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TYY_model.py
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TYY_model.py
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# This code is imported from the following project: https://github.com/asmith26/wide_resnets_keras
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
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 *
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')(feat_a)
pred_a = Dense(1,name='pred_a')(feat_a)
model = Model(inputs=inputs, outputs=[pred_a])
return model
class TYY_MobileNet_dex:
def __init__(self, image_size, alpha, num_neu):
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
self.num_neu = num_neu
# 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')(feat_a)
pred_a_softmax = Dense(self.num_neu,activation='softmax', name='pred_a_softmax')(feat_a)
def mult_fixed_range(x, num_neu):
a = x[:,0]*0
for i in range(0,num_neu):
a = a+i*x[:,i]*101/num_neu
return a
pred_a = Lambda(mult_fixed_range, arguments={'num_neu': self.num_neu}, output_shape=(1,), name='pred_a')(pred_a_softmax)
model = Model(inputs=inputs, outputs=[pred_a_softmax,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
class TYY_DenseNet_dex:
def __init__(self, image_size, depth, num_neu):
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
self.num_neu = num_neu
# 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_softmax = Dense(self.num_neu,activation='softmax', name='pred_a_softmax')(feat_a)
def mult_fixed_range(x, num_neu):
a = x[:,0]*0
for i in range(0,num_neu):
a = a+i*x[:,i]*101/num_neu
return a
pred_a = Lambda(mult_fixed_range, arguments={'num_neu': self.num_neu}, output_shape=(1,), name='pred_a')(pred_a_softmax)
model = Model(inputs=inputs, outputs=[pred_a_softmax,pred_a])
return model