googLeNet/googLeNet.py at master · dingchenwei/googLeNet · GitHub

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from scipy.misc import imread, imresize
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, Dropout
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD
import sys
from inceptionModel import inception_model

def define_model(weight_path = None):
    input = Input(shape=(224, 224, 3))

    conv1_7x7_s2 = Conv2D(filters=64, kernel_size=(7, 7), strides=(2, 2), padding='same', activation='relu', kernel_regularizer=l2(0.01))(input)

    maxpool1_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(conv1_7x7_s2)

    conv2_3x3_reduce = Conv2D(filters=64, kernel_size=(1, 1), padding='same', activation='relu', kernel_regularizer=l2(0.01))(maxpool1_3x3_s2)

    conv2_3x3 = Conv2D(filters=192, kernel_size=(3, 3), padding='same', activation='relu', kernel_regularizer=l2(0.01))(conv2_3x3_reduce)

    maxpool2_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(conv2_3x3)

    inception_3a = inception_model(input=maxpool2_3x3_s2, filters_1x1=64, filters_3x3_reduce=96, filters_3x3=128, filters_5x5_reduce=16, filters_5x5=32, filters_pool_proj=32)

    inception_3b = inception_model(input=inception_3a, filters_1x1=128, filters_3x3_reduce=128, filters_3x3=192, filters_5x5_reduce=32, filters_5x5=96, filters_pool_proj=64)

    maxpool3_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(inception_3b)

    inception_4a = inception_model(input=maxpool3_3x3_s2, filters_1x1=192, filters_3x3_reduce=96, filters_3x3=208, filters_5x5_reduce=16, filters_5x5=48, filters_pool_proj=64)

    inception_4b = inception_model(input=inception_4a, filters_1x1=160, filters_3x3_reduce=112, filters_3x3=224, filters_5x5_reduce=24, filters_5x5=64, filters_pool_proj=64)

    inception_4c = inception_model(input=inception_4b, filters_1x1=128, filters_3x3_reduce=128, filters_3x3=256, filters_5x5_reduce=24, filters_5x5=64, filters_pool_proj=64)

    inception_4d = inception_model(input=inception_4c, filters_1x1=112, filters_3x3_reduce=144, filters_3x3=288, filters_5x5_reduce=32, filters_5x5=64, filters_pool_proj=64)

    inception_4e = inception_model(input=inception_4d, filters_1x1=256, filters_3x3_reduce=160, filters_3x3=320, filters_5x5_reduce=32, filters_5x5=128, filters_pool_proj=128)

    maxpool4_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(inception_4e)

    inception_5a = inception_model(input=maxpool4_3x3_s2, filters_1x1=256, filters_3x3_reduce=160, filters_3x3=320, filters_5x5_reduce=32, filters_5x5=128, filters_pool_proj=128)

    inception_5b = inception_model(input=inception_5a, filters_1x1=384, filters_3x3_reduce=192, filters_3x3=384, filters_5x5_reduce=48, filters_5x5=128, filters_pool_proj=128)

    averagepool1_7x7_s1 = AveragePooling2D(pool_size=(7, 7), strides=(7, 7), padding='same')(inception_5b)

    drop1 = Dropout(rate=0.4)(averagepool1_7x7_s1)

    linear = Dense(units=1000, activation='softmax', kernel_regularizer=l2(0.01))(keras.layers.core.Flatten(drop1))
    last = linear


    model = Model(inputs=input, outputs=last)
    model.summary()


if __name__ == '__main__':
    model = define_model()