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Example

import N2D2 import numpy path = "/nvme0/DATABASE/MNIST/raw/" batchSize = 1 nb_epochs = 1 epoch_size = 1

In this section we will create a simple convolutional neural network for the dataset MNIST using the python binding of N2D2.

Creation of the network

We first have to create an object Network. This object will be the backbone of the model, linking the different cells.

We have to begin with the initialisation of this object since it creates a seed that generates randomness.

net = N2D2.Network() deepNet = N2D2.DeepNet(net)

Importation of the dataset

To import the MNIST dataset we will use a custom class :pyN2D2.MNIST_IDX_Database.

database = N2D2.MNIST_IDX_Database() database.load(path)

In the following code, the path variable represent the path to the dataset MNIST.

Applying transformation to the dataset

We can create transformation using the class :pyN2D2.Transformation.

trans = N2D2.DistortionTransformation() trans.setParameter("ElasticGaussianSize", "21") trans.setParameter("ElasticSigma", "6.0") trans.setParameter("ElasticScaling", "36.0") trans.setParameter("Scaling", "10.0") trans.setParameter("Rotation", "10.0") padcrop = N2D2.PadCropTransformation(24, 24)

But to apply them to the data, we need :pyN2D2.StimuliProvider.

:pyN2D2.StimuliProvider is a class that acts as a data loader for the neural network.

stimuli = N2D2.StimuliProvider(database, [24, 24, 1], batchSize, False) stimuli.addTransformation(N2D2.PadCropTransformation(24, 24), database.StimuliSetMask(0)) stimuli.addOnTheFlyTransformation(trans, database.StimuliSetMask(0))

We can apply transformation in two ways. The first one is the standard one, we apply the transformation once to the whole dataset. This is useful for transformation like normalization or :pyN2D2.PadCropTransformation. The other way is to add the transformation "on the fly", this mean that each time we load a data, we apply the transformation. This is especially adapted to random transformation like :pyN2D2.DistortionTransformation since you add more diversity to the data.

Defining network topology

To define our network topology, we use :pyN2D2.Cell objects.

conv1 = N2D2.ConvCell_Frame_float(deepNet, "conv1", [4, 4], 16, [1, 1], [2, 2], [5, 5], [1, 1], N2D2.TanhActivation_Frame_float()) conv2 = N2D2.ConvCell_Frame_float(deepNet, "conv2", [5, 5], 24, [1, 1], [2, 2], [5, 5], [1, 1], N2D2.TanhActivation_Frame_float()) fc1 = N2D2.FcCell_Frame_float(deepNet, "fc1", 150, N2D2.TanhActivation_Frame_float()) fc2 = N2D2.FcCell_Frame_float(deepNet, "fc2", 10, N2D2.TanhActivation_Frame_float())

Once the cells are created, we need to connect them.

conv2mapping = [

True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, True, True, True, True, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, True, True, False, True, True, False, False, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, True, True, False, False, True, True, False, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, True, True, False, False, False, True, True, False, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, True, True, False, False, False, False, True, True, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, True, True, False, False, False, False, False, True, True, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, True, True, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, False, False, True, True, False, False, False, True, True, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, False, False, True, True, False, False, True, True, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, False, True, True, False, False, True, True, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, False, True, True, False, True, True, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, True, True, False, True, True, False, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, False, True, True, True, True, False, False, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, True, True, True, True, False, False, False, False, False, False, False, False, False, False, False, False, False, True, True, False, False, False, False, False, False, True, True, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, True, True, True]

t_conv2mapping = N2D2.Tensor_bool(numpy.array(conv2mapping))

conv1.addInput(stimuli) conv2.addInput(conv1, t_conv2mapping) fc1.addInput(conv2) fc2.addInput(fc1)

The first layer receive the :pyN2D2.StimuliProvider class as an input. The other layers have their input set with the previous cell. In this example, we also create a different mapping for the :pyN2D2.ConvCell_Frame_float conv2.

Learning phase

Once the network is created, we can begin the learning phase. First, we need to create a :pyN2D2.Target object. This object defines the output of the network.

tar = N2D2.TargetScore('target', fc2, stimuli)

conv1.initialize() conv2.initialize() fc1.initialize() fc2.initialize()

Finally, we can initiate the learning loop.

for epoch in range(nb_epochs):
for i in range(epoch_size):

stimuli.readRandomBatch(set=N2D2.Database.Learn) tar.provideTargets(N2D2.Database.Learn) conv1.propagate() conv2.propagate() fc1.propagate() fc2.propagate() tar.process(N2D2.Database.Learn) fc2.backPropagate() fc1.backPropagate() conv2.backPropagate() conv1.backPropagate() conv1.update() conv2.update() fc1.update() fc2.update()