Hi,
It's hard to read your code because you haven't reduced it to a minimal
example. However, you can set the value of a shared variable using
<variable>.set_value():
x = theano.shared(0.)
print(x.get_value())
x.set_value(1)
print(x.get_value())
You might be creating new shared variables when you don't mean to?
Keep in mind that that writing x = theano.shared(0.) creates a new shared
variable object and binds it to the name x. If an object named x previously
exists, assignment does not change the original object.
Best luck.
On Saturday, January 28, 2017 at 11:01:09 PM UTC-7, chathu matharage wrote:
>
> I'm training convolution neural network using firefly algorithm. Although
> I update the weights of the network they seems not updated. So how to
> update the network weights manually?
> This is how I update the weights
>
> def set_params(params, bestSolution, layers=[layer0, layer1, layer2,
> layer21, layer3]):
> # print('Best sol: ', bestSolution)
> # print('params: ', params)
>
> l0w = bestSolution[0:150]
> l0b = bestSolution[150:156]
> l1w = bestSolution[156:2556]
> l1b = bestSolution[2556:2572]
> l2w = bestSolution[2572:33292]
> l2b = bestSolution[33292:33412]
> l21w = bestSolution[33412:43492]
> l21b = bestSolution[43492:43576]
> l3w = bestSolution[43576:44416]
> l3b = bestSolution[44416:44426]
>
>
> # first conpool layer weights
> params[8] = theano.shared(
> numpy.reshape(numpy.asarray(l0w, dtype=theano.config.floatX), (6,
> 1, 5, 5)),
> borrow=True
> )
> layers[0].params[0] = params[8]
> # print('before: ')
> # layers[0].ppp()
> layers[0].W = params[8]
> # print('after: ')
> # layers[0].ppp()
>
>
>
> # first conpool layer biases
> params[9] = theano.shared(numpy.reshape(numpy.asarray(l0b,
> dtype=theano.config.floatX), (6)), borrow=True)
> layers[0].params[1] = params[9]
> layers[0].b = params[9]
>
> # second conpool layer weights
> params[6] = theano.shared(
> numpy.reshape(numpy.asarray(l1w, dtype=theano.config.floatX), (16,
> 6, 5, 5)),
> borrow=True
> )
> layers[1].params[0] = params[6]
> layers[1].W = params[6]
>
> # second conpool layer biases
> params[7] = theano.shared(numpy.reshape(numpy.asarray(l1b,
> dtype=theano.config.floatX), (16)), borrow=True)
> layers[1].params[1] = params[7]
> layers[1].b = params[7]
>
> # first hidden layer weights
> params[4] = theano.shared(numpy.reshape(numpy.asarray(l2w,
> dtype=theano.config.floatX), (256, 120)), name='W',
> borrow=True)
> layers[2].params[0] = params[4]
> layers[2].W = params[4]
>
> # first hidden layer biases
> params[5] = theano.shared(numpy.reshape(numpy.array(l2b,
> dtype=theano.config.floatX), (120)), name='b', borrow=True)
> layers[2].params[1] = params[5]
> layers[2].b = params[5]
>
> # second hidden layer weights
> params[2] = theano.shared(numpy.reshape(numpy.asarray(l21w,
> dtype=theano.config.floatX), (120, 84)), name='W',
> borrow=True)
> layers[3].params[0] = params[2]
> layers[3].W = params[2]
>
> # second hidden layer biases
> params[3] = theano.shared(numpy.reshape(numpy.asarray(l21b,
> dtype=theano.config.floatX), (84)), name='b',
> borrow=True)
> layers[3].params[1] = params[3]
> layers[3].b = params[3]
>
> # output layer weights
> params[0] = theano.shared(
> numpy.reshape(numpy.asarray(l3w, dtype=theano.config.floatX), (84,
> 10)),
> name='W',
> borrow=True
> )
> layers[4].params[0] = params[0]
> layers[4].W = params[0]
>
> # output layer biases
> params[1] = theano.shared(
> numpy.reshape(numpy.asarray(l3b, dtype=theano.config.floatX),
> (10)),
> name='b',
> borrow=True
> )
> layers[4].params[1] = params[1]
> layers[4].b = params[1]
>
> firefly = params[0] + params[1] + params[2] + params[3] + params[4] +
> params[5] + params[6] + params[7] + params[8] + params[9]
> return firefly
>
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