Inception/GoogLeNet for https://predictiveprogrammer.com/famous-convolutional-neural-network-architectures-1

inception.py

from keras import layers
from keras.models import Model

from functools import partial

conv1x1 = partial(layers.Conv2D, kernel_size=1, activation='relu')
conv3x3 = partial(layers.Conv2D, kernel_size=3, padding='same', activation='relu')
conv5x5 = partial(layers.Conv2D, kernel_size=5, padding='same', activation='relu')

def inception_module(in_tensor, c1, c3_1, c3, c5_1, c5, pp):
    conv1 = conv1x1(c1)(in_tensor)

    conv3_1 = conv1x1(c3_1)(in_tensor)
    conv3 = conv3x3(c3)(conv3_1)

    conv5_1 = conv1x1(c5_1)(in_tensor)
    conv5 = conv5x5(c5)(conv5_1)

    pool_conv = conv1x1(pp)(in_tensor)
    pool = layers.MaxPool2D(3, strides=1, padding='same')(pool_conv)

    merged = layers.Concatenate(axis=-1)([conv1, conv3, conv5, pool])
    return merged

def aux_clf(in_tensor):
    avg_pool = layers.AvgPool2D(5, 3)(in_tensor)
    conv = conv1x1(128)(avg_pool)
    flattened = layers.Flatten()(conv)
    dense = layers.Dense(1024, activation='relu')(flattened)
    dropout = layers.Dropout(0.7)(dense)
    out = layers.Dense(1000, activation='softmax')(dropout)
    return out

def inception_net(in_shape=(224,224,3), n_classes=1000, opt='sgd'):
    in_layer = layers.Input(in_shape)

    conv1 = layers.Conv2D(64, 7, strides=2, activation='relu', padding='same')(in_layer)
    pad1 = layers.ZeroPadding2D()(conv1)
    pool1 = layers.MaxPool2D(3, 2)(pad1)
    conv2_1 = conv1x1(64)(pool1)
    conv2_2 = conv3x3(192)(conv2_1)
    pad2 = layers.ZeroPadding2D()(conv2_2)
    pool2 = layers.MaxPool2D(3, 2)(pad2)

    inception3a = inception_module(pool2, 64, 96, 128, 16, 32, 32)
    inception3b = inception_module(inception3a, 128, 128, 192, 32, 96, 64)
    pad3 = layers.ZeroPadding2D()(inception3b)
    pool3 = layers.MaxPool2D(3, 2)(pad3)

    inception4a = inception_module(pool3, 192, 96, 208, 16, 48, 64)
    inception4b = inception_module(inception4a, 160, 112, 224, 24, 64, 64)
    inception4c = inception_module(inception4b, 128, 128, 256, 24, 64, 64)
    inception4d = inception_module(inception4c, 112, 144, 288, 32, 48, 64)
    inception4e = inception_module(inception4d, 256, 160, 320, 32, 128, 128)
    pad4 = layers.ZeroPadding2D()(inception4e)
    pool4 = layers.MaxPool2D(3, 2)(pad4)

    aux_clf1 = aux_clf(inception4a)
    aux_clf2 = aux_clf(inception4d)

    inception5a = inception_module(pool4, 256, 160, 320, 32, 128, 128)
    inception5b = inception_module(inception5a, 384, 192, 384, 48, 128, 128)
    pad5 = layers.ZeroPadding2D()(inception5b)
    pool5 = layers.MaxPool2D(3, 2)(pad5)

    avg_pool = layers.GlobalAvgPool2D()(pool5)
    dropout = layers.Dropout(0.4)(avg_pool)
    preds = layers.Dense(1000, activation='softmax')(dropout)

    model = Model(in_layer, [preds, aux_clf1, aux_clf2])
    model.compile(loss="categorical_crossentropy", optimizer=opt,
	              metrics=["accuracy"])
    return model

if __name__ == '__main__':
    model = inception_net()
    print(model.summary())