VGG Net for https://predictiveprogrammer.com/famous-convolutional-neural-network-architectures-1

vgg.py

from keras import layers
from keras.models import Model, Sequential

from functools import partial

conv3 = partial(layers.Conv2D,
                kernel_size=3,
                strides=1,
                padding='same',
                activation='relu')

def block(in_tensor, filters, n_convs):
    conv_block = in_tensor
    for _ in range(n_convs):
        conv_block = conv3(filters=filters)(conv_block)
    return conv_block

def _vgg(in_shape=(227,227,3),
         n_classes=1000,
         opt='sgd',
         n_stages_per_blocks=[2, 2, 3, 3, 3]):
    in_layer = layers.Input(in_shape)

    block1 = block(in_layer, 64, n_stages_per_blocks[0])
    pool1 = layers.MaxPool2D()(block1)
    block2 = block(pool1, 128, n_stages_per_blocks[1])
    pool2 = layers.MaxPool2D()(block2)
    block3 = block(pool2, 256, n_stages_per_blocks[2])
    pool3 = layers.MaxPool2D()(block3)
    block4 = block(pool3, 512, n_stages_per_blocks[3])
    pool4 = layers.MaxPool2D()(block4)
    block5 = block(pool4, 512, n_stages_per_blocks[4])
    pool5 = layers.MaxPool2D()(block5)
    flattened = layers.GlobalAvgPool2D()(pool5)

    dense1 = layers.Dense(4096, activation='relu')(flattened)
    dense2 = layers.Dense(4096, activation='relu')(dense1)
    preds = layers.Dense(1000, activation='softmax')(dense2)

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

def vgg16(in_shape=(227,227,3), n_classes=1000, opt='sgd'):
    return _vgg(in_shape, n_classes, opt)

def vgg19(in_shape=(227,227,3), n_classes=1000, opt='sgd'):
    return _vgg(in_shape, n_classes, opt, [2, 2, 4, 4, 4])

if __name__ == '__main__':
    model = vgg19()
    print(model.summary())