# LSTM Manual Calculation in Pytorch

Hi, I’m doing manual calculations for the LSTM layer and want to compare the results with the output of the program in PyTorch. However, I found the results were different. I use 1 layer of LSTM and initialized all of the bias and weight with values of 1 and the h_0 and c_0 value with 0.
Here is the LSTM formula from the official PyTorch website: I will send a Google Drive link containing the result from PyTorch and my manual calculation because I can’t upload more than 1 image right now, here is the GDrive link:

Can someone explain why I got a different result from the output of the program?
Thank you!

I haven’t checked your code, as you’ve posted images in the folder.
However, this post might be helpful, which shows a manual implementation to fix another user error.

1 Like

Hi, thank you for answering my question! I am new in this field, and I was tried to calculate the LSTM with excel, but I didn’t think about make it manual with python. So, I tried the code that you have referred to, and it works on my train data when the batch_first = False, it produces the same output for Official LSTM and Manual LSTM. However, when I change the batch_first = True, it not produces the same value anymore, while I need to change the batch_first to True, because my dataset shape is tensor of (Batch, Sequences, Input size). Which part of the Manual LSTM needs to be changed to produces the same output as the Official LSTM produces when batch_first = True? Here is the code snippet:

``````import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

train_x = torch.tensor([[[0.14285755], , [0.04761982], [0.04761982], [0.04761982],
[0.04761982], [0.04761982], [0.09523869], [0.09523869], [0.09523869],
[0.09523869], [0.09523869], [0.04761982], [0.04761982], [0.04761982],
[0.04761982], [0.09523869], [0.        ], [0.        ], [0.        ],
[0.        ], [0.09523869], [0.09523869], [0.09523869], [0.09523869],
[0.09523869], [0.09523869], [0.09523869],[0.14285755], [0.14285755]]],

seed = 23
torch.manual_seed(seed)
np.random.seed(seed)

pytorch_lstm = torch.nn.LSTM(1, 1, bidirectional=False, num_layers=1, batch_first=True)
weights = torch.randn(pytorch_lstm .weight_ih_l0.shape,dtype = torch.float)
pytorch_lstm.weight_ih_l0 = torch.nn.Parameter(weights)
# Set bias to Zero
pytorch_lstm.bias_ih_l0 = torch.nn.Parameter(torch.zeros(pytorch_lstm.bias_ih_l0.shape))
pytorch_lstm.weight_hh_l0 = torch.nn.Parameter(torch.ones(pytorch_lstm.weight_hh_l0.shape))
# Set bias to Zero
pytorch_lstm.bias_hh_l0 = torch.nn.Parameter(torch.zeros(pytorch_lstm.bias_ih_l0.shape))
pytorch_lstm_out = pytorch_lstm(train_x)

batchsize=1

# Manual Calculation
W_ii, W_if, W_ig, W_io = pytorch_lstm.weight_ih_l0.split(1, dim=0)
b_ii, b_if, b_ig, b_io = pytorch_lstm.bias_ih_l0.split(1, dim=0)

W_hi, W_hf, W_hg, W_ho = pytorch_lstm.weight_hh_l0.split(1, dim=0)
b_hi, b_hf, b_hg, b_ho = pytorch_lstm.bias_hh_l0.split(1, dim=0)

prev_h = torch.zeros((batchsize,1))
prev_c = torch.zeros((batchsize,1))

i_t = torch.sigmoid(F.linear(train_x, W_ii, b_ii) + F.linear(prev_h, W_hi, b_hi))
f_t = torch.sigmoid(F.linear(train_x, W_if, b_if) + F.linear(prev_h, W_hf, b_hf))
g_t = torch.tanh(F.linear(train_x, W_ig, b_ig) + F.linear(prev_h, W_hg, b_hg))
o_t = torch.sigmoid(F.linear(train_x, W_io, b_io) + F.linear(prev_h, W_ho, b_ho))
c_t = f_t * prev_c + i_t * g_t
h_t = o_t * torch.tanh(c_t)

print('nn.LSTM output {}, manual output {}'.format(pytorch_lstm_out, h_t))
print('nn.LSTM hidden {}, manual hidden {}'.format(pytorch_lstm_out, h_t))
print('nn.LSTM state {}, manual state {}'.format(pytorch_lstm_out, c_t))
``````