inceptionModel.py

from keras.layers import Input, Dense, Conv2D, MaxPooling2D
from keras.models import Model
from keras.layers.merge import concatenate
from keras.regularizers import l2

def inception_model(input, filters_1x1, filters_3x3_reduce, filters_3x3, filters_5x5_reduce, filters_5x5, filters_pool_proj):
    conv_1x1 = Conv2D(filters=filters_1x1, kernel_size=(1, 1), padding='same', activation='relu', kernel_regularizer=l2(0.01))(input)

    conv_3x3_reduce = Conv2D(filters=filters_3x3_reduce, kernel_size=(1, 1), padding='same', activation='relu', kernel_regularizer=l2(0.01))(input)

    conv_3x3 = Conv2D(filters=filters_3x3, kernel_size=(3, 3), padding='same', activation='relu', kernel_regularizer=l2(0.01))(conv_3x3_reduce)

    conv_5x5_reduce  = Conv2D(filters=filters_5x5_reduce, kernel_size=(1, 1), padding='same', activation='relu', kernel_regularizer=l2(0.01))(input)

    conv_5x5 = Conv2D(filters=filters_5x5, kernel_size=(5, 5), padding='same', activation='relu', kernel_regularizer=l2(0.01))(conv_5x5_reduce)

    maxpool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(input)

    maxpool_proj = Conv2D(filters=filters_pool_proj, kernel_size=(1, 1), strides=(1, 1), padding='same', activation='relu', kernel_regularizer=l2(0.01))(maxpool)

    inception_output = concatenate([conv_1x1, conv_3x3, conv_5x5, maxpool_proj], axis=3)  # use tf as backend

    return inception_output