nolearn

I need to do realtime augmentation on my dataset for input to CNN, but i am having a really tough time finding suitable libraries for it. I have tried caffe but the DataTransform doesn't support many realtime augmentations like rotating etc. So for ease of implementation i settled with Lasagne . But it seems that it also doesn't support realtime augmentation. I have seen some posts related to Facial Keypoints detection where he's using Batchiterator of nolearn.lasagne . But i am not sure whether its realtime or not. There's no proper tutorial for it. So finally how should i do realtime

I tried to use DBN function imported from nolearn package, and here is my code: from nolearn.dbn import DBN import numpy as np from sklearn import cross_validation fileName = 'data.csv' fileName_1 = 'label.csv' data = np.genfromtxt(fileName, dtype=float, delimiter = ',') label = np.genfromtxt(fileName_1, dtype=int, delimiter = ',') clf = DBN( [data, 300, 10], learn_rates=0.3, learn_rate_decays=0.9, epochs=10, verbose=1, ) clf.fit(data,label) score = cross_validation.cross_val_score(clf, data, label,scoring='f1', cv=10) print score Since my data has the shape(1231, 229) and label with the shape

I'm using Lasagne to create a CNN for the MNIST dataset. I'm following closely to this example: Convolutional Neural Networks and Feature Extraction with Python . The CNN architecture I have at the moment, which doesn't include any dropout layers, is: NeuralNet( layers=[('input', layers.InputLayer), # Input Layer ('conv2d1', layers.Conv2DLayer), # Convolutional Layer ('maxpool1', layers.MaxPool2DLayer), # 2D Max Pooling Layer ('conv2d2', layers.Conv2DLayer), # Convolutional Layer ('maxpool2', layers.MaxPool2DLayer), # 2D Max Pooling Layer ('dense', layers.DenseLayer), # Fully connected layer (