Here is my full code
Model definition
def __init__(self):
super(NET,self).__init__()
#Couches de convolution - Encoder
self.CONV1=nn.Conv1d(1,16,kernel_size=300,padding=0,stride=2)
self.CONV2=nn.Conv1d(16,32,kernel_size=150,padding=0,stride=2)
self.CONV3=nn.Conv1d(32,64,kernel_size=75,padding=0,stride=1)
#Fonctions d'activation - Encoder
self.RELU=nn.ReLU()
#Maxpooling - Encoder
self.MAX1=nn.MaxPool1d(kernel_size=2,stride=2,padding=0)
self.MAX2=nn.MaxPool1d(kernel_size=2,stride=2,padding=0)
self.MAX3=nn.MaxPool1d(kernel_size=2,stride=2,padding=0)
#Bidirectionnel LSTM
self.LSTM=nn.LSTM(input_size=60,hidden_size=500,num_layers=2,batch_first=False) ```
**Definition of forward**
```def forward(self,x):
C1=F.relu(self.CONV1(x))
M1=self.MAX1(C1)
C2=F.relu(self.CONV2(M1))
M2=self.MAX2(C2)
C3=F.relu(self.CONV3(M2))
M3=self.MAX3(C3)
h0= Variable(torch.zeros(2,64,500))
c0= Variable(torch.zeros(2,64,500))
Pred,out= self.LSTM(M3,(h0,c0))```
**Trying to have a summary of my model**
"from torchsummary import summary
summary(net, input_size=(1,4000))"
**The full error**
AttributeError Traceback (most recent call last)
<ipython-input-162-69d32417aabe> in <module>()
1 from torchsummary import summary
----> 2 summary(net, input_size=(1,4000))
5 frames
/usr/local/lib/python3.6/dist-packages/torchsummary/torchsummary.py in summary(model, input_size, batch_size, device)
70 # make a forward pass
71 # print(x.shape)
---> 72 model(*x)
73
74 # remove these hooks
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
<ipython-input-160-c66aa62e34fe> in forward(self, x)
37 h0= Variable(torch.zeros(2,64,500))
38 c0= Variable(torch.zeros(2,64,500))
---> 39 Pred,out= self.LSTM(M3,(h0,c0))
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
724 _global_forward_hooks.values(),
725 self._forward_hooks.values()):
--> 726 hook_result = hook(self, input, result)
727 if hook_result is not None:
728 result = hook_result
/usr/local/lib/python3.6/dist-packages/torchsummary/torchsummary.py in hook(module, input, output)
21 if isinstance(output, (list, tuple)):
22 summary[m_key]["output_shape"] = [
---> 23 [-1] + list(o.size())[1:] for o in output
24 ]
25 else:
/usr/local/lib/python3.6/dist-packages/torchsummary/torchsummary.py in <listcomp>(.0)
21 if isinstance(output, (list, tuple)):
22 summary[m_key]["output_shape"] = [
---> 23 [-1] + list(o.size())[1:] for o in output
24 ]
25 else:
AttributeError: 'tuple' object has no attribute 'size'