.LayersModel.fitDataset() now expects each item to be an object with two fields: xs and ys, instead of an array of two elements.

Rationale: Make it explicit what are the features and what are the labels (targets). Avoid surprising behavior during batching.
const model = tf.sequential({layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

const xs = [[1, 2, 3], [4, 5, 6], [7, 8, 9]];
const ys = [[0], [1], [2]];
const xDataset = tf.data.array(xs).map(x => tf.tensor(x));
const yDataset = tf.data.array(ys).map(y => tf.tensor(y));

// Before:
const dataset = tf.data.zip([xDataset, yDataset]).batch(2);
await model.fitDataset(dataset, {epochs: 4});

// After:
const dataset = tf.data.zip({xs: xDataset, ys: yDataset}).batch(2);
// Note that the features are specified explicitly by the key 'xs' and
// the labels (targets) by the key 'ys'.
await model.fitDataset(dataset, {epochs: 4});
12. tfjs-vis: always put the HTML element the first in argument list

Rationale: Consistency among all tfjs-vis functions.
// Before:
tfvis.render.linechart(dataToPlot, divElement, options);
tfvis.render.scatterplot(dataToPlot, divElement, options);
tfvis.render.barchart(dataToPlot, divElement, options);
tfvis.render.histogram(dataToPlot, divElement, options);
tfvis.render.heatmap(dataToPlot, divElement);

// After:
tfvis.render.linechart(divElement, dataToPlot, options);
tfvis.render.scatterplot(divElement, dataToPlot, options);
tfvis.render.barchart(divElement, dataToPlot, options);
tfvis.render.histogram(divElement, dataToPlot, options);
tfvis.render.heatmap(divElement, dataToPlot);
13. tfjs-vis: confusionMatrix argument changes

Rationale: Succinctness and clarity.
// Before:
tfvis.show.confusionMatrix(divElement, confusionMatrix, classNames);

// After:
tfvis.show.confusionMatrix(divElement, {
  values: confusionMatrix, 
  tickLabels: classNames
});
14. tfjsvis: tfvis.render.heatmap() and tfvis.show.confusionMatrix(): rename labels as tickLabels

Rationale: Avoid confusion between axis labels and tick labels.
// Before:
tfvis.show.confusionMatrix(divElement, {
  values,
  labels: ['apple', 'banana', 'orange']
});
tfvis.render.heatmap(divElement, {
  values,
  xLabels: ['x1', 'x2', 'x3'],
  yLabels: ['y1', 'y2', 'y3']
});

// After:
tfvis.show.confusionMatrix(divElement, {
  values,
  tickLabels: ['apple', 'banana', 'orange']
});
tfvis.render.heatmap(divElement, {
  values,
  xTickLabels: ['x1', 'x2', 'x3'],
  yTickLabels: ['y1', 'y2', 'y3']
});
15. tf.data.generator() has been renamed to tf.data.func()

Rationale: In v1.0.0, tf.data.generator() expects an actual JavaScript generator. In 0.x, it took a plain function and hence was a misnomer.
let i = 0;

// Before:
const gen = tf.data.generator(() => ({value: ++i, done: i === 10}));

// After:
const gen = tf.data.func(() => ({value: ++i, done: i === 10}));