gohrnet.py

from keras.models import Model
from keras.layers import Dense, Conv1D, Input, Reshape, Permute, Add, Flatten, BatchNormalization, Activation
from keras import backend as K
from keras.regularizers import l2


#make residual tower of convolutional blocks
def make_model(input_size, num_filters=32, num_outputs=1, d1=64, d2=64, word_size=16, ks=3,depth=1, reg_param=10**-5, final_activation='sigmoid'):
  #Input and preprocessing layers
  inp = Input(shape=(input_size,));
  rs = Reshape((input_size//word_size, word_size))(inp);
  perm = Permute((2,1))(rs);
  #add a single residual layer that will expand the data to num_filters channels
  #this is a bit-sliced layer
  conv0 = Conv1D(num_filters, kernel_size=1, padding='same', kernel_regularizer=l2(reg_param))(perm);
  conv0 = BatchNormalization()(conv0);
  conv0 = Activation('relu')(conv0);
  #add residual blocks
  shortcut = conv0;
  for i in range(depth):
    conv1 = Conv1D(num_filters, kernel_size=ks, padding='same', kernel_regularizer=l2(reg_param))(shortcut);
    conv1 = BatchNormalization()(conv1);
    conv1 = Activation('relu')(conv1);
    conv2 = Conv1D(num_filters, kernel_size=ks, padding='same',kernel_regularizer=l2(reg_param))(conv1);
    conv2 = BatchNormalization()(conv2);
    conv2 = Activation('relu')(conv2);
    shortcut = Add()([shortcut, conv2]);
  #add prediction head
  flat1 = Flatten()(shortcut);
  dense1 = Dense(d1,kernel_regularizer=l2(reg_param))(flat1);
  dense1 = BatchNormalization()(dense1);
  dense1 = Activation('relu')(dense1);
  dense2 = Dense(d2, kernel_regularizer=l2(reg_param))(dense1);
  dense2 = BatchNormalization()(dense2);
  dense2 = Activation('relu')(dense2);
  out = Dense(num_outputs, activation=final_activation, kernel_regularizer=l2(reg_param))(dense2);
  model = Model(inputs=inp, outputs=out);
  return(model);