EEGNet in Keras

EEGNet.py

def EEGNet(nb_classes, Chans = 64, Samples = 128, 
             dropoutRate = 0.5, kernLength = 64, F1 = 8, 
             D = 2, F2 = 16, norm_rate = 0.25, dropoutType = 'Dropout'):
    """ Keras Implementation of EEGNet
    http://iopscience.iop.org/article/10.1088/1741-2552/aace8c/meta

    Inputs:
        
      nb_classes      : int, number of classes to classify
      Chans, Samples  : number of channels and time points in the EEG data
      dropoutRate     : dropout fraction
      kernLength      : length of temporal convolution in first layer. We found
                        that setting this to be half the sampling rate worked
                        well in practice. For the SMR dataset in particular
                        since the data was high-passed at 4Hz we used a kernel
                        length of 32.     
      F1, F2          : number of temporal filters (F1) and number of pointwise
                        filters (F2) to learn. Default: F1 = 8, F2 = F1 * D. 
      D               : number of spatial filters to learn within each temporal
                        convolution. Default: D = 2
      dropoutType     : Either SpatialDropout2D or Dropout, passed as a string.

    """
    from keras.layers import * # like any other bad programmer would do :) 

#     nb_classes =10
#     Chans = 64
#     Samples = 128
#     dropoutRate = 0.5
#     kernLength = 64
#     F1 = 8
#     D = 2
#     F2 = 16
#     norm_rate = 0.25
#     dropoutType = Dropout

    if dropoutType == 'SpatialDropout2D':
        dropoutType = SpatialDropout2D
    elif dropoutType == 'Dropout':
        dropoutType = Dropout
    else:
        raise ValueError('dropoutType must be one of SpatialDropout2D '
                         'or Dropout, passed as a string.')
    

    model = Sequential()
    model.add(Conv2D(F1, (1,kernLength) , padding = 'same', input_shape = (1,Chans,Samples), use_bias= False))
    model.add(BatchNormalization(axis = 1))
    model.add(DepthwiseConv2D((Chans, 1), use_bias = False, 
                                    depth_multiplier = D,
                                    depthwise_constraint = max_norm(1.)))
 
    model.add(BatchNormalization(axis = 1)
    model.add(Activation('elu'))
    model.add(AveragePooling2D((1, 4)))
    model.add(dropoutType(dropoutRate))


    model.add(SeparableConv2D(F2, (1, 16), use_bias = False, padding = 'same'))
    model.add(BatchNormalization(axis = 1))
    model.add(Activation('elu'))
    model.add(AveragePooling2D((1, 8)))
    model.add(dropoutType(dropoutRate))
    model.add(Flatten(name = 'flatten'))
    model.add(Dense(nb_classes, name = 'dense',kernel_constraint = max_norm(norm_rate)))
        
    model = Model(inputs=input1, outputs=Activation('softmax', name = 'softmax'))
    return Mmodel