Small convolutional neural network with residual connections implemented with Keras

residual_convnet_keras.py

"""Small convnet with residual connections.
inspired by https://gist.github.com/mjdietzx/0cb95922aac14d446a6530f87b3a04ce, 
which builds a full ResNet-50 or ResNeXt-50 model
"""

NUM_CLASSES = 2
from keras.layers import BatchNormalization, Conv2D, LeakyReLU, Input, MaxPool2D, Dense, Flatten, Dropout
from keras.models import Model

def add_common_layers(y):
  y = BatchNormalization()(y)
  y = LeakyReLU()(y)
  return y

def residual_block(y, nb_filters):
  shortcut = y
  y = Conv2D(nb_filters, kernel_size=(1, 1), strides=(1, 1), padding='same')(y)
  y = add_common_layers(y)

  y = Conv2D(nb_filters, kernel_size=(3, 3), strides=(1, 1), padding='same')(y)
  y = add_common_layers(y)

  y = Conv2D(nb_filters, kernel_size=(1, 1), strides=(1, 1), padding='same')(y)
  y = add_common_layers(y)

  y = add([shortcut, y])
  y = LeakyReLU()(y)

  return y

img_input = Input(shape=(100, 100, 1))
x = Conv2D(8, (3, 3))(img_input)
x = add_common_layers(x)

x = MaxPool2D()(x)
x = residual_block(x, 8)

x = MaxPool2D()(x)
x = residual_block(x, 8)

x = MaxPool2D()(x)
x = residual_block(x, 8)

x = Flatten()(x)
x = Dense(16)(x)
x = Dropout(0.5)(x)

prediction = Dense(NUM_CLASSES, activation='softmax')(x)

model = Model(inputs=[img_input], outputs=[prediction])