BiLSTM Empty Backward Cell states and Hidden states

Question

Lee Rui En 2022-5-12

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1717220-bilstm-empty-backward-cell-states-and-hidden-states

回答： Aditya 2024-2-26

Hi,

I noticed that for the BiLSTM layer, the second half of CellStates and HiddenStates (which corresponds to the backwards states) are always 0, despite that I have called classifyAndUpdateStates().

Note that the NumHiddenUnits of the layer is set to 15. The first 15 elements in the CellState and HiddenState array are not 0, while the subsequent 15 elements are 0.

I have also tried to replicate the behaviour of the forward pass of a biLSTM layer of matlab using the codes below:

%% Testing using BiLSTM layer

clc;

% Selecting one input data

n = 1;

X = Xdata{n};

Y = Ydata(n);

n_seq = size(X, 2); % number of sequences to be fed in

layer_num = 3; % First biLSTM layer

inputSize = size(Xdata{1}, 1);

numHU = net_best.Layers(layer_num).NumHiddenUnits;

% Activation using matlab functions

feature = activations(net_best, X(:, 1:n_seq), layer_num);

[net_best_seq, Ypred] = classifyAndUpdateState(net_best, X(:, 1:n_seq));

hidden_state_actual = feature{1};

cell_state_actual = net_best_seq.Layers(layer_num).CellState;

% Recreation of activation

% Getting weights and biases

W.Wall = net_best.Layers(layer_num).InputWeights;

b.ball = net_best.Layers(layer_num).Bias;

R.Rall = net_best.Layers(layer_num).RecurrentWeights;

W.Wi_f = W.Wall(0*numHU+1:1*numHU, :);

W.Wf_f = W.Wall(1*numHU+1:2*numHU, :);

W.Wg_f = W.Wall(2*numHU+1:3*numHU, :);

W.Wo_f = W.Wall(3*numHU+1:4*numHU, :);

W.Wi_b = W.Wall(4*numHU+1:5*numHU, :);

W.Wf_b = W.Wall(5*numHU+1:6*numHU, :);

W.Wg_b = W.Wall(6*numHU+1:7*numHU, :);

W.Wo_b = W.Wall(7*numHU+1:8*numHU, :);

b.bi_f = b.ball(0*numHU+1:1*numHU, :);

b.bf_f = b.ball(1*numHU+1:2*numHU, :);

b.bg_f = b.ball(2*numHU+1:3*numHU, :);

b.bo_f = b.ball(3*numHU+1:4*numHU, :);

b.bi_b = b.ball(4*numHU+1:5*numHU, :);

b.bf_b = b.ball(5*numHU+1:6*numHU, :);

b.bg_b = b.ball(6*numHU+1:7*numHU, :);

b.bo_b = b.ball(7*numHU+1:8*numHU, :);

R.Ri_f = R.Rall(0*numHU+1:1*numHU, :);

R.Rf_f = R.Rall(1*numHU+1:2*numHU, :);

R.Rg_f = R.Rall(2*numHU+1:3*numHU, :);

R.Ro_f = R.Rall(3*numHU+1:4*numHU, :);

R.Ri_b = R.Rall(4*numHU+1:5*numHU, :);

R.Rf_b = R.Rall(5*numHU+1:6*numHU, :);

R.Rg_b = R.Rall(6*numHU+1:7*numHU, :);

R.Ro_b = R.Rall(7*numHU+1:8*numHU, :);

c_prev = zeros(2*inputSize, 1);

c_prev_f = c_prev(0*numHU+1:1*numHU, :);

c_prev_b = c_prev(1*numHU+1:2*numHU, :);

h_prev = zeros(2*inputSize, 1);

h_prev_f = h_prev(0*numHU+1:1*numHU, :);

h_prev_b = h_prev(1*numHU+1:2*numHU, :);

% Initialising

c_f = c_prev_f;

h_f = h_prev_f;

c_b = c_prev_b;

h_b = h_prev_b;

for a = 1:n_seq

c_prev_f = c_f;

h_prev_f = h_f;

c_prev_b = zeros(numHU, 1); %c_b; % !! resetting backward cell state instead of inheriting it from previous sequence

h_prev_b = zeros(numHU, 1); %c_f; % !! resetting backward hidden state instead of inheriting it from previous sequence

Xin = activations(net_best, X(:, a), layer_num-1); % get the input after BN

Xin = Xin{1};

% forward pass

i_f = sigmoid(dlarray(W.Wi_f*Xin+R.Ri_f*h_prev_f+b.bi_f));

f_f = sigmoid(dlarray(W.Wf_f*Xin+R.Rf_f*h_prev_f+b.bf_f));

g_f = tanh(dlarray(W.Wg_f*Xin+R.Rg_f*h_prev_f+b.bg_f));

o_f = sigmoid(dlarray(W.Wo_f*Xin+R.Ro_f*h_prev_f+b.bo_f));

c_f = f_f.*c_prev_f+i_f.*g_f;

h_f = o_f.*tanh(c_f);

% backward pass

i_b = sigmoid(dlarray(W.Wi_b*Xin+R.Ri_b*h_prev_b+b.bi_b));

f_b = sigmoid(dlarray(W.Wf_b*Xin+R.Rf_b*h_prev_b+b.bf_b));

g_b = tanh(dlarray(W.Wg_b*Xin+R.Rg_b*h_prev_b+b.bg_b));

o_b = sigmoid(dlarray(W.Wo_b*Xin+R.Ro_b*h_prev_b+b.bo_b));

c_b = f_b.*c_prev_b+i_b.*g_b;

h_b = o_b.*tanh(c_b);

end

c = [c_f; c_b];

h = [h_f; h_b];

hidden_state_actual = hidden_state_actual(:, end);

% comparing custom implementation and implementation by matlab

fprintf('Cell state: %f\n', sum(abs(single(c)-cell_state_actual)));

fprintf('Hidden state: %f\n', sum(abs(single(h)-hidden_state_actual)));

I notice that the output of the forward pass by matlab function can be replicated, only if the backward cell states and hidden states are reset after every sequence.

Is this a bug?

Thanks in advance.

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Aditya 2024-2-26

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1717220-bilstm-empty-backward-cell-states-and-hidden-states#answer_1416378

Although it might not be a bug, it might be a feature unique to MATLAB's handling of state updates in bidirectional LSTM (BiLSTM) layers when classifyAndUpdateState is used. Both forward and backward data processing is done by the BiLSTM layer, and information from the full sequence should be captured in the final states.

The backward pass of a BiLSTM typically begins at the end of the sequence and travels towards the beginning during a forward pass. Because the backward state does not yet have any future context, it is assumed to be zero-initialized for each new sequence while processing a sequence one time step at a time.

However, when you process the entire sequence at once, MATLAB's classifyAndUpdateState function should update both the forward and backward states across the whole sequence. If the backward states remain zero after processing the entire sequence, it could indicate an issue with how the states are being handled or updated.

In your code snippet, it appears that you are manually resetting the backward cell states and hidden states to zero after every sequence (as indicated by the comments in the code). This would not be the correct approach when processing the entire sequence at once, as the backward states should carry information from the end of the sequence back to the begining.