vgg162.py

from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model
from keras.layers import Activation, Convolution2D, MaxPooling2D, BatchNormalization, Flatten, Dense, Dropout, Conv2D,MaxPool2D, ZeroPadding2D

num_classes = 42

def model(input, num_classes):
    model = Sequential()
    model.add(Conv2D(input_shape=input, filters=64, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(Conv2D(filters=512, kernel_size=(3, 3), padding="same", activation="relu"))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Flatten())
    model.add(Dense(units=4096, activation="relu"))
    #model.add(Dense(units=4096, activation="relu"))
    model.add(Dense(units=42, activation="relu"))
    #model.add(Dense(units=num_classes, activation="softmax"))
    print(model.summary())
    return model
#"""
if __name__=="__main__":
    input_shape = (128,128,3)
    model = model(input_shape, num_classes)

#"""