from keras.models import Model
from keras.layers import Input, Dense, Dropout, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, concatenate
from keras.layers.convolutional import Conv2D, MaxPooling2D, AveragePooling2D
import numpy as np

seed = 7
np.random.seed(seed)


def Conv2d_BN(x, nb_filter, kernel_size, padding='same', strides=(1, 1), name=None):
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None

    x = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x)
    x = BatchNormalization(axis=3, name=bn_name)(x)
    return x


def Inception(x, nb_filter):
    branch1x1 = Conv2d_BN(x, nb_filter, (1, 1), padding='same', strides=(1, 1), name=None)

    branch3x3 = Conv2d_BN(x, nb_filter, (1, 1), padding='same', strides=(1, 1), name=None)
    branch3x3 = Conv2d_BN(branch3x3, nb_filter, (3, 3), padding='same', strides=(1, 1), name=None)

    branch5x5 = Conv2d_BN(x, nb_filter, (1, 1), padding='same', strides=(1, 1), name=None)
    branch5x5 = Conv2d_BN(branch5x5, nb_filter, (1, 1), padding='same', strides=(1, 1), name=None)

    branchpool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
    branchpool = Conv2d_BN(branchpool, nb_filter, (1, 1), padding='same', strides=(1, 1), name=None)

    x = concatenate([branch1x1, branch3x3, branch5x5, branchpool], axis=3)

    return x


inpt = Input(shape=(224, 224, 3))
# padding = 'same'，填充為(步長-1）/2,還可以用ZeroPadding2D((3,3))
x = Conv2d_BN(inpt, 64, (7, 7), strides=(2, 2), padding='same')
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
x = Conv2d_BN(x, 192, (3, 3), strides=(1, 1), padding='same')
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
x = Inception(x, 64)  # 256
x = Inception(x, 120)  # 480
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
x = Inception(x, 128)  # 512
x = Inception(x, 128)
x = Inception(x, 128)
x = Inception(x, 132)  # 528
x = Inception(x, 208)  # 832
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
x = Inception(x, 208)
x = Inception(x, 256)  # 1024
x = AveragePooling2D(pool_size=(7, 7), strides=(7, 7), padding='same')(x)
x = Dropout(0.4)(x)
x = Dense(1000, activation='relu')(x)
x = Dense(1000, activation='softmax')(x)
model = Model(inpt, x, name='inception')
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.summary()