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test_lstm_shanghai.m
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clc
clear
load Shanghai_Gold_Fix_PM
m = 5; % input dimension
l = 256; % hidden node dimension
n = 5; % target dimension
miniBatchSize = 20; % length of LSTM network
data_size = numel(data);
train_size = floor(numel(data)*0.95/miniBatchSize/m)*miniBatchSize*m+m;
test_size = numel(data)-train_size;
train_data = data(1:train_size);
test_data = data(train_size+1:end);
mu = mean(train_data); sig = std(train_data);
std_train_data = (train_data - mu) / sig;
std_test_data = (test_data - mu) / sig;
Wf = 0.01*randn(l,m); Rf = 0.01*randn(l,l); bf = 0.01*randn(l,1);
Wi = 0.01*randn(l,m); Ri = 0.01*randn(l,l); bi = 0.01*randn(l,1);
Wg = 0.01*randn(l,m); Rg = 0.01*randn(l,l); bg = 0.01*randn(l,1);
Wo = 0.01*randn(l,m); Ro = 0.01*randn(l,l); bo = 0.01*randn(l,1);
V = 0.01*randn(n,l); b = 0.01*randn(n,1);
mWf = zeros(l,m); mRf = zeros(l,l); mbf = zeros(l,1);
mWi = zeros(l,m); mRi = zeros(l,l); mbi = zeros(l,1);
mWg = zeros(l,m); mRg = zeros(l,l); mbg = zeros(l,1);
mWo = zeros(l,m); mRo = zeros(l,l); mbo = zeros(l,1);
mV = zeros(n,l); mb = zeros(n,1);
epoch_num = 100;
learning_rate = 0.001;
mnt_rate = 0.9;
numMiniBatch = floor(train_size/miniBatchSize/m); % number of possible full mini-batches
bList = 1:miniBatchSize:(numMiniBatch-1)*miniBatchSize+1; % min-batch index list
tic
for k = 1:5
for epoch = 1:epoch_num
h0 = zeros(l,1);
c0 = zeros(l,1);
L = 0;
for p = 1:numMiniBatch
bStart = (p-1)*miniBatchSize*m+1;
input = [];
target = [];
for i = 1:miniBatchSize
s = bStart + (i-1)*m;
input = [input std_train_data(s:s+m-1)'];
target = [target std_train_data(s+m:s+2*m-1)'];
end
[dWf,dRf,dbf,dWi,dRi,dbi,dWg,dRg,dbg,dWo,dRo,dbo,dV,db,h0,c0,loss,~] = ...
lstm(Wf,Rf,bf,Wi,Ri,bi,Wg,Rg,bg,Wo,Ro,bo,V,b,input,target,h0,c0);
mWf = mnt_rate*mWf - learning_rate*dWf;
mWi = mnt_rate*mWi - learning_rate*dWi;
mWg = mnt_rate*mWg - learning_rate*dWg;
mWo = mnt_rate*mWo - learning_rate*dWo;
mRf = mnt_rate*mRf - learning_rate*dRf;
mRi = mnt_rate*mRi - learning_rate*dRi;
mRg = mnt_rate*mRg - learning_rate*dRg;
mRo = mnt_rate*mRo - learning_rate*dRo;
mbf = mnt_rate*mbf - learning_rate*dbf;
mbi = mnt_rate*mbi - learning_rate*dbi;
mbg = mnt_rate*mbg - learning_rate*dbg;
mbo = mnt_rate*mbo - learning_rate*dbo;
mV = mnt_rate*mV - learning_rate*dV;
mb = mnt_rate*mb - learning_rate*db;
Wf = Wf + mWf; Rf = Rf + mRf; bf = bf + mbf;
Wi = Wi + mWi; Ri = Ri + mRi; bi = bi + mbi;
Wg = Wg + mWg; Rg = Rg + mRg; bg = bg + mbg;
Wo = Wo + mWo; Ro = Ro + mRo; bo = bo + mbo;
V = V + mV; b = b + mb;
L = L + loss;
end
% if (~mod(epoch, 1) || epoch == 1)
% str = sprintf('epoch: %d, loss: %f', epoch, L);
% disp(str);
% end
end
toc
h0 = zeros(l,1);
c0 = zeros(l,1);
for p = 1:numMiniBatch
bStart = (p-1)*miniBatchSize*m+1;
input = [];
for i = 1:miniBatchSize
s = bStart+(i-1)*m;
input = [input std_train_data(s:s+m-1)'];
[h0,c0,yy] = lstm_forward(...
Wf,Rf,bf,Wi,Ri,bi,Wg,Rg,bg,Wo,Ro,bo,V,b,input,h0,c0);
end
end
hend = h0;
cend = c0;
numBTest_size = floor(test_size/m);
pred = [];
for i = 1:numBTest_size
input = std_test_data((i-1)*m+1:i*m)';
[hend,cend,pp] = lstm_forward(...
Wf,Rf,bf,Wi,Ri,bi,Wg,Rg,bg,Wo,Ro,bo,V,b,input,hend,cend);
pred = [pred pp'];
end
pred = pred*sig + mu;
target = std_test_data*sig +mu;
xx = 1:test_size;
figure
plot(xx, target, xx, pred);
[RMSE, MAPE] = eval_error(target, pred);
iter = k*epoch;
str = sprintf('epoch: %d, RMSE: %f, MAPE: %f', iter, RMSE, MAPE);
disp(str);
end