How to use the daal.algorithms.neural_networks.initializers.uniform.Batch function in daal

convolution2.parameter.kernelSizes = convolution2d.KernelSizes(5, 5)
    convolution2.parameter.strides = convolution2d.Strides(1, 1)
    convolution2.parameter.nKernels = 64
    convolution2.parameter.weightsInitializer = initializers.xavier.Batch()
    convolution2.parameter.biasesInitializer = initializers.uniform.Batch(0, 0)

    # Create pooling layer
    maxpooling2 = maximum_pooling2d.Batch(4)
    maxpooling2.parameter.kernelSizes = pooling2d.KernelSizes(2, 2)
    maxpooling2.parameter.paddings = pooling2d.Paddings(0, 0)
    maxpooling2.parameter.strides = pooling2d.Strides(2, 2)

    # Create fullyconnected layer
    fullyconnected3 = fullyconnected.Batch(256)
    fullyconnected3.parameter.weightsInitializer = initializers.xavier.Batch()
    fullyconnected3.parameter.biasesInitializer = initializers.uniform.Batch(0, 0)

    # Create ReLU layer
    relu3 = relu.Batch()

    # Create fully connected layer
    fullyconnected4 = fullyconnected.Batch(10)
    fullyconnected4.parameter.weightsInitializer = initializers.xavier.Batch()
    fullyconnected4.parameter.biasesInitializer = initializers.uniform.Batch(0, 0)

    # Create Softmax layer
    softmax = loss.softmax_cross.Batch()

    # Create LeNet Topology
    topology = training.Topology()
    conv1 = topology.add(convolution1)
    pool1 = topology.add(maxpooling1)

def configureNet():

    # Create layers of the neural network
    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer1 = layers.fullyconnected.Batch(20)

    fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)

    fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer2 = layers.fullyconnected.Batch(40)

    fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)

    fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer3 = layers.fullyconnected.Batch(2)

    fullyConnectedLayer3.parameter.weightsInitializer = initializers.uniform.Batch(-0.005, 0.005)

    fullyConnectedLayer3.parameter.biasesInitializer = initializers.uniform.Batch(0, 1)

    # Create softmax layer and initialize layer parameters

def configureNet():
    # Create layers of the neural network
    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer1 = layers.fullyconnected.Batch(5)
    fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
    fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer2 = layers.fullyconnected.Batch(2)
    fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
    fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)

    # Create softmax layer and initialize layer parameters
    softmaxCrossEntropyLayer = layers.loss.softmax_cross.Batch()

    # Create configuration of the neural network with layers
    topology = training.Topology()

    # Add layers to the topology of the neural network
    topology.push_back(fullyConnectedLayer1)
    topology.push_back(fullyConnectedLayer2)
    topology.push_back(softmaxCrossEntropyLayer)
    topology.get(fc1).addNext(fc2)
    topology.get(fc2).addNext(sm1)
    return topology

fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer2 = layers.fullyconnected.Batch(40)

    fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)

    fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer3 = layers.fullyconnected.Batch(2)

    fullyConnectedLayer3.parameter.weightsInitializer = initializers.uniform.Batch(-0.005, 0.005)

    fullyConnectedLayer3.parameter.biasesInitializer = initializers.uniform.Batch(0, 1)

    # Create softmax layer and initialize layer parameters
    softmaxCrossEntropyLayer = loss.softmax_cross.Batch()

    # Create topology of the neural network
    topology = training.Topology()

    # Add layers to the topology of the neural network
    fc1 = topology.add(fullyConnectedLayer1)
    fc2 = topology.add(fullyConnectedLayer2)
    fc3 = topology.add(fullyConnectedLayer3)
    sm = topology.add(softmaxCrossEntropyLayer)
    topology.get(fc1).addNext(fc2)
    topology.get(fc2).addNext(fc3)
    topology.get(fc3).addNext(sm)

def configureNet():
    m2 = 40
    # Create layers of the neural network
    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer1 = layers.fullyconnected.Batch(20)
    fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
    fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer2 = layers.fullyconnected.Batch(m2)
    fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
    fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer3 = layers.fullyconnected.Batch(2)
    fullyConnectedLayer3.parameter.weightsInitializer = initializers.uniform.Batch(-0.005, 0.005)
    fullyConnectedLayer3.parameter.biasesInitializer = initializers.uniform.Batch(0, 1)

    # Create softmax layer and initialize layer parameters
    softmaxCrossEntropyLayer =  layers.loss.softmax_cross.Batch()

    # Create topology of the neural network

def configureNet():
    m2 = 40
    # Create layers of the neural network
    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer1 = layers.fullyconnected.Batch(20)
    fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
    fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer2 = layers.fullyconnected.Batch(m2)
    fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
    fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer3 = layers.fullyconnected.Batch(2)
    fullyConnectedLayer3.parameter.weightsInitializer = initializers.uniform.Batch(-0.005, 0.005)
    fullyConnectedLayer3.parameter.biasesInitializer = initializers.uniform.Batch(0, 1)

    # Create softmax layer and initialize layer parameters
    softmaxCrossEntropyLayer =  layers.loss.softmax_cross.Batch()

    # Create topology of the neural network
    topology = training.Topology()

    # Add layers to the topology of the neural network
    fc1 = topology.add(fullyConnectedLayer1)
    fc2 = topology.add(fullyConnectedLayer2)

def configureNet():
    # Create layers of the neural network
    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer1 = layers.fullyconnected.Batch(5)
    fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
    fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer2 = layers.fullyconnected.Batch(2)
    fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
    fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)

    # Create softmax layer and initialize layer parameters
    softmaxCrossEntropyLayer = layers.loss.softmax_cross.Batch()

    # Create configuration of the neural network with layers
    topology = training.Topology()

    # Add layers to the topology of the neural network
    topology.push_back(fullyConnectedLayer1)
    topology.push_back(fullyConnectedLayer2)
    topology.push_back(softmaxCrossEntropyLayer)
    topology.get(fc1).addNext(fc2)
    topology.get(fc2).addNext(sm1)
    return topology

def configureNet():
    m2 = 40
    # Create layers of the neural network
    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer1 = layers.fullyconnected.Batch(20)
    fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
    fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer2 = layers.fullyconnected.Batch(m2)
    fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
    fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer3 = layers.fullyconnected.Batch(2)
    fullyConnectedLayer3.parameter.weightsInitializer = initializers.uniform.Batch(-0.005, 0.005)
    fullyConnectedLayer3.parameter.biasesInitializer = initializers.uniform.Batch(0, 1)

    # Create softmax layer and initialize layer parameters
    softmaxCrossEntropyLayer =  layers.loss.softmax_cross.Batch()

    # Create topology of the neural network
    topology = training.Topology()

def configureNet():
    # Create layers of the neural network
    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer1 = layers.fullyconnected.Batch(5)
    fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
    fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)

    # Create fully-connected layer and initialize layer parameters
    fullyConnectedLayer2 = layers.fullyconnected.Batch(2)
    fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
    fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)

    # Create softmax layer and initialize layer parameters
    softmaxCrossEntropyLayer = layers.loss.softmax_cross.Batch()

    # Create configuration of the neural network with layers
    topology = training.Topology()

    # Add layers to the topology of the neural network
    topology.push_back(fullyConnectedLayer1)
    topology.push_back(fullyConnectedLayer2)
    topology.push_back(softmaxCrossEntropyLayer)