Loading mixed datatypes into MATLAB Neural Network Toolbox

I'm having difficulty loading my tab-delimited input file into MATLABs nprtool, which I think is because nprtool GUI doesn't support loading of mixed data types. The .tab file I am loading in has roughly 1100 data samples (rows), each of which look like:

864 1342470776.212023000    172.25.177.41   155.34.234.20   HTTP    440 58689   http-alt    GET http://i.cdn.turner.com/cnn/.e/img/3.0/global/header/hdr-main.gif   image/png,image/*;q=0.8,*/*;q=0.5   gzip,deflate            0.000094000     http://www.cnn.com/

The above is just a single sample in the input vector. I've tried loading the file in using the nprtool GUI, but it doesn't recognize the data correctly. It treats it all as 'textdata' with some junk in the 'data' section. I then tried to do it without the GUI via script. This method throws an error (below). Any way around this? Below is a snippet of the script I'm using to load the file and the error. Any help is appreciated. Thanks!

text1 = fopen('/Users/cgarry/Desktop/CRANEUM/output.tab');
pacTargets = importdata('/Users/cgarry/Desktop/CRANEUM/data.tab','\t');
pacInputs = textscan(text1,'%s %s %s %s %s %s %s %s %s %s %s %s %s %s %s %s');
fclose(text1);

inputs = pacInputs;
targets = pacTargets;

% Create a Pattern Recognition Network
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);


% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;


% Train the Network
[net,tr] = train(net,inputs,targets);

% Test the Network
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);

% View the Network
view(net)

% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotconfusion(targets,outputs)
%figure, ploterrhist(errors)


>> nnet
Error using trainscg (line 97)
Inputs X{1,1} is not numeric or logical.

Error in network/train (line 106)
[net,tr] = feval(net.trainFcn,net,X,T,Xi,Ai,EW,net.trainParam);

Error in nnet (line 21)
[net,tr] = train(net,inputs,targets);