lasagne

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

How to implement Weighted Binary CrossEntropy on theano? My Convolutional neural network only predict 0 ~~ 1 (sigmoid). I want to penalize my predictions in this way : Basically, i want to penalize MORE when the model predicts 0 but the truth was 1. Question : How can I create this Weighted Binary CrossEntropy function using theano and lasagne ? I tried this below prediction = lasagne.layers.get_output(model) import theano.tensor as T def weighted_crossentropy(predictions, targets): # Copy the tensor tgt = targets.copy("tgt") # Make it a vector # tgt = tgt.flatten() # tgt = tgt.reshape(3000) #

I loaded the mnist_conv.py example from official github of Lasagne. At the and, I would like to predict my own example. I saw that "lasagne.layers.get_output()" should handle numpy arrays from official documentation, but it doesn't work and I cannot figure out how can I do that. Here's my code: if __name__ == '__main__': output_layer = main() #the output layer from the net exampleChar = np.zeros((28,28)) #the example I would predict outputValue = lasagne.layers.get_output(output_layer, exampleChar) print(outputValue.eval()) but it gives me: TypeError: ConvOp (make_node) requires input be a 4D

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 (