Thesis-NoiseDetection-CNN/cnn_models.py

# main imports
import sys

# 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 and modules imports
sys.path.insert(0, '') # trick to enable import of main folder module

import custom_config as cfg
#from models import metrics


def generate_model_2D(_input_shape):

    model = Sequential()

    model.add(Conv2D(140, (3, 3), input_shape=_input_shape))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(70, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(20, (3, 3)))
    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('sigmoid'))
    # 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(2))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  #metrics=['accuracy', metrics.auc])
                  metrics=['accuracy'])

    return model


def generate_model_3D(_input_shape):

    model = Sequential()

    print(_input_shape)

    model.add(Conv3D(200, (1, 3, 3), input_shape=_input_shape))
    model.add(Activation('relu'))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))

    model.add(Conv3D(100, (1, 3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))

    model.add(Conv3D(40, (1, 3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling3D(pool_size=(1, 2, 2)))

    model.add(Flatten())

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

    model.add(Dense(128))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    model.add(Activation('relu'))

    model.add(Dense(64))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    model.add(Activation('relu'))

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

    model.add(Dense(2))
    model.add(Activation('sigmoid'))

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

    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)

    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])
                  metrics=['accuracy'])

    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 >= 2:
        return generate_model_3D(_input_shape)