nn.LSTMCell

nn.LSTMCell is a PyTorch class that implements a single LSTM cell. It takes in an input tensor and the previous hidden and cell states as inputs, and computes the current hidden and cell states based on the LSTM equations.

The LSTM equations are as follows:

i_t = sigmoid(W_i * [h_{t-1}, x_t] + b_i)
f_t = sigmoid(W_f * [h_{t-1}, x_t] + b_f)
g_t = tanh(W_g * [h_{t-1}, x_t] + b_g)
o_t = sigmoid(W_o * [h_{t-1}, x_t] + b_o)
c_t = f_t * c_{t-1} + i_t * g_t
h_t = o_t * tanh(c_t)

where x_t is the input tensor at time t, h_{t-1} and c_{t-1} are the previous hidden and cell states, i_t, f_t, g_t, and o_t are the input gate, forget gate, cell gate, and output gate activations, and W_i, W_f, W_g, W_o, b_i, b_f, b_g, and b_o are the weights and biases of the LSTM cell.

nn.LSTMCell can be used to build a multi-layer LSTM network by stacking multiple LSTM cells together. It is often used in combination with other PyTorch modules such as nn.Linear and nn.Dropout to create more complex models.