Class CSVDataset

Groups elements into batches.

It is assumed that each of the incoming dataset elements has the same structure -- i.e. the same set of keys at each location in an object hierarchy. For each key, the resulting Dataset provides a batched element collecting all of the incoming values for that key.

• Incoming primitives are grouped into a 1-D Tensor.
• Incoming Tensors are grouped into a new Tensor where the 0th axis is the batch dimension.
• Incoming arrays are converted to Tensor and then batched.
• A nested array is interpreted as an n-D Tensor, so the batched result has n+1 dimensions.
• An array that cannot be converted to Tensor produces an error.

If an array should not be batched as a unit, it should first be converted to an object with integer keys.

Here are a few examples:

Batch a dataset of numbers:

const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);
await a.forEachAsync(e => e.print());
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Batch a dataset of arrays:

const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);
await b.forEachAsync(e => e.print());
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Batch a dataset of objects:

const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},
  {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},
  {a: 8, b: 18}]).batch(4);
await c.forEachAsync(e => {
  console.log('{');
  for(var key in e) {
    console.log(key+':');
    e[key].print();
  }
  console.log('}');
})
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Doc

Concatenates this Dataset with another.

const a = tf.data.array([1, 2, 3]);
const b = tf.data.array([4, 5, 6]);
const c = a.concatenate(b);
await c.forEachAsync(e => console.log(e));
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Doc

Filters this dataset according to predicate.

const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
  .filter(x => x%2 === 0);
await a.forEachAsync(e => console.log(e));
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Doc

Apply a function to every element of the dataset.

After the function is applied to a dataset element, any Tensors contained within that element are disposed.

const a = tf.data.array([1, 2, 3]);
await a.forEachAsync(e => console.log(e));
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Doc

Maps this dataset through a 1-to-1 transform.

const a = tf.data.array([1, 2, 3]).map(x => x*x);
await a.forEachAsync(e => console.log(e));
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Maps this dataset through an async 1-to-1 transform.

const a =
 tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){
   setTimeout(() => {
     resolve(x * x);
   }, Math.random()*1000 + 500);
 }));
console.log(await a.toArray());
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Creates a Dataset that prefetches elements from this dataset.

Doc

Repeats this dataset count times.

NOTE: If this dataset is a function of global state (e.g. a random number generator), then different repetitions may produce different elements.

const a = tf.data.array([1, 2, 3]).repeat(3);
await a.forEachAsync(e => console.log(e));
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Pseudorandomly shuffles the elements of this dataset. This is done in a streaming manner, by sampling from a given number of prefetched elements.

const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);
await a.forEachAsync(e => console.log(e));
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Creates a Dataset that skips count initial elements from this dataset.

const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);
await a.forEachAsync(e => console.log(e));
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Creates a Dataset with at most count initial elements from this dataset.

const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);
await a.forEachAsync(e => console.log(e));
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Collect all elements of this dataset into an array.

Obviously this will succeed only for small datasets that fit in memory. Useful for testing and generally should be avoided if possible.

const a = tf.data.array([1, 2, 3, 4, 5, 6]);
console.log(await a.toArray());
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Collect all elements of this dataset into an array with prefetching 100 elements. This is useful for testing, because the prefetch changes the order in which the Promises are resolved along the processing pipeline. This may help expose bugs where results are dependent on the order of Promise resolution rather than on the logical order of the stream (i.e., due to hidden mutable state).