Static Data Types and More

With this release we have finally added support for static data type
information for tensors (not for symbolic tensors yet though -- for now
we effectively have support for a statically-typed version of numpy
for Scala). This is an important milestone and contributes significantly
to type safety, which can help catch errors at compile time, rather than
runtime. For example:

val t1 = Tensor(0.5, 1) // The inferred type is Tensor[FLOAT64].
val t2 = Tensor(1, 2)   // The inferred type is Tensor[INT32].
val t3 = t1 + t2        // The inferred type is Tensor[FLOAT64].
val t4 = t3.isNaN       // The inferred type is Tensor[BOOLEAN].
val t5 = t3.any()       // Fails at compile-time because `any()` is only
                        // supported for Tensor[BOOLEAN].

Other new features include:

• Improvements to the high-level learn API:
  • Layers can now provide and use their own parameter generator, and
    can also access the current training step
    (using Layer.currentStep).
  • Layers now support .map(...).
  • Added support for batch normalization.
• Added support for tf.logSigmoid and tf.lrn.
• Added support for the following new metrics:
  • Grouped precision.
  • Precision-at-k.
• data module:
  • Added support for loading the extreme classification repository
    datasets (i.e., data.XCLoader).
  • Added support for randomly splitting datasets.