Variables

What types of variables are used in SameDiff, their properties and how to switch these types.

What are variables

All values defining or passing through each SameDiff instance - be it weights, bias, inputs, activations or general parameters - all are handled by objects of class SDVariable.

Observe that by variables we normally mean not just single values - as it is done in various online examples describing autodifferentiation - but rather whole multidimensional arrays of them.

Variable types

All variables in SameDiff belong to one of four variable types, constituting an enumeration VariableType. Here they are:

• VARIABLE: are trainable parameters of your network, e.g. weights and bias of a layer. Naturally, we want them

  to be both stored for further usage - we say, that they are persistent - as well as being updated during training.

• CONSTANT: are those parameters which, like variables, are persistent for the network, but are not being

  trained; they, however, may be changed externally by the user.

• PLACEHOLDER: store temporary values that are to be supplied from the outside, like inputs and labels.

  Accordingly, since new placeholders' values are provided at each iteration, they are not stored: in other words,

  unlike VARIABLE and CONSTANT, PLACEHOLDER is not persistent.

• ARRAY: are temporary values as well, representing outputs of operations within a SameDiff, for

  instance sums of vectors, activations of a layer, and many more. They are being recalculated at each iteration, and

  therefor, like PLACEHOLDER, are not persistent.

To infer the type of a particular variable, you may use the method getVariableType, like so:

VariableType varType = yourVariable.getVariableType();

The current value of a variable in a form of INDArray may be obtained using getArr or getArr(true) - the latter one if you wish the program to throw an exception if the variable's value is not initialized.

Data types

The data within each variable also has its data type, contained in DataType enum. Currently in DataType there are three floating point types: FLOAT, DOUBLE and HALF; four integer types: LONG, INT, SHORT and UBYTE; one boolean type BOOL - all of them will be referred as numeric types. In addition, there is a string type dubbed UTF8; and two helper data types COMPRESSED and UNKNOWN. The 16-bit floating point format BFLOAT16 and unsigned integer types (UINT16, UINT32 and UINT64) will be available in 1.0.0-beta5.

To infer the data type of your variable, use

DataType dataType = yourVariable.dataType();

You may need to trace your variable's data type since at times it does matter, which types you use in an operation. For example, a convolution product, like this one

SDVariable prod = samediff.cnn.conv1d(input, weights, config);

will require its SDVariable arguments input and weights to be of one of the floating point data types, and will throw an exception otherwise. Also, as we shall discuss just below, all the SDVariables of type VARIABLE are supposed to be of floating point type.

Common features of variables

Before we go to the differences between variables, let us first look at the properties they all share

• All variables are ultimately derived from an instance of SameDiff, serving as parts of its

  graph. In fact, each variable has a SameDiff as one of its fields.

• Results (outputs) of all operations are of ARRAY type.

• All SDVariable's involved in an operation are to belong to the same SameDiff.

• All variables may or may not be given names - in the latter case, a name is actually created automatically. Either

  way, the names need to be/are created unique. We shall come back to naming below.

Differences between variable types

Let us now have a closer look at each type of variables, and what distinguish them from each other.

Variables

Variables are the trainable parameters of your network. This predetermines their nature in SameDiff. As we briefly mentioned above, variables' values need to be both preserved for application, and updated during training. Training means, that we iteratively update the values by small fractions of their gradients, and this only makes sense if variables are of floating point types (see data types above).

Variables may be added to your SameDiff using different versions of var function from your SameDiff instance. For example, the code

SDVariable weights = samediff.var("weights", DataType.FLOAT, 784, 10);

adds a variable constituting of a 784x10 array of float numbers - weights for a single layer MNIST perceptron in this case - to a pre-existing SameDiff instance samediff.

However, this way the values within a variable will be set as zeros. You may also create a variable with values from a preset INDArray. Say

SDVariable weights = samediff.var("weigths", Nd4j.nrand(784, 10).div(28));

will create a variable filled with normally distributed randomly generated numbers with variance 1/28. You may put any other array creation methods instead of nrand, or any preset array, of course. Also, you may use some popular initialization scheme, like so:

SDVariable weights = samediff.var("weights", new XavierInitScheme('c', 784, 10), DataType.FLOAT, 784, 10);

Now, the weights will be randomly initialized using the Xavier scheme. There are other ways to create and

fill variables: you may look them up in the 'known subclasses' section [of our javadoc](https://deeplearning4j.org/api/latest/org/nd4j/weightinit/WeightInitScheme.html").

Constants

Constants hold values that are stored, but - unlike variables - remain unchanged during training. These, for instance, may be some hyperparamters you wish to have in your network and be able to access from the outside. Or they may be pretrained weights of a neural network that you wish to keep unchanged (see more on that in Changing Variable Type below). Constants may be of any data type

• so e.g. int and boolean are allowed alongside with float and double.

In general, constants are added to SameDiff by means of constant methods. A constant may be created form an INDArray, like that:

SDVariable constant = samediff.constant("constants", Nd4j.create(new float[] {3.1415f, 42f}));

A constant consisting of a single scalar value may be created using one of the scalar methods:

INDArray someScalar = samediff.scalar("scalar", 42);

Again, we refer to the javadoc for the whole reference.

Placeholders

The most common placeholders you'll normally have in a SameDiff are inputs and, when applicable, labels. You may create placeholders of any data type, depending on the operations you use them in. To add a placeholder to a SameDiff, you may call one of placeHolder methods, e.g. like that:

SDVariable in = samediff.placeHolder("input", DataType.FLOAT, -1, 784);

as in MNIST example. Here we specify name, data type and then shape of your placeholder - here, we have 28x28 grayscale pictures rendered as 1d vectors (therefore 784) coming in batches of length we don't know beforehand (therefore -1).

Arrays

Variables of ARRAY type appear as outputs of operations within SameDiff. Accordingly, the data type of an array-type variable depends on the kind of operation it is produced by and variable type(s) ot its argument(s). Arrays are not persistent - they are one-time values that will be recalculated from scratch at the next step. However, unlike placeholders, gradients are computed for them, as those are needed to update the values of VARIABLE's.

There are as many ways array-type variables are created as there are operations, so you're better up focusing on our operations section, our javadoc and examples.

Recap table

Let us summarize the main properties of variable types in one table:

	Trainable	Gradients	Persistent	Workspaces	Datatypes	Instantiated from
VARIABLE	Yes	Yes	Yes	Yes	Float only	Instance
CONSTANT	No	No	Yes	No	Any	Instance
PLACEHOLDER	No	No	No	No	Any	Instance
ARRAY	No	Yes	No	Yes	Any	Operations

We haven't discussed what 'Workspaces' mean - if you do not know, do not worry, this is an internal technical term that basically describes how memory is managed internally.

Changing variable types

You may change variable types as well. For now, there are three of such options:

Variable to constant

At times - for instance if you perform transfer learning - you may wish to turn a variable into a constant. This is done like so:

samediff.convertToConstant(someVariable);

where someVariable is an instance of SDVariable of VARIABLE type. The variable someVariable will not be trained any more.

Constant to variable

Conversely, constants - if they are of floating point data type - may be converted to variables. So, for instance, if you wish your frozen weights to become trainable again

samediff.convertToVariable(frozenWeights); //not frozen any more

Placeholder to constant

Placeholders may be converted to constants as well - for instance, if you need to freeze one of the inputs. There are no restrictions on the data type, yet, since placeholder values are not persistent, their value should be set before you turn them into constants. This can be done as follows

placeHolder.setArray(someArray);
samediff.convertToConstant(placeHolder);

For now it is not possible to turn a constant back into a placeholder, we may consider adding this functionality if there is a need for that. For now, if you wish to effectively freeze your placeholder but be able to use it again, consider supplying it with constant values rather than turning it into a constant.

Variables' names and values

Getting variables from SameDiff

Recall that every variable in an instance of SameDiff has its unique String name. Your SameDiff actually tracks your variables by their names, and allows you to retrieve them by using getVariable(String name) method.

Consider the following line:

SDVariable regressionCost = weights.mmul(input).sub("regression_prediction", bias).squaredDifference(labels);

Here, in the function sub we actually have implicitly introduced a variable (of type ARRAY) that holds the result of the subtraction. By adding a name into the operations's argument, we've secured ourselves the possibility to retrieve the variable from elsewhere: say, if later you need to infer the difference between the labels and the prediction as a vector, you may just write:

SDVariable errorVector = samediff.getVariable("regressionPrediction").sub(labels);

This becomes especially handy if your whole SameDiff instance is initialized elsewhere, and you still need to get hold of some of its variables - say, multiple outputs.

You can get and set the name of an SDVariable the methods getVarName and setVarName respectively. When renaming, note that variable's name is to remain unique within its SameDiff.

Getting variable's value

You may retrieve any variable's current value as an INDArray using the method eval(). Note that for non-persistent variables, the value should first be set. For variables with gradients, the gradient's value may also be inferred using the method getGradient.