Current generation of Reconfigurable Match-Action Tables switches are highly programmable, able to support stateful operations and pipeline specifications using languages like P4. Nevertheless, these switches do not offer primitives to support real-valued operations on the data plane, thus requiring support from external servers or middleboxes to perform advanced operations. We introduce InREC, a system that extends the capabilities of programmable switches to support in-network real-valued operations using the IEEE half-precision floating point representation. It relies on decomposing real-valued functions into lookup tables taking into account the RMT model constraints to reach the right trade-off between accuracy and resource usage. InREC prototype on Barefoot Tofino switches demonstrates the efficiency of InREC for in-network computation of different types of operations and its application for in-network logistic regression models used for classification problems. Our evaluation of InREC shows that it is possible to implement complex in-network applications with high accuracy and low latency.