first real commit

Author: Greg

NN_shape_test.sh

@@ -0,0 +1,12 @@
+echo "----------- NN Size Experiment ---------------"
+
+echo 'zero. Zero indexed by mistake, but its staying this way'
+#python batch_mnistNN.py --error-file 'train_error_online_NN0.pickle' --test-error-on 1 --test-error-file 'test_error_online_NN0.pickle' --alpha .02 --batch-size 1 --NN-setup 0 --num-epochs 3
+echo 'one'
+python batch_mnistNN.py --error-file 'pickles/train_error_online_NN1.pickle' --test-error-on 1 --test-error-file 'pickles/test_error_online_NN1.pickle' --alpha .02 --batch-size 1 --NN-setup 1 --num-epochs 3
+echo 'two'
+python batch_mnistNN.py --error-file 'pickles/train_error_online_NN2.pickle' --test-error-on 1 --test-error-file 'pickles/test_error_online_NN2.pickle' --alpha .02 --batch-size 1 --NN-setup 2 --num-epochs 3
+echo 'three'
+python batch_mnistNN.py --error-file 'pickles/train_error_online_NN3.pickle' --test-error-on 1 --test-error-file 'pickles/test_error_online_NN3.pickle' --alpha .02 --batch-size 1 --NN-setup 3 --num-epochs 3
+echo 'four'
+python batch_mnistNN.py --error-file 'pickles/train_error_online_NN4.pickle' --test-error-on 1 --test-error-file 'pickles/test_error_online_NN4.pickle' --alpha .02 --batch-size 1 --NN-setup 4 --num-epochs 3

batch_mnistNN.py

@@ -0,0 +1,152 @@
+from batch_neural_network import *
+import numpy as np
+import itertools
+import pickle
+import argparse
+
+def reshapeInstance(t_input, t_label, numElements):
+
+  #reshape and scale // make it a list
+  t_input = np.reshape(t_input,(numElements,1))
+  t_input = (t_input + 1)/257.
+  t_input = t_input.tolist()
+  t_input = list(itertools.chain.from_iterable(t_input))
+
+  #make it a list
+  t_label = t_label.tolist()
+  t_label = list(itertools.chain.from_iterable(t_label))
+
+  return (t_input, t_label)
+
+def main():
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--error-file', type=str, default='error_batch.pickle')
+  parser.add_argument('--test-error-file', type=str, default='test_error_batch.pickle')
+  parser.add_argument('--test-error-on', type=int, default=0);
+  parser.add_argument('--NN-setup', type=int, default = 0)
+
+  parser.add_argument('--num-epochs', type=int, default=3, help='no help for you!')
+  parser.add_argument('--alpha', type=float, default=.03)
+  parser.add_argument('--batch-size', type=int, default=2, help='number of examples per batch, -1 is full batch')
+  args = parser.parse_args()
+  error_file = args.error_file
+  test_error_file = args.test_error_file;
+  test_error_on = args.test_error_on;
+  nn_setup = args.NN_setup;
+
+  #config parameters
+  numEpochs = args.num_epochs
+  alpha = args.alpha
+  batchSize = args.batch_size
+
+  # loading in that good good data
+  trainImages = np.load('data/train_images.npy')
+  trainLabels = np.load('data/train_labels.npy')
+  
+  testImages = np.load('data/test_images.npy')
+  testLabels = np.load('data/test_labels.npy')
+
+  imSize = np.shape(trainImages[0])
+  inputSize = imSize[0]*imSize[1]
+  numLabels = len(trainLabels[0])
+
+  #print '# features in:',inputSize
+  #print '# possibe labels out:',numLabels
+
+  #Neural Network Definition
+  hiddenLayerDimensionList = [[350,175,85,40,20,numLabels], [300,150,75,40,numLabels], [280,130,60,numLabels], [250,100,numLabels], [175,numLabels]];
+  hiddenLayerDimensions = hiddenLayerDimensionList[nn_setup];
+  # hiddenLayerDimensions = [ 300, 30, numLabels ]
+
+  NN = BatchNeuralNetwork(hiddenLayerDimensions, inputSize,alpha)
+  correctTrain = 0.0
+  error_over_iters = np.array([], dtype='float64')
+  test_error_over_iters = np.array([], dtype='float64')
+
+  if batchSize == -1: batchSize = len(trainLabels)
+
+  for j in range(0,numEpochs):
+    i_last = 0
+    for i in range(0,len(trainLabels),batchSize): 
+
+      upperBound = i+batchSize
+      if upperBound > len(trainLabels) - 1:
+        upperBound = len(trainLabels) - 1
+
+      inputs = trainImages[i:upperBound]
+      labels = trainLabels[i:upperBound]
+
+      train_inputs = [];
+      train_labels = [];
+      #in case the last batch isn't full size
+      bsBatchSize = upperBound - i 
+      for k in range(0, bsBatchSize):
+        (train_input, train_label) = reshapeInstance(inputs[k], labels[k], inputSize)
+        train_inputs.append(train_input)
+        train_labels.append(train_label)
+
+      y_hats = NN.feedForward(train_inputs)
+      outputDeltas = NN.computeDError(train_labels, y_hats)
+      NN.feedBack(outputDeltas)
+
+      # print y_hats
+      for a in range(0,len(y_hats)):
+        y_hat = y_hats[a]
+        train_label = train_labels[a]
+        same = (np.argmax(np.asarray(y_hat)) == np.argmax(np.asarray(train_label)))
+        correctTrain += float(same)
+
+      if (i % 1000 == 0 and i != 0):
+        correctTest = 0.0;
+        denom = i - i_last
+        i_last = i
+        train_accuracy = 100 - (100 * (float(correctTrain)/float(denom)))
+        print 'Epoch//Sample:',j,'//',i
+        print 'Train Error:', int(train_accuracy),'%'
+        correctTrain = 0
+        error_over_iters = np.append(error_over_iters, train_accuracy)
+        
+        if test_error_on:
+          correct = 0;
+          for idx in range(0,len(testLabels),batchSize):
+
+            upperBound = idx+batchSize
+            if upperBound > len(trainLabels) - 1:
+              upperBound = len(trainLabels) - 1
+
+            inputs = testImages[idx:upperBound]
+            labels = testLabels[idx:upperBound]
+
+            # print labels
+
+            test_inputs = [];
+            test_labels = [];
+            #in case the last batch isn't full size
+            bsBatchSize = upperBound - idx 
+            for k in range(0, bsBatchSize):
+              (test_input, test_label) = reshapeInstance(inputs[k], labels[k], inputSize)
+              test_inputs.append(test_input)
+              test_labels.append(test_label)
+
+            y_hats = NN.feedForward(test_inputs)
+            
+            for a in range(0,len(y_hats)):
+              y_hat = y_hats[a]
+              test_label = test_labels[a]
+
+              same = (np.argmax(np.asarray(y_hat)) == np.argmax(np.asarray(test_label)))
+              correctTest += float(same)
+
+          test_error =  100 - (100 * (float(correctTest)/float(idx+1)));
+          test_error_over_iters = np.append(test_error_over_iters, test_error);
+          print 'Test Error:', int(test_error), '%'
+
+  with open(error_file, 'wb') as f:
+    pickle.dump(error_over_iters, f)
+  
+  if test_error_on:
+    with open(test_error_file, 'wb') as f:
+      pickle.dump(test_error_over_iters, f)
+
+if __name__ == '__main__':
+  main()