Hello! This is my first post on fastai. Loving the videos so far.

I have completed video 7 and I am trying to adapt the RNN model to predict on a different type of data rather than NLP. My data is tabular time series data, so each column represents a time and each row represents an item. To try to do this I walked through the lesson 7 RNN notebook and I have been making the appropriate changes.

I have successfully transformed the first versions of the RNN to work with the tabular time series data, but I’m struggling to include hidden RNN layers. I will post here the function that I transformed successfully and the one I am struggling with. If anyone has any advice on how to make the second function work that would be greatly appreciated.

==

1st function, successfully adapted (note that the variable names are slightly different):

FASTAI VERSION FOR NLP:

```
class Model_0(nn.Module):
def __init__(self):
super().__init__()
self.embed = nn.Embedding(nv, nh)
self.ln_hidden = nn.Linear(nh, nh)
self.ln_output = nn.Linear(nh, nv)
self.bn = nn.BatchNorm1d(nh)
def forward(self, x):
h = torch.zeros(x.shape[0], nh)
for i in range(x.shape[1]):
h += self.embed(x[:,i])
h = self.bn(F.relu(self.ln_hidden(h)))
print(self.ln_output(h).shape)
return self.ln_output(h)
```

MY VERSION FOR TABULAR TIME SERIES:

```
class Model_1(nn.Module):
def __init__(self):
super().__init__()
self.l_in = nn.Linear(nv, nh)
self.l_hidden = nn.Linear(nh, nh)
self.l_out_1 = nn.Linear(nh, 2)
self.bn = nn.BatchNorm1d(nh)
self.bno = nn.BatchNorm1d(2)
self.classes = [0,1]
def forward(self, x0, x):
t1 = self.l_in(x[:,0].unsqueeze(1))
hi = self.bn(F.relu(self.l_hidden(t1)))
for i in range(1, x.shape[1]):
hi += self.l_in(x[:,i].unsqueeze(1))
hi = self.bn(F.relu(self.l_hidden(hi)))
tout = self.bno(F.relu(self.l_out_1(hi)))
return tout.squeeze(1)
```

==

2nd function, not working

FASTAI VERSION

```
class Model_5(nn.Module):
def __init__(self):
super().__init__()
self.embed = nn.Embedding(nv, nh)
self.rnn = nn.GRU(nh, nh, 2, batch_first=True)
self.ln_output = nn.Linear(nh, nv)
self.bn = BatchNorm1dFlat(nh)
self.h = torch.zeros(2, x.shape[0], nh)
def forward(self, x):
res, h = self.rnn(self.embed(x), self.h)
self.h = h.detach()
return self.ln_output(self.bn(res))
```

MY VERSION

```
out = 1
layers = 2
class Model_2(nn.Module):
def __init__(self):
super().__init__()
self.l_in = nn.Linear(nv, nh)
self.l_hidden = nn.GRU(nh, nh, layers, batch_first=True)
self.l_out_1 = nn.Linear(nh, out)
self.bn = nn.BatchNorm1d(nh)
self.bno = BatchNorm1dFlat(out)
self.classes = [0,1]
def forward(self, x0, x):
hi = torch.zeros(layers, x.shape[0], nh)
t = self.l_in(x.unsqueeze(2))
res, hi = self.l_hidden(t, hi)
squeezed = self.l_out_1(res)
rel = F.relu(squeezed)
tout = self.bno(rel)
return tout.squeeze(-1)
```

When I run this code everything works fine, except when I print the confusion matrix every quadrant has a 0, and when I try to print learn.summary() I get the following error:

```
~/anaconda3/lib/python3.6/site-packages/fastai/callbacks/hooks.py in <listcomp>(.0)
149 with hook_params(flatten_model(m))as hook_p:
150 x = m.eval()(*x) if is_listy(x) else m.eval()(x)
--> 151 output_size = [((o.stored.shape[1:]) if o.stored is not None else None) for o in hook_o]
152 params = [(o.stored if o.stored is not None else (None,None)) for o in hook_p]
153 params, trainables = map(list,zip(*params))
AttributeError: 'list' object has no attribute 'shape'
```

I’m running as follows:

```
learn = Learner(data, Model_2(), loss_func=nn.CrossEntropyLoss(), metrics=error_rate)
learn.fit_one_cycle(10, 3e-3)
```

I am running with the same data for the first model and the second model.

Any advice would be greatly appreciated! I hope this question clear. If not please let me know and I will change it accordingly.