test_face_all.m

addpath core/
addpath optimization/
addpath results/
addpath utils/
addpath data/

clearvars -global config;
global config mem;
gpuDevice(1);

load('results/speaker-naming/face_only/pre-train.mat');
config = model;
lstm_init_v52();
config.weights = model.weights;
config.data_mean = model.data_mean;
config.one_over_data_std = model.one_over_data_std;

load('data/speaker-naming/processed_training_data/val_face/1');
% test_samples = test_samples(:,:,1:2000);
% test_labels = test_labels(:,1:2000);
test_labels = reshape(test_labels, size(test_labels,1), 1, size(test_labels,2));
test_labels = repmat(test_labels, [1 config.max_time_steps 1]);
test_samples = config.NEW_MEM(test_samples);
test_labels = config.NEW_MEM(test_labels);
test_samples = bsxfun(@times, bsxfun(@minus, test_samples, config.data_mean), config.one_over_data_std);

correct_num = 0;
for ii = 1:size(test_samples, 3)/config.batch_size
    fprintf('%d/%d\n', ii * config.batch_size, floor(size(test_samples, 3)/config.batch_size)*config.batch_size);
    start_idx = config.batch_size * (ii-1) + 1;
    end_idx = start_idx + config.batch_size - 1;

    val_sample = test_samples(:,:,start_idx:end_idx);
    val_label = test_labels(:,:,start_idx:end_idx);                    

    lstm_core_v52(val_sample, 1);

    [value, estimated_labels] = max(mem.net_out(:,end,:));
    [value, true_labels] = max(val_label(:,end,:));
    correct_num = correct_num + length(find(estimated_labels == true_labels));
end

acc = correct_num / double(floor(size(test_samples, 3)/config.batch_size)*config.batch_size);
fprintf('\nval_acc: %.2f%%\n', acc*100);