TYY_model.py

import logging
import sys
import numpy as np
from keras.models import Model
from keras.layers import Input, Activation, add, Dense, Flatten, Dropout, Multiply, Embedding, Lambda, Add, Concatenate, Activation
from keras.layers.convolutional import Conv2D, AveragePooling2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras import backend as K
from keras.optimizers import SGD,Adam
from keras.applications.mobilenet import MobileNet
from densenet import *
from keras.utils import plot_model

sys.setrecursionlimit(2 ** 20)
np.random.seed(2 ** 10)


class TYY_MobileNet_reg:
    def __init__(self, image_size, alpha):
        

        if K.image_dim_ordering() == "th":
            logging.debug("image_dim_ordering = 'th'")
            self._channel_axis = 1
            self._input_shape = (3, image_size, image_size)
        else:
            logging.debug("image_dim_ordering = 'tf'")
            self._channel_axis = -1
            self._input_shape = (image_size, image_size, 3)
        self.alpha = alpha

#    def create_model(self):
    def __call__(self):
        logging.debug("Creating model...")

        inputs = Input(shape=self._input_shape)
        model_mobilenet = MobileNet(input_shape=self._input_shape, alpha=self.alpha, depth_multiplier=1, dropout=1e-3, include_top=False, weights=None, input_tensor=None, pooling=None)
        x = model_mobilenet(inputs)
        #flatten = Flatten()(x)
        
        feat_a = Conv2D(20,(1,1),activation='relu')(x)
        feat_a = Flatten()(feat_a)
        feat_a = Dropout(0.2)(feat_a)
        feat_a = Dense(32,activation='relu',name='feat_a')(feat_a)

        pred_a = Dense(1,name='pred_a')(feat_a)
        model = Model(inputs=inputs, outputs=[pred_a])


        return model


class TYY_DenseNet_reg:
    def __init__(self, image_size, depth):
        
        if K.image_dim_ordering() == "th":
            logging.debug("image_dim_ordering = 'th'")
            self._channel_axis = 1
            self._input_shape = (3, image_size, image_size)
        else:
            logging.debug("image_dim_ordering = 'tf'")
            self._channel_axis = -1
            self._input_shape = (image_size, image_size, 3)
        self.depth = depth

#    def create_model(self):
    def __call__(self):
        logging.debug("Creating model...")

        inputs = Input(shape=self._input_shape)
        model_densenet = DenseNet(input_shape=self._input_shape, depth=self.depth, include_top=False, weights=None, input_tensor=None)
        flatten = model_densenet(inputs)
        
        feat_a = Dense(128,activation='relu')(flatten)
        feat_a = Dropout(0.2)(feat_a)
        feat_a = Dense(32,activation='relu',name='feat_a')(feat_a)

        pred_a = Dense(1,name='pred_a')(feat_a)
        model = Model(inputs=inputs, outputs=[pred_a])
        
        return model