How to solve the situation of multiple data in one sample

[wky1998]

thank you for your sharing，but i don't know how to solve the data such as(time_step,batch,input_size),time_step is the number of data per sample，and time_step is different in each sample. and input_size=2. i don't konw the means of for i in steps in the function of Recurrent. it means 5000 samples or time_step is 5000?

[stefanonardo]

When you have to deal with sequences of different length, you should use Packed Sequences. You can follow this procedure (obviously you need to know the length of each sequence):

TRAINING

parameters: 
# Tensor of size (max_timestep, batch, input_size) containing the sequences sorted
# by length. You must pad shorter sequences (typically with zero).
input_sequences
# Sorted list containing the length of each sequence
lengths

# Pack input
packed_input = torch.nn.utils.rnn(pack_padded_sequence(input_sequences, lengths)

# Prepare target
target_flat = aux.prepare_target(target, lengths, washout)

model(packed_input, washout, None, target_flat)

TEST

parameters: 
# Tensor of size (max_timestep, batch, input_size) containing the sequences sorted
# by length. You must pad shorter sequences (typically with zero).
input_sequences
# Sorted list containing the length of each sequence
lengths

# Pack input
packed_input = torch.nn.utils.rnn(pack_padded_sequence(input_sequences, lengths)

output, hidden = model(packed_input, washout)

# Prepare target for comparison, removing washout and padding the sequences.
washout_target, washout_lengths = aux.washout_tensor(target, washout, lengths)

# Remember to use (you'll have to implement it usually) a loss function 
# which discards padding values.
loss = loss_fcn(output, washout_target, washout_lengths)

[wky1998]

Thank you for your answer.it's great, but i still have some questions. My data have 5000 sample and each sample have different length,but input size are all 2. each sample has a target y. like this sample [[Dimension,longitude],[Dimension,longitude],[Dimension,longitude],[Dimension,longitude],].i need to use previous position to predict the last [Dimension,longitude]. Because each sample have different number of [Dimension,longitude], i need to pack them to max_length? then input shape is (max_lenght,batch,input_size), and "for i in steps " in the function of Recurrent, steps is max_lenght? suppose this how can i train these 5000 samples?

[stefanonardo]

Why are you interested in Recurrent? You only have to worry about ESN interface. As I said, prepare a tensor containing all your sequences, appropriately padded whenever necessary, then pack them with PyTorch utilities. Anyway this problem is not really inherent to pytorch-esn you can find many resources about the usage of variable length sequences with PyTorch. The way you can use packed sequences is the same here.