I am working on the MNIST dataset and using data augmentation to train a neural network. I have a BatchIterator which randomly extracts from each picture a 24, 24 subimage, and uses it as input for the NN.
As far as training is concerned, everything goes fines. But for prediction, I want to extract 5 sub-images from a given image, and average the predictions, but I cannot get it to work:
Here's my BatchIterator:
class CropIterator(BatchIterator):
def __init__(self, batch_size, crop=4, testing=False):
super(CropIterator, self).__init__(batch_size)
self.testing = testing
self.crop = crop
def transform(self, Xb, yb):
crop = self.crop
batch_size, channels, width, height = Xb.shape
if not self.testing:
y_new = yb
X_new = np.zeros([batch_size, channels, width - crop, height - crop]).astype(np.float32)
for i in range(batch_size):
x = np.random.randint(0, crop+1)
y = np.random.randint(0, crop+1)
X_new[i] = Xb[i, :, x:x+width-crop, y:y+height-crop]
else:
X_new = np.zeros([5 * batch_size, channels, width - crop, height - crop]).astype(np.float32)
y_new = np.zeros(5 * batch_size).astype(np.int32)
for i in range(batch_size):
for idx, position in enumerate([(0,0), (0, crop), (crop, 0), (crop, crop), (crop//2, crop//2)]):
# all extreme cropppings + the middle one
x_idx = position[0]
y_idx = position[1]
X_new[5*i+idx, :] = Xb[i, :, x_idx:x_idx+width-crop, y_idx:y_idx+height-crop]
y_new[5*i+idx] = yb[i]
return X_new, y_new
Fitting my net to the training data works, but when I do net.predict(X_test), I get an error because CropIterator.transform() is, I believe, called with yb equal to None.
here's the full call stack:
/usr/local/lib/python2.7/site-packages/nolearn/lasagne/base.pyc in predict(self, X)
526 return self.predict_proba(X)
527 else:
--> 528 y_pred = np.argmax(self.predict_proba(X), axis=1)
529 if self.use_label_encoder:
530 y_pred = self.enc_.inverse_transform(y_pred)
/usr/local/lib/python2.7/site-packages/nolearn/lasagne/base.pyc in predict_proba(self, X)
518 def predict_proba(self, X):
519 probas = []
--> 520 for Xb, yb in self.batch_iterator_test(X):
521 probas.append(self.apply_batch_func(self.predict_iter_, Xb))
522 return np.vstack(probas)
/usr/local/lib/python2.7/site-packages/nolearn/lasagne/base.pyc in __iter__(self)
78 else:
79 yb = None
---> 80 yield self.transform(Xb, yb)
81
82 @property
<ipython-input-56-59463a9f9924> in transform(self, Xb, yb)
33 y_idx = position[1]
34 X_new[5*i+idx, :] = Xb[i, :, x_idx:x_idx+width-crop, y_idx:y_idx+height-crop]
---> 35 y_new[5*i+idx] = yb[i]
36 return X_new, y_new
37
TypeError: 'NoneType' object has no attribute '__getitem__'
Any idea on how to fix it in the testing part of CropIterator.transform() ?
Looking at the code for nolearn.lasagne.BatchIterator and how it is used by the nolearn.lasagne.NeuralNet class, it looks like BatchIterators need to work when y is not provided, i.e. in prediction mode. Note the call at line 520 where X is provided but no value is given for y so it defaults to None.
Your CropIterator currently assumes that yb is always a non-None value. I don't know if it makes sense to do anything useful when yb is not provided but I assume you could just transform Xb and return None for y_new if yb is None.