Hello again,
I am not sure why this is happening. The only thing that is maybe unusual about my code is that I am training a RNN by feeding its o/p as i/p to the next time-step (after passing it thru a FC layer). Any advice would be greatly appreciated.
I am attaching a screenshot of the error (pls ignore the image on the right)

Screenshot from 2018-03-19 16-58-00.png2560×1024 440 KB

Here is my forward pass:
def forward(self,frames,actions,nsteps):
104         
105         frame_emb = self.encoder(frames)
106         #project for action conditioning
107         frame_embs = self.encoder_linear(frame_emb)
108         
109         action_emb = self.action_linear(actions.view(-1,6))
110         action_emb = action_emb.view(-1,nsteps,2048)
111         
112         #initial hidden state
113         h0 = Variable(torch.zeros(1, 16, 512).cuda()) 
114         c0 = Variable(torch.zeros(1, 16, 512).cuda())
115         hidden = (h0,c0)
116 
117         outputs = Variable(torch.zeros((16,nsteps,84,84))).cuda()
118         rewards = Variable(torch.zeros((16,nsteps,3))).cuda()
119 
120         for step in range(nsteps):
121             rnn_input = self.decoder_linear((frame_embs*action_emb[:,step,:])) + self.bias
122             rnn_input = rnn_input.unsqueeze(1)
123             ActCondEmb,hidden = self.rnn(rnn_input,hidden)
124 
125             ActEmb = ActCondEmb.contiguous().view(-1,ActCondEmb.size(-1))
126             dec_emb = self.decoder_proj(ActEmb)
127             #part of input for next time step
128             frame_embs = self.encoder_linear(dec_emb)
129             #model predictions
130             output = self.decoder(dec_emb)
131             outputs[:,step,:,:] = output
132             reward_predictions = self.reward_linear(dec_emb)
133             rewards[:,step,:] = reward_predictions
134 
135         return outputs, rewards