asdas

import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten

def createVGGmodel(CLASSES):
    """
    ===DESCRIPTION===
    ----------------------------------------------------
    |   Function creates and return VGGNet-16 model    |
    |   proposed and created by :                      |
    |   Karen Simonyan∗& Andrew Zisserman              |
    |   https://arxiv.org/pdf/1409.1556.pdf            |
    ----------------------------------------------------
    ===INPUT===
    CLASSES       -     amount of classes that we want to calssify

    ===OUTPUT===
    model         -     VGGNet-16 model

    """
    IMAGE_INPUT = (224,224,3)

    VGGNet16 = Sequential()
    VGGNet16.add(Conv2D(64,(3,3), activation = "relu", padding = "same",name = "block1_conv1", input_shape = IMAGE_INPUT))
    VGGNet16.add(Conv2D(64,(3,3), activation = "relu", padding = "same", name = "block1_conv2"))
    VGGNet16.add(MaxPooling2D((2,2), strides = (2,2), name = "block1_pooling"))

    VGGNet16.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1'))
    VGGNet16.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2'))
    VGGNet16.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pooling'))

    VGGNet16.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1'))
    VGGNet16.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2'))
    VGGNet16.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3'))
    VGGNet16.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pooling'))

    VGGNet16.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1'))
    VGGNet16.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2'))
    VGGNet16.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3'))
    VGGNet16.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pooling'))

    VGGNet16.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1'))
    VGGNet16.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2'))
    VGGNet16.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3'))
    VGGNet16.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pooling'))

    VGGNet16.add(Flatten())
    VGGNet16.add(Dense(2048, activation='relu', name='fc1')) #Decreased by 50%, because i dont want to predict 1000 classes
    VGGNet16.add(Dense(1024, activation='relu', name='fc2'))
    VGGNet16.add(Dense(CLASSES, activation='softmax', name='predictions'))
    VGGNet16.compile(optimizer = "rmsprop", loss = 'categorical_crossentropy', metrics=['acc'])

    return VGGNet16