wdir = r'C:/Users/hauer/Documents/Repositories/cfds_project'
save_dir = os.path.join(wdir, 'pytorch_models')
model_name = 'rnn.torch'
if (not os.path.isdir(save_dir)):
os.mkdir(save_dir)
save(model.state_dict(), os.path.join(save_dir, model_name))
model = RNN(input_size,
seq_len,
output_size=output_size,
hidden_dim=hidden_dim,
n_layers=n_layers)
model.load_state_dict(load(os.path.join(save_dir, model_name)))
model.eval()
# =============================================================================
# # Evaluation / Plotting
# =============================================================================
# Run RNN with whole df, only selecting the outputs that are wanted for prediction
X_eval = df.iloc[:, 1:].values
y_eval = df.iloc[:, 0].values
X_eval_T = from_numpy(X_eval).float()
N, _ = X_eval_T.shape
X_eval_T = X_eval_T.view([-1, N, dummy_dim])
hidden_0 = zeros(1, N, hidden_dim)
model.eval()
