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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