BACKPROPCLASSIFY.M

Call makebatches.

I.CALCULATE THE TRANING MISCLASSIFICATION ERROR

Initialize err_cr, counter to zero.

w1-first layer weights(Vis-Hid)

w2-second layer weights(Hid-Pen1)

w3-third layer weights(Pen1-Pen2)

w4-final layer weights(Pen2-Output)

1. For each batch of data:

1. First calculate the weight probabilities for each of the hidden layer and the final output layer

For Hidden layer 1(Hidden)Weight probability1 (wp1jk) =1/(1+e-(∑dataik * w1ij +ᶲj)) where data is the data in a single batch and w1 is the weight matrix of connections between visible neurons to neurons in the first hidden layer.

For Hidden layer 2(Pen1)Weight probability2 (wp2jk) =1/(1+e-(∑wp1ik * w2ij +ᶲj));Similarly calculate wp3 for Hidden Layer 3(Pen 2)

But the final output (Pen1 to visible output neurons) is calculated as:targetout= exp (wp3*w_class) e-(∑wp3ik * w_class+ᶲj)); where w_class is the matrix containing weights and biases for the pen2-output layer.target: expected output

For the matrix targetout, sum up each row and divide the entries of that row with the sum of that particular row.2. Find out the maximum values in a particular row along with their index for the matrix targetout and target and store them as I,J,I1,J1 respectively.

3. Now calculate the number of the similar entries in J,J1 and add this number to a counter.Calculate err_cr as: err_cr- ∑10

j=1∑100k=1 targetjk*log(targetoutjk)

2. Calculate train_error for that particular epoch(iteration) as:numcases*numbatches-counter ,where numcases: size of each batch (i.e. 100);numbatches: number of batches and counter was obtained from previous step.3. Calculate train_cerror for that particular epoch as:

err_cr/numbatches

II. CALCULATE THE TEST MISCLASSIFICATION ERRORRepeat the above steps but with training data replaced by test data and calculate test_err, test_cerror.The test_err and train_err represents the number of misclassified samples for a particular epoch(iteration).

III. COMBINE 10 MINIBATCHES INTO A LARGER BATCH

IV. PERFORM CONJUGATE GRADIENT 3 LINESEARCHES FOE EACH SUCH LARGER BATCH

1. If epoch(iteration)<6 then recalculate wp1,wp2 and wp3 and call minimize with CG_CLASSIFY_INIT and elements in w_class as arguments. Reshape the obtained result in w_class.

2. Else call minimize with CG_CLASSIFY and elements in w1,w2,w3 and w_class as arguments. Reshape the original result to get back w1,w2,w3 and w_class from it.

3. Save the weights and errors calculated in two separate files.