theano

I have a fully connected multilayer perceptron trained in Keras. I feed it an N-dimensional feature vector and it predicts one out of M classes for the input vector. The training and prediction is working well. Now I want to analyze what part of the input feature vector is actually responsible for a particular class. For example, lets say there are two classes A and B , and an input vector f . The vector f belongs to class A and the network predicts it correctly - the output of the network is A=1 B=0 . Because I have some domain knowledge, I know that the entire f is actually not responsible

I've been messing with Keras, and like it so far. There's one big issue I have been having, when working with fairly deep networks: When calling model.train_on_batch, or model.fit etc., Keras allocates significantly more GPU memory than what the model itself should need. This is not caused by trying to train on some really large images, it's the network model itself that seems to require a lot of GPU memory. I have created this toy example to show what I mean. Here's essentially what's going on: I first create a fairly deep network, and use model.summary() to get the total number of parameters

Can someone help me rewrite this one function (the doTheMath function) to do the calculations on the GPU? I used a few good days now trying to get my head around it but to no result. I wonder maybe somebody can help me rewrite this function in whatever way you may seem fit as log as I gives the same result at the end. I tried to use @jit from numba but for some reason it is actually much slower than running the code as usual. With a huge sample size, the goal is to decrease the execution time considerably so naturally I believe the GPU is the fastest way to do it. I'll explain a little what is

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I would like to design a deep net with one (or more) convolutional layers (CNN) and one or more fully connected hidden layers on top. For deep network with fully connected layers there are methods in theano for unsupervised pre-training, e.g., using denoising auto-encoders or RBMs . My question is: How can I implement (in theano) an unsupervised pre-training stage for convolutional layers? I do not expect a full implementation as an answer, but I would appreciate a link to a good tutorial or a reliable reference. This paper describes an approach for building a stacked convolutional autoencoder

I am trying to do a transfer learning; for that purpose I want to remove the last two layers of the neural network and add another two layers. This is an example code which also output the same error. from keras.models import Sequential from keras.layers import Input,Flatten from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.layers.core import Dropout, Activation from keras.layers.pooling import GlobalAveragePooling2D from keras.models import Model in_img = Input(shape=(3, 32, 32)) x = Convolution2D(12, 3, 3, subsample=(2, 2), border_mode='valid', name='conv1')(in

I have a simple NN model for detecting hand-written digits from a 28x28px image written in python using Keras (Theano backend): model0 = Sequential() #number of epochs to train for nb_epoch = 12 #amount of data each iteration in an epoch sees batch_size = 128 model0.add(Flatten(input_shape=(1, img_rows, img_cols))) model0.add(Dense(nb_classes)) model0.add(Activation('softmax')) model0.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) model0.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, Y_test)) score = model0