Posted On: Feb 22, 2018
A few major differences between the Keras, Tensor flow and PyTorch are as follows
|S. No.||Criteria of classification||Keras||TensorFlow||PyTorch|
|1.||Based on platform||Keras is an open source platform for deep learning and neural network building that is designed to run over TensorFlow||The TensorFlow is an open-source library designed for dataflow programming.||The PyTorch is an open source machine learning library designed for Python, which is based on Torch.|
|2.||Based on API||The Keras is designed with a high level of API and designed to run on top of TensorFlow and the PyTorch.||The TensorFlow is designed to provide both high and low APIs.||The PyTorch generally. operates with lower APIs.|
|3.||Based on the Architecture||Based on the readability and ease of use Keras is the best.||The TensorFlow is at the middle level in between the Keras and the PyTorch on the basis of architecture.||The PyTorch comes at the lowest level based on the ease of usage and readability.|
|4.||Based on Debugging criteria||The Keras, very less requires debugging.||TensorFlow is at the middle level in between the PyTorch and the Keras based on Debugging criteria.||The PyTorch has the best debugging facilities amongst the three|
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Tensors can be thought of as vectors and matrices of higher dimensions. They represent n-dimensional arrays of base data-type. Each element of a tensor is of the same data-type which is always known. ...