DNNs are computational intensive, and nowadays, their acceleration on the FPGA has received much attention. Many methods to accelerate DNNs have been proposed. Despite their performance features like acceptable accuracy or low latency, their use is not widely accepted by software designers who usually do not have enough knowledge of the hardware details of the proposed accelerators. HLS tools are the major promising tools that can act as a bridge between software designers and hardware implementation. However, not only most HLS tools just support C and C++ descriptions as input, but also their result is very sensitive to the coding style. It makes it difficult for the software developers to adopt them, as DNNs are mostly described in high-level languages such as Tensorflow or Keras. In this paper, an integrated toolchain is presented that, in addition to converting the Keras DNN descriptions to a simple, flat, and synthesizable C output, provides other features such as accuracy verification, C level knobs to easily change the data types from floating-point to fixed-point with arbitrary bit width, and latency and area utilization adjustment using HLS knobs.