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Anano - Shared variable as input function for a large data set

I'm new to Theano ... We apologize if this is obvious.

I am trying to train CNN based on the LeNet tutorial . The main difference from the tutorial is that my data set is too large to fit in memory, so I have to load each batch during training.

The original model has the following:

train_model = theano.function(
    [index],
    cost,
    updates=updates,
    givens={
        x: train_set_x[index * batch_size: (index + 1) * batch_size],
        y: train_set_y[index * batch_size: (index + 1) * batch_size]
    }
)

... Which does not work for me, since it assumes that it is train_set_xfully loaded into memory.

So, I switched to this:

train_model = theano.function([x,y], cost, updates=updates)

And tried to call him:

data, target = load_data(minibatch_index)  # load_data returns typical numpy.ndarrays for a given minibatch

data_shared = theano.shared(np.asarray(data, dtype=theano.config.floatX), borrow=True)
target_shared = T.cast(theano.shared(np.asarray(target, dtype=theano.config.floatX), borrow=True), 'int32')

cost_ij = train_model(data_shared ,target_shared )

But received:

TypeError: ( " anano ": 103 " 0 ( 0)", " , ", : , (, ) ? ')

, , Theano. , ...?

+4
1

Theano ( theano.function(...)) , numpy. - numpy , , .

, :

cost_ij = train_model(data, target)

: GPU, , , , , GPU , , , ; , GPU.

+5

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