Tensor
Modeled after the Tensor class in Torch, the Tensor package (written in Scala and leveraging Intel MKL) in BigDL provides numeric computing support for the deep learning applications (e.g., the input, output, weight, bias and gradient of the neural networks).
A Tensor is essentially a multi-dimensional array of numeric types (Float or Double), you can import the numeric implicit objects(com.intel.analytics.bigdl.numeric.NumericFloat or com.intel.analytics.bigdl.numeric.NumericDouble), to specify the numeric type you want.
Scala example:
You may check it out in the interactive Scala shell (by typing scala -cp bigdl_SPARKVERSION-BIGDLVERSION-SNAPSHOT-jar-with-dependencies.jar), for instance:
scala> import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.Tensor
scala> import com.intel.analytics.bigdl.numeric.NumericFloat
import com.intel.analytics.bigdl.numeric.NumericFloat
scala> import com.intel.analytics.bigdl.utils.T
import com.intel.analytics.bigdl.utils.T
scala> val tensor = Tensor(2, 3)
tensor: com.intel.analytics.bigdl.tensor.Tensor =
0.0 0.0 0.0
0.0 0.0 0.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
Tensor can be created with existing data.
scala> val a = Tensor(T(
| T(1f, 2f, 3f),
| T(4f, 5f, 6f)))
a: com.intel.analytics.bigdl.tensor.Tensor[Float] =
1.0 2.0 3.0
4.0 5.0 6.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 2x3]
scala> val b = Tensor(T(
| T(6f, 5f, 4f),
| T(3f, 2f, 1f)))
b: com.intel.analytics.bigdl.tensor.Tensor[Float] =
6.0 5.0 4.0
3.0 2.0 1.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 2x3]
+ - * / can be applied to tensor. When the second parameter is a constant value, + - * * is element-wise operation. But when the second parameter is a tensor, + - / is element-wise operation to the tensor too, but * is a matrix multiply on two 2D tensors.
scala> a + 1
res: com.intel.analytics.bigdl.tensor.Tensor[Float] =
2.0 3.0 4.0
5.0 6.0 7.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 2x3]
scala> a + b
res: com.intel.analytics.bigdl.tensor.Tensor[Float] =
7.0 7.0 7.0
7.0 7.0 7.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
scala> a - b
res: com.intel.analytics.bigdl.tensor.Tensor[Float] =
-5.0 -3.0 -1.0
1.0 3.0 5.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
scala> a * b.t
res: com.intel.analytics.bigdl.tensor.Tensor[Float] =
28.0 10.0
73.0 28.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x2]
scala> a / b
res: com.intel.analytics.bigdl.tensor.Tensor[Float] =
0.16666667 0.4 0.75
1.3333334 2.5 6.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]
For more API, navigate to API Guide/Full API docs on side bar.
SparseTensor
To describe an SparseTensor, we need indices, values, and shape:
indices means the indices of non-zero elements; values means the values of the non-zero elements;
shape means the dense shape of this SparseTensor.
For example, an 2D 3x4 DenseTensor:
1, 0, 0, 4
0, 2, 0, 0
0, 0, 3, 0
It's sparse representation should be
indices(0) = Array(0, 0, 1, 2)
indices(1) = Array(0, 3, 1, 2)
values = Array(1, 4, 2, 3)
shape = Array(3, 4)
This 2D SparseTensor representation is similar to zero-based coordinate matrix storage format.
Scala example:
scala> import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.Tensor
scala> import com.intel.analytics.bigdl.numeric.NumericFloat
import com.intel.analytics.bigdl.numeric.NumericFloat
scala> val indices = Array(Array(0, 0, 1, 2), Array(0, 3, 1, 2))
indices: Array[Array[Int]] = Array(Array(0, 0, 1, 2), Array(0, 3, 1, 2))
scala> val values = Array(1, 4, 2, 3)
values: Array[Int] = Array(1, 4, 2, 3)
scala> val shape = Array(3, 4)
shape: Array[Int] = Array(3, 4)
scala> val sparseTensor = Tensor.sparse(indices, values, shape)
sparseTensor: com.intel.analytics.bigdl.tensor.Tensor[Int] =
(0, 0) : 1
(0, 3) : 4
(1, 1) : 2
(2, 2) : 3
[com.intel.analytics.bigdl.tensor.SparseTensor of size 3x4]
scala> val denseTensor = Tensor.dense(sparseTensor)
denseTensor: com.intel.analytics.bigdl.tensor.Tensor[Int] =
1 0 0 4
0 2 0 0
0 0 3 0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 3x4]
Table
Modeled after the Table class in Torch, the Table class (defined in package com.intel.analytics.bigdl.utils) is widely used in BigDL (e.g., a Table of Tensor can be used as the input or output of neural networks). In essence, a Table can be considered as a key-value map, and there is also a syntax sugar to create a Table using T() in BigDL.
Scala example:
import com.intel.analytics.bigdl.utils.T
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.numeric.NumericFloat
println(T(Tensor(2,2).fill(1), Tensor(2,2).fill(2)))
Output is
{
2: 2.0 2.0
2.0 2.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 2x2]
1: 1.0 1.0
1.0 1.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 2x2]
}
Sample
A Sample represent one record of your data set. One record contains feature and label, feature is one tensor or a few tensors; while label is one tensor or a few tensors, and it may be empty in testing or unsupervised learning. For example, one image and its category in image classification, one word in word2vec and one sentence and its label in RNN language model are all Sample.
Every Sample is actually a set of tensors, and them will be transformed to the input/output of the model. For example, in the case of image classification, a Sample have two tensors. One is 3D tensor representing a image, another is a 1-element tensor representing its category. For the 1-element label, you also can use a T instead of tensor.
Scala example:
import com.intel.analytics.bigdl.dataset.Sample
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.numeric.NumericFloat
val image = Tensor(3, 32, 32).rand
val label = 1f
val sample = Sample(image, label)
Python example:
from bigdl.util.common import Sample
import numpy as np
image = np.random.rand(3, 32, 32)
label = np.array(1)
sample = Sample.from_ndarray(image, label)
MiniBatch
MiniBatch is a data structure to feed input/target to model in Optimizer. It provide getInput() and getTarget() function to get the input and target in this MiniBatch.
In almost all the cases, BigDL's default MiniBatch class can fit user's requirement. Just create your RDD[Sample] and pass it to Optimizer. If MiniBatch can't meet your requirement, you can implement your own MiniBatch class by extends MiniBatch.
MiniBatch can be created by MiniBatch(nInputs: Int, nOutputs: Int), nInputs means number of inputs, nOutputs means number of outputs. And you can use set(samples: Seq[Sample[T]) to fill the content in this MiniBatch. If you Samples are not the same size, you can use PaddingParam to pad the Samples to the same size.
Scala example:
import com.intel.analytics.bigdl.dataset.Sample
import com.intel.analytics.bigdl.dataset.MiniBatch
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.numeric.NumericFloat
val samples = Array.tabulate(5)(i => Sample(Tensor(1, 3, 3).fill(i), i + 1f))
val miniBatch = MiniBatch(1, 1).set(samples)
println(miniBatch.getInput())
println(miniBatch.getTarget())
Output is
(1,1,.,.) =
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
(2,1,.,.) =
1.0 1.0 1.0
1.0 1.0 1.0
1.0 1.0 1.0
(3,1,.,.) =
2.0 2.0 2.0
2.0 2.0 2.0
2.0 2.0 2.0
(4,1,.,.) =
3.0 3.0 3.0
3.0 3.0 3.0
3.0 3.0 3.0
(5,1,.,.) =
4.0 4.0 4.0
4.0 4.0 4.0
4.0 4.0 4.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 5x1x3x3]
1.0
2.0
3.0
4.0
5.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 5x1]
If you Samples are not the same size, you can use PaddingParam to pad the Samples to the same size.
import com.intel.analytics.bigdl.dataset._
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.numeric.NumericFloat
val sample1 = Sample(Tensor.range(1, 6, 1).resize(2, 3), 1f)
val sample2 = Sample(Tensor.range(7, 9, 1).resize(1, 3), 2f)
val sample3 = Sample(Tensor.range(10, 18, 1).resize(3, 3), 3f)
val samples = Array(sample1, sample2, sample3)
val featurePadding = PaddingParam(Some(Array(Tensor(T(-1f, -2f, -3f)))), FixedLength(Array(4)))
val labelPadding = PaddingParam[Float](None, FixedLength(Array(4)))
val miniBatch = MiniBatch(1, 1, Some(featurePadding), Some(labelPadding)).set(samples)
println(miniBatch.getInput())
println(miniBatch.getTarget())
Output is
(1,.,.) =
1.0 2.0 3.0
4.0 5.0 6.0
-1.0 -2.0 -3.0
-1.0 -2.0 -3.0
(2,.,.) =
7.0 8.0 9.0
-1.0 -2.0 -3.0
-1.0 -2.0 -3.0
-1.0 -2.0 -3.0
(3,.,.) =
10.0 11.0 12.0
13.0 14.0 15.0
16.0 17.0 18.0
-1.0 -2.0 -3.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 3x4x3]
1.0 0.0 0.0 0.0
2.0 0.0 0.0 0.0
3.0 0.0 0.0 0.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 3x4]
DataSet
DataSet is a set of data which is used in the model optimization process. You can use DataSet.array() and DataSet.rdd() function to create a Dataset. The DataSet can be accessed in a random data sample sequence. In the training process, the data sequence is a looped endless sequence. While in the validation process, the data sequence is a limited length sequence. User can use the data() method to get the data sequence.
Notice: In most case, we recommend using a RDD[Sample] for Optimizer. Only when you want to write an application with some advanced optimization, using DataSet directly is recommended.
Scala example:
import com.intel.analytics.bigdl.utils.T
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.numeric.NumericFloat
import com.intel.analytics.bigdl.dataset.DataSet
val tensors = Array.tabulate(5)(i => Tensor(1, 3, 3).fill(i))
val dataset = DataSet.array(tensors) // Local model, just for testing and example.
dataset.shuffle()
val iter = dataset.data(false)
while (iter.hasNext) {
val d = iter.next()
println(d)
}
Output may be
(1,.,.) =
4.0 4.0 4.0
4.0 4.0 4.0
4.0 4.0 4.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1x3x3]
(1,.,.) =
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1x3x3]
(1,.,.) =
2.0 2.0 2.0
2.0 2.0 2.0
2.0 2.0 2.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1x3x3]
(1,.,.) =
1.0 1.0 1.0
1.0 1.0 1.0
1.0 1.0 1.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1x3x3]
(1,.,.) =
3.0 3.0 3.0
3.0 3.0 3.0
3.0 3.0 3.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1x3x3]