iunet.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

def model(input):
    model = Sequential()

    model.add(Convolution2D(32, (3,3), padding='same', use_bias=False, input_shape=(input)))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())

    model.add(Convolution2D(32, (3,3), padding='same', use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2)))

    model.add(Convolution2D(64, (3,3), padding='same', use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())

    model.add(Convolution2D(64, (3,3), padding='same', use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2)))

    model.add(Convolution2D(96, (3,3), padding='same', use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())

    model.add(Convolution2D(96, (3,3), padding='same', use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2)))

    model.add(Convolution2D(128, (3,3),padding='same', use_bias=False))
    # model.add(BatchNormalization())
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())

    model.add(Convolution2D(128, (3,3),padding='same', use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2)))

    model.add(Convolution2D(256, (3,3),padding='same',use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())

    model.add(Convolution2D(256, (3,3),padding='same',use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2)))

    model.add(Convolution2D(512, (3,3), padding='same', use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())

    model.add(Convolution2D(512, (3,3), padding='same', use_bias=False))
    model.add(LeakyReLU(alpha = 0.1))
    model.add(BatchNormalization())


    model.add(Flatten())
    model.add(Dense(512,activation='relu'))
    model.add(Dropout(0.1))
    model.add(Dense(42))
    print(model.summary())
    return model
#"""
if __name__=="__main__":
    input_shape = (128,128,3)
    model = model(input_shape)

#"""