Thanks! Here’s my code
import torch
import torch.nn as nn
from torch.autograd import Variable
torch.manual_seed(0)
np.random.seed(0)
# Custom RNN
class CustomRNN(nn.Module):
def __init__(self, cell_type, hidden_size, num_layers, bidirectional):
super(CustomRNN, self).__init__()
torch.manual_seed(0)
if bidirectional:
self.num_directions = 2
else:
self.num_directions = 1
if cell_type=="RNN":
self.rnn = nn.RNN(input_size=3, hidden_size=hidden_size,
num_layers=num_layers, batch_first=True,
bidirectional=bidirectional)
elif cell_type=="GRU":
self.rnn = nn.GRU(input_size=3, hidden_size=hidden_size,
num_layers=num_layers, batch_first=True,
bidirectional=bidirectional)
else:
self.rnn = nn.LSTM(input_size=3, hidden_size=hidden_size,
num_layers=num_layers, batch_first=True,
bidirectional=bidirectional)
self.linear = nn.Linear(hidden_size*self.num_directions, 1 )
self.act = nn.ReLU()
def forward(self, x):
pred, hidden = self.rnn(x, None)
pred = self.linear(pred)
#pred = torch.clamp(pred, min=0.)
#pred = self.act(pred)
#pred = torch.min(pred, x)
return pred
num_folds = 5
if torch.cuda.is_available():
cuda_av = True
else:
cuda_av=False
# Specifying the params
fold_num = 0
num_folds = 5
cell_type="GRU"
hidden_size = 100
lr = 1
bidirectional = False
hours = np.arange(1, 25, 1)
# sine and co-sine for incorporating the hour of the day
d=pd.DataFrame([np.sin(2 * np.pi * hours/24), np.cos(2 * np.pi * hours/24)]).T
train, test = get_train_test(num_folds=num_folds, fold_num=fold_num)
train_inp = train[:, 0, :, :].reshape(-1, 24, 1) # continuous-valued input of length 24, dimension 1
train_out = train[:, 1, :, :].reshape(-1, 24, 1) # continuous-valued variable to be estimated of length 24, dimension 1
# Making train_inp_time of #samples, 24, 3
train_inp_time = np.zeros((train_inp.shape[0], 24, 3))
for sample in range(train_inp.shape[0]):
temp = d.copy()
temp['val'] = train_inp[sample, :, :]
train_inp_time[sample :, :] = temp.values
loss_func = nn.L1Loss()
r = CustomRNN(cell_type, hidden_size, 1, bidirectional)
if cuda_av:
r = r.cuda()
loss_func = loss_func.cuda()
optimizer = torch.optim.Adam(r.parameters(), lr=lr)
num_iterations=100
for t in range(num_iterations):
inp = Variable(torch.Tensor(train_inp_time), requires_grad=True)
train_y = Variable(torch.Tensor(train_out))
if cuda_av:
inp = inp.cuda()
train_y = train_y.cuda()
pred = r(inp)
print(pred.std().data[0], pred.mean().data[0])
optimizer.zero_grad()
loss = loss_func(pred, train_y)
if t % 1 == 0:
print(t, loss.data[0])
loss.backward()
optimizer.step()
