Erase of CNN models from common modules

models/cnn_models.py

@@ -1,206 +0,0 @@
-# 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)