Is it possible to create CNN in Python + PyBrain ?

If so, will it make it difficult for you to write how to do this?

Closed due to the fact that the question is too common for the participants cheops , Kromster , user194374, D-side , Streletz Jun 18 '16 at 7:42 .

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1 answer 1

Can. An example from the official githab :

from __future__ import print_function from pybrain.structure.modules.linearlayer import LinearLayer from pybrain.structure.modules.tanhlayer import TanhLayer from pybrain.structure.moduleslice import ModuleSlice from pybrain.structure.networks.feedforward import FeedForwardNetwork from pybrain.structure.connections.shared import MotherConnection, SharedFullConnection from pybrain.structure.modules.sigmoidlayer import SigmoidLayer __author__ = 'Tom Schaul, tom@idsia.ch' # TODO: code up a more general version # TODO: use modulemash.viewonflatlayer() class SimpleConvolutionalNetwork(FeedForwardNetwork): """ A network with a specific form of weight-sharing, on a single 2D layer, convoluting neighboring inputs (within a square). """ def __init__(self, inputdim, insize, convSize, numFeatureMaps, **args): FeedForwardNetwork.__init__(self, **args) inlayer = LinearLayer(inputdim * insize * insize) self.addInputModule(inlayer) self._buildStructure(inputdim, insize, inlayer, convSize, numFeatureMaps) self.sortModules() def _buildStructure(self, inputdim, insize, inlayer, convSize, numFeatureMaps): #build layers outdim = insize - convSize + 1 hlayer = TanhLayer(outdim * outdim * numFeatureMaps, name='h') self.addModule(hlayer) outlayer = SigmoidLayer(outdim * outdim, name='out') self.addOutputModule(outlayer) # build shared weights convConns = [] for i in range(convSize): convConns.append(MotherConnection(convSize * numFeatureMaps * inputdim, name='conv' + str(i))) outConn = MotherConnection(numFeatureMaps) # establish the connections. for i in range(outdim): for j in range(outdim): offset = i * outdim + j outmod = ModuleSlice(hlayer, inSliceFrom=offset * numFeatureMaps, inSliceTo=(offset + 1) * numFeatureMaps, outSliceFrom=offset * numFeatureMaps, outSliceTo=(offset + 1) * numFeatureMaps) self.addConnection(SharedFullConnection(outConn, outmod, outlayer, outSliceFrom=offset, outSliceTo=offset + 1)) for k, mc in enumerate(convConns): offset = insize * (i + k) + j inmod = ModuleSlice(inlayer, outSliceFrom=offset * inputdim, outSliceTo=offset * inputdim + convSize * inputdim) self.addConnection(SharedFullConnection(mc, inmod, outmod)) if __name__ == '__main__': from scipy import array, ravel from .custom.convboard import ConvolutionalBoardNetwork from pybrain.rl.environments.twoplayergames.tasks import CaptureGameTask N = ConvolutionalBoardNetwork(4, 3, 5) input = [[[0, 0], [0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0], [1, 1]], [[0, 0], [1, 1], [0, 0], [0, 1]], [[0, 0], [1, 0], [1, 1], [0, 1]], ] res = N.activate(ravel(array(input))) res = res.reshape(4, 4) print((N['pad'].inputbuffer[0].reshape(6, 6, 2)[:, :, 0])) print(res) t = CaptureGameTask(4) print((t(N))) if False: N = SimpleConvolutionalNetwork(4, 2, 5) print(N) res = N.activate(ravel(array(input))) res = res.reshape(3, 3) print(res)