from keras.layers.merge import concatenate
# x = merge([a, b], mode='concat', concat_axis=-1)
x = concatenate([a, b], axis=-1)

from keras.engine import merge
m = merge([init, x], mode='sum')
Equivalent Keras 2.0.2 code:

from keras.layers import add
m = add([init, x])

 # x = Convolution2D(32 // nb_filters_reduction_factor, 3, 3, subsample=(1, 1), activation='relu',
 #     init='he_normal', border_mode='valid', dim_ordering='tf')(x)
 x = Conv2D(32 // nb_filters_reduction_factor, (3, 3), activation="relu", strides=(1, 1), padding="valid",
    data_format="channels_last",
    kernel_initializer="he_normal")(x)

# b = MaxPooling2D((3, 3), strides=(1, 1), border_mode='valid', dim_ordering='tf')(x)
b = MaxPooling2D((3, 3), strides=(1, 1), padding='valid', data_format="channels_last")(x)

from keras.layers.merge import concatenate
# x = merge([a, b], mode='concat', concat_axis=-1)
x = concatenate([a, b], axis=-1)

from keras.engine import merge
m = merge([init, x], mode='sum')
Equivalent Keras 2.0.2 code:

from keras.layers import add
m = add([init, x])

 # x = Convolution2D(32 // nb_filters_reduction_factor, 3, 3, subsample=(1, 1), activation='relu',
 #     init='he_normal', border_mode='valid', dim_ordering='tf')(x)
 x = Conv2D(32 // nb_filters_reduction_factor, (3, 3), activation="relu", strides=(1, 1), padding="valid",
    data_format="channels_last",
    kernel_initializer="he_normal")(x)