Implementation differences in LSTM layers- Tensorflow vs Pytorch | Towards Data Science

Abstraction: API and parameter differences in LSTM layer implementations across TF and PyTorch

Key points:

• Default input shape differs: TF expects (batch, timesteps, features); PyTorch expects (timesteps, batch, features) unless batch_first=True
• TF default output is last-timestep hidden state only; PyTorch always returns a tuple: (all-timestep outputs, (h_T, c_T))
• PyTorch uses two bias vectors per gate equation instead of TF's one, adding 4×n_h extra parameters per LSTM layer
• TF exposes activation customization (activation, recurrent_activation params); PyTorch LSTM activations are fixed
• PyTorch num_layers=k creates a stacked LSTM block in one object; TF requires manually stacking layers
• Recovering cell state cT: TF requires return_state=True at init; PyTorch always provides it in the output tuple

Connections: Tensorflow · Pytorch · Recurrent Neural Networks · Deep Learning Frameworks

Source: https://towardsdatascience.com/lstm-by-example-using-tensorflow-feb0c1968537