Similar to the TensorFlow Python API, by Google, TensorFlow for Scala provides multiple APIs. The lowest level API – Core API – provides you with complete programming control. the core API is suitable for machine learning researchers and others who require fine levels of control over their models. The higher level APIs are built on top of the Core API. These higher level APIs are typically easier to learn and use. In addition, the higher level APIs make repetitive tasks easier and more consistent between different users. A high-level API like the Learn API helps you manage datasets, models, (distributed) training, and inference.
The main APIs of TensorFlow for Scala introduced in this guide are:
- Tensor API: Provides a simple way for manipulating tensors and performing computations involving tensors. This is similar in functionality to the NumPy library used by Python programmers.
- Learn API: High-level interface for creating, training, and using neural networks. This is similar in functionality to the Keras library used by Python programmers, with the main difference being that it is strongly-typed and offers a much richer functional interface for building neural networks. Furthermore, it supports distributed training in a way that is very similar to the TensorFlow Estimators API.
- Core API: Low-level graph construction interface, similar to that offered by the TensorFlow Python API, with the main difference being that this interface is strongly-typed wherever possible.
The fact that this library is strongly-typed is mentioned a couple times in the above paragraph and that’s because it is a very important feature. It means that many problems with the code you write will show themselves at compile time, which means that your chances of running into the experience of waiting for a neural network to train for a week only to find out that your evaluation code crashed and you lost everything, decrease significantly.
This guide starts with an introduction of the Tensor API and goes from high-level to low-level concepts as you progress. Concepts such as the TensorFlow graph and sessions only appear in the Core API section.
NOTE: This guide borrows a lot pf material from the official Python API documentation of TensorFlow and adapts it for the purposes of TensorFlow for Scala. It also introduces a lot of new constructs specific to this library.
TensorFlow, as the name indicates, is a framework to define and run computations involving tensors. A tensor is a
generalization of vectors and matrices to potentially higher dimensions. Internally, TensorFlow represents tensors as
n-dimensional arrays of some underlying data type. A
Tensor has a
which corresponds to 32-bit floating point numbers) and a
Shape (that is, the number of dimensions it has and
the size of each dimension – e.g.,
Shape(10, 2) which corresponds to a matrix with 10 rows and 2 columns) associated
with it. Each element in the
Tensor has the same data type. For example, the following code creates an
integer tensor filled with zeros with shape
[2, 5] (i.e., a two-dimensional array holding integer values, where the
first dimension size is 2 and the second is 5):
scala> val tensor = Tensor.zeros(INT32, Shape(2, 5)) tensor: org.platanios.tensorflow.api.tensors.Tensor = INT32[2, 5]
You can print the contents of a tensor as follows:
scala> tensor.summarize(flattened = true) res0: String = INT32[2, 5]: [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
As already mentioned, tensors have a data type. Various numeric data types are supported, as well as strings (i.e.,
tensors containing strings are supported). It is not possible to have a
Tensor with more than one data type.
It is possible, however, to serialize arbitrary data structures as strings and store those in
The list of all supported data types is:
- STRING: String.
- BOOLEAN: Boolean.
- FLOAT16: 16-bit half-precision floating-point.
- FLOAT32: 32-bit single-precision floating-point.
- FLOAT64: 64-bit double-precision floating-point.
- BFLOAT16: 16-bit truncated floating-point.
- COMPLEX64: 64-bit single-precision complex.
- COMPLEX128: 128-bit double-precision complex.
- INT8: 8-bit signed integer.
- INT16: 16-bit signed integer.
- INT32: 32-bit signed integer.
- INT64: 64-bit signed integer.
- UINT8: 8-bit unsigned integer.
- UINT16: 16-bit unsigned integer.
- QINT8: Quantized 8-bit signed integer.
- QINT16: Quantized 16-bit signed integer.
- QINT32: Quantized 32-bit signed integer.
- QUINT8: Quantized 8-bit unsigned integer.
- QUINT16: Quantized 16-bit unsigned integer.
- RESOURCE: Handle to a mutable resource.
- VARIANT: Variant.
It is also possible to cast
Tensors from one data type to another using the
val floatTensor = Tensor(FLOAT32, 1, 2, 3) // Floating point vector containing the elements: 1.0f, 2.0f, and 3.0f floatTensor.cast(INT32) // Integer vector containing the elements: 1, 2, and 3 tfi.cast(floatTensor, INT32) // Integer vector containing the elements: 1, 2, and 3
NOTE: In general, all tensor-supported operations can be accessed as direct methods/operators of the
Tensor object, or as static methods defined in the
tfi package, which stands for TensorFlow Imperative
(given the imperative nature of that API).
Tensor’s data type can be inspected using:
floatTensor.dataType // Returns FLOAT32
When creating a
Tensor from a Scala objects you may optionally specify the data type. If you don’t,
TensorFlow chooses a data type that can represent your data. It converts Scala integers to
INT32 and Scala floating
point numbers to either
FLOAT64 depending on their precision.
Tensor(1, 2, 3) // INT32 tensor Tensor(1, 2L, 3) // INT64 tensor Tensor(2.4f, -0.1f) // FLOAT32 tensor Tensor(0.6f, 1.0) // FLOAT64 tensor
The rank of a
Tensor is its number of dimensions. Synonyms for rank include order or degree or
n-dimension. Note that rank in TensorFlow is not the same as matrix rank in mathematics. As the following table shows,
each rank in TensorFlow corresponds to a different mathematical entity:
|0||Scalar (magnitude only)|
|1||Vector (magnitude and direction)|
|2||Matrix (table of numbers)|
|3||3-Tensor (cube of numbers)|
|n||n-Tensor (you get the idea)|
val t0 = Tensor.ones(INT32, Shape()) // Creates a scalar equal to the value 1 val t1 = Tensor.ones(INT32, Shape(10)) // Creates a vector with 10 elements, all of which are equal to 1 val t2 = Tensor.ones(INT32, Shape(5, 2)) // Creates a matrix with 5 rows with 2 columns // You can also create tensors in the following way: val t3 = Tensor(2.0, 5.6) // Creates a vector that contains the numbers 2.0 and 5.6 val t4 = Tensor(Tensor(1.2f, -8.4f), Tensor(-2.3f, 0.4f)) // Creates a matrix with 2 rows and 2 columns
A rank of a tensor can be obtained in one of two ways:
t4.rank // Returns the value 2 tfi.rank(t4) // Also returns the value 2
Indexing / Slicing
Similar to NumPy, tensors can be indexed/sliced in various ways:
An indexer can be one of:
Ellipsis: Corresponds to a full slice over multiple dimensions of a tensor. Ellipses are used to represent zero or more dimensions of a full-dimension indexer sequence.
NewAxis: Corresponds to the addition of a new dimension.
Slice: Corresponds to a slice over a single dimension of a tensor.
Examples of constructing and using indexers are provided in the
Ellipsis and the
Slice class documentation.
Here we provide examples of indexing over tensors using indexers:
val t = Tensor.zeros(FLOAT32, Shape(4, 2, 3, 8)) t(::, ::, 1, ::) // Tensor with shape [4, 2, 1, 8] t(1 :: -2, ---, 2) // Tensor with shape [1, 2, 3, 1] t(---) // Tensor with shape [4, 2, 3, 8] t(1 :: -2, ---, NewAxis, 2) // Tensor with shape [1, 2, 3, 1, 1] t(1 ::, ---, NewAxis, 2) // Tensor with shape [3, 2, 3, 1, 1]
--- corresponds to an ellipsis.
Note that each indexing sequence is only allowed to contain at most one Ellipsis. Furthermore, if an ellipsis is not
provided, then one is implicitly appended at the end of indexing sequence. For example,
foo(2 :: 4) is equivalent to
foo(2 :: 4, ---).
The low level API can be used to define computations that will be executed at a later point, and potentially execute
them. It can also be used to create custom layers for the Learn API. The main type of object
underlying the low level API is the
Output, which represents the value of a
Tensor that has not
yet been computed. Its name comes from the fact that it represents the output of some computation. An
Output object thus represents a partially defined computation that will eventually produce a value. Core
TensorFlow programs work by first building a graph of
Output objects, detailing how each output is computed
based on the other available outputs, and then by running parts of this graph to achieve the desired results.
Similar to a
Tensor, each element in an
Output has the same data type, and the data type is
always known. However, the shape of an
Output might be only partially known. Most operations produce tensors
of fully-known shapes if the shapes of their inputs are also fully known, but in some cases it’s only possible to find
the shape of a tensor at graph execution time.
It is important to understand the main concepts underlying the core API:
- Sparse Output:
With the exception of
Variables, the value of outputs is immutable, which means that in the context of a
single execution, outputs only have a single value. However, evaluating the same output twice can result in different
values. For example, that tensor may be the result of reading data from disk, or generating a random number.
Working with Outputs
Logging in the native TensorFlow library can be controlled by setting the
TF_CPP_MIN_LOG_LEVEL environment variable:
0: Debug level (default).
1: Warning level.
2: Error level.
3: Fatal level.