TensorFlow the massively popular open-source platform to develop and integrate large scale AI and Deep Learning Models has recently been updated to its newer form TensorFlow 2.0. This brings a massive boost in features in the originally feature-rich ML ecosystem created by the TensorFlow community.
What is Open-Source and how is it made TensorFlow so successful?
Along with this came the support for hardware acceleration for running large scale Machine Learning codes. These include CUDA(library for running ML code on GPUs), TPUs(Tensor Processing Unit- Custom hardware provided by Google specially designed and developed to process tensors using TensorFlow) for multiple machine configuration, GPU, GPGPU, Cloud-based TPU’s, ASIC (Application Specific Integrated Circuits) FPGAs(Field-Programmable Gate Arrays- These are exclusively used for custom Programmable Hardware). This also includes new additions such as NVIDIA’s Jetson TX2 and Intel’s Movidius chips.
Now coming back to the newer and much feature rich TensorFlow2.0:
This is a graphical implementation of the changes:
The things which are added include:
- Using tf.data for data loading(Or NumPy).
- Use Keras for model construction.(We can also use any premade Estimators).
- Use tf.function for DAG graph execution or use eager execution.
- Utilize distribution strategy for high-performance-computing and deep learning models. (For TPUs, GPUs etc).
At last we can now build big ML and Deep Learning models easily, effectively on TensorFlow2.0 for end users and implement them on a large scale.
Training a neural network to categorize MNSIT data
Expected Accuracy 88-91%
Eager Execution :
tf.Tensor([[4.]], shape=(1, 1), dtype=float32)
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