--- title: RNN_FCN keywords: fastai sidebar: home_sidebar summary: "This is an unofficial PyTorch implementation by Ignacio Oguiza - oguiza@gmail.com based on:" description: "This is an unofficial PyTorch implementation by Ignacio Oguiza - oguiza@gmail.com based on:" nb_path: "nbs/107_models.RNN_FCN.ipynb" ---
bs = 16
n_vars = 3
seq_len = 12
c_out = 2
xb = torch.rand(bs, n_vars, seq_len)
test_eq(RNN_FCN(n_vars, c_out, seq_len)(xb).shape, [bs, c_out])
test_eq(LSTM_FCN(n_vars, c_out, seq_len)(xb).shape, [bs, c_out])
test_eq(MLSTM_FCN(n_vars, c_out, seq_len)(xb).shape, [bs, c_out])
test_eq(GRU_FCN(n_vars, c_out, shuffle=False)(xb).shape, [bs, c_out])
test_eq(GRU_FCN(n_vars, c_out, seq_len, shuffle=False)(xb).shape, [bs, c_out])
LSTM_FCN(n_vars, seq_len, c_out, se=8)