Airplane manufacturing giant Airbus is using TensorFlow to extract and analyze information from satellite images to deliver valuable real-time information to clients. The driving idea behind the API is being able to translate from idea to a result in as little time as possible. Keras furthers the abstractions of TensorFlow by providing a simplified API intended for building models for common use cases. Keras is a high-level API that runs on top of TensorFlow.
Tensor board is the unified visualization framework for Tensorflow and Keras. For example, TensorBoard, which allows users to visually monitor the training process, underlying computational graphs, and metrics for purposes of debugging runs and evaluating model performance. TensorFlow also contains many supporting features. Trained models can then run on a range of platforms, from desktop to mobile and all the way to cloud. In both cases, the process is sped up by placing tensors on the GPU. Using the TensorFlow architecture, training is generally done on a desktop or in a data center. The second, and often more intuitive method, is using eager execution, which follows imperative programming principles and evaluates operations immediately. The primary method is by building a computational graph that defines a dataflow for training the model.
The framework inputs data as a multidimensional array called tensors and executes in two different fashions. There are three distinct parts that define the TensorFlow workflow, namely preprocessing of data, building the model, and training the model to make predictions.
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