Thesis-CommonModules/models/models.py

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv3D, MaxPooling3D, AveragePooling3D
from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
from keras import backend as K
import tensorflow as tf

from modules.utils import config as cfg
from modules.models import metrics


def generate_model_2D(_input_shape):

    model = Sequential()

    model.add(Conv2D(60, (2, 2), input_shape=_input_shape))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(40, (2, 2)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(20, (2, 2)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())

    model.add(Dense(140))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(120))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(80))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(40))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(20))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    model.compile(loss='binary_crossentropy',
                  optimizer='rmsprop',
                  metrics=['accuracy', metrics.auc])

    return model

def generate_model_3D(_input_shape):

    model = Sequential()

    print(_input_shape)

    model.add(Conv3D(60, (1, 2, 2), input_shape=_input_shape))
    model.add(Activation('relu'))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))

    model.add(Conv3D(40, (1, 2, 2)))
    model.add(Activation('relu'))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))

    model.add(Conv3D(20, (1, 2, 2)))
    model.add(Activation('relu'))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))

    model.add(Flatten())

    model.add(Dense(140))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(120))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(80))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(40))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(20))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    model.compile(loss='binary_crossentropy',
                  optimizer='rmsprop',
                  metrics=['accuracy', metrics.auc])

    return model


def get_model(n_channels, _input_shape):

    if n_channels == 1:
        return generate_model_2D(_input_shape)

    if n_channels == 3:
        return generate_model_3D(_input_shape)