controlCNN.py

import graphAttack as ga
import numpy as np
import scipy.optimize

"""Control script"""


def run():
    """Run the model"""
    trainData = np.random.random((5, 1, 10, 10))
    trainLabels = np.random.random((5, 10))

    # ------ conv2D operation testing
    mainGraph = ga.Graph()
    feed = mainGraph.addOperation(ga.Variable(trainData), doGradient=False, feederOperation=True)

    cnn1 = ga.addConv2dLayer(mainGraph,
                             inputOperation=feed,
                             nFilters=3,
                             filterHeigth=5,
                             filterWidth=5,
                             padding="SAME",
                             convStride=1,
                             activation=ga.ReLUActivation,
                             batchNormalisation=True,
                             pooling=ga.MaxPoolOperation,
                             poolHeight=2,
                             poolWidth=2,
                             poolStride=2)

    flattenOp = mainGraph.addOperation(ga.FlattenFeaturesOperation(cnn1))
    flattenDrop = mainGraph.addOperation(ga.DropoutOperation(
        flattenOp, 0.0), doGradient=False, finalOperation=False)

    l1 = ga.addDenseLayer(mainGraph, 20,
                          inputOperation=flattenDrop,
                          activation=ga.ReLUActivation,
                          dropoutRate=0.0,
                          batchNormalisation=False)
    l2 = ga.addDenseLayer(mainGraph, 10,
                          inputOperation=l1,
                          activation=ga.SoftmaxActivation,
                          dropoutRate=0.0,
                          batchNormalisation=False)
    fcost = mainGraph.addOperation(
        ga.CrossEntropyCostSoftmax(l2, trainLabels),
        doGradient=False,
        finalOperation=True)

    def fprime(p, data, labels):
        mainGraph.feederOperation.assignData(data)
        mainGraph.resetAll()
        mainGraph.finalOperation.assignLabels(labels)
        mainGraph.attachParameters(p)
        c = mainGraph.feedForward()
        mainGraph.feedBackward()
        g = mainGraph.unrollGradients()
        return c, g

    def f(p):
        data = trainData
        labels = trainLabels
        mainGraph.feederOperation.assignData(data)
        mainGraph.resetAll()
        mainGraph.finalOperation.assignLabels(labels)
        mainGraph.attachParameters(p)
        c = mainGraph.feedForward()
        return c

    params = mainGraph.unrollGradientParameters()
    numGrad = scipy.optimize.approx_fprime(params, f, 1e-8)
    analCostGraph, analGradientGraph = fprime(params, trainData, trainLabels)
    return numGrad, analGradientGraph, analCostGraph, mainGraph


if (__name__ == "__main__"):
    nGrad, aGrad, aCost, mainGraph = run()
    params = mainGraph.unrollGradientParameters()
    print(mainGraph)

    print("\n%-16.16s %-16.16s" % ("Grad difference", "Total Gradient"))
    print("%-16.8e %-16.8e" % (np.sum(np.abs(aGrad - nGrad)), np.sum(np.abs(aGrad))))