Thesis-CommonModules/models/cnn_models.py

# model imports
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv3D, MaxPooling3D, AveragePooling3D
from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
from keras.applications.vgg19 import VGG19
from keras import backend as K
import tensorflow as tf

# configuration imports
from . import metrics
from ..config import cnn_config as cfg

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(0.5))

    model.add(Dense(120))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))

    model.add(Dense(80))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))

    model.add(Dense(40))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))

    model.add(Dense(20))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))

    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(0.5))

    model.add(Dense(120))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))

    model.add(Dense(80))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))

    model.add(Dense(40))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))

    model.add(Dense(20))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))

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

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

    return model


# using transfer learning (VGG19)
def generate_model_3D_TL(_input_shape):

    # load pre-trained model
    model = VGG19(weights='imagenet', include_top=False, input_shape=_input_shape)
    # display model layers
    model.summary()

    # do not train convolutional layers
    for layer in model.layers[:5]:
        layer.trainable = False

    predictions_model = Sequential(model)

    #Adding custom Layers
    '''predictions_model.add(Flatten(model.output))

    predictions_model.add(Dense(1024))
    predictions_model.add(Activation('relu'))
    predictions_model.add(BatchNormalization())
    predictions_model.add(Dropout(0.5))

    predictions_model.add(Dense(512))
    predictions_model.add(Activation('relu'))
    predictions_model.add(BatchNormalization())
    predictions_model.add(Dropout(0.5))

    predictions_model.add(Dense(256))
    predictions_model.add(Activation('relu'))
    predictions_model.add(BatchNormalization())
    model.add(Dropout(0.5))

    predictions_model.add(Dense(100))
    predictions_model.add(Activation('relu'))
    predictions_model.add(BatchNormalization())
    predictions_model.add(Dropout(0.5))

    predictions_model.add(Dense(20))
    predictions_model.add(Activation('relu'))
    predictions_model.add(BatchNormalization())
    predictions_model.add(Dropout(0.5))

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

    # adding custom Layers 
    x = model.output
    x = Flatten()(x)
    x = Dense(1024, activation="relu")(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Dense(256, activation="relu")(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Dense(64, activation="relu")(x)
    x = BatchNormalization()(x)
    x = Dropout(0.5)(x)
    x = Dense(16, activation="relu")(x)
    predictions = Dense(1, activation="softmax")(x)

    # creating the final model 
    model_final = Model(input=model.input, output=predictions)

    model_final.summary()

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

    return model_final


def get_model(n_channels, _input_shape, tl=False):
    
    if tl:
        if n_channels == 3:
            return generate_model_3D_TL(_input_shape)
        else:
            print("Can't use transfer learning with only 1 channel")

    if n_channels == 1:
        return generate_model_2D(_input_shape)

    if n_channels == 3:
        return generate_model_3D(_input_shape)