pytorch-vs-tensorflow-spotting-the-difference-25c75777377b

Recall RNNs: with static graphs, the input sequence length will stay constant. Here we introduce datasets module which contains wrappers for popular datasets used to benchmark deep learning architectures. There are large amounts of ready to use modules in torch.nn Notice how PyTorch uses object oriented approach to define basic building blocks and give us some'rails' to move on while providing ability to extend functionality via subclassing. Here we will use tf.layers and tf.contrib.learn The code follows the official tutorial on tf.layers: So, both TensorFlow and PyTorch provide useful abstractions to reduce amounts of boilerplate code and speed up model development.