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cnn.py
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cnn.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Sep 16 15:53:33 2017
@author: wawan
"""
from __future__ import print_function
from __future__ import absolute_import
import warnings
from keras.layers import Concatenate, Maximum, Average
from keras.layers import Lambda
from keras.models import Model
from keras import layers
from keras.layers import Dense
from keras.layers import Input
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Conv2D
from keras.layers import SeparableConv2D
from keras.layers import MaxPooling2D
from keras.layers import GlobalAveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras.engine.topology import get_source_inputs
from keras.utils.data_utils import get_file
from keras import backend as K
from keras.callbacks import TensorBoard, ModelCheckpoint, CSVLogger, EarlyStopping, LearningRateScheduler
from keras.models import load_model
TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels.h5'
TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5'
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dropout
from keras.layers import Flatten
from keras.utils import np_utils
from keras import optimizers
import IPython
import h5py
from keras.utils.io_utils import HDF5Matrix
from tensorflow.python import debug as tf_debug
from keras import metrics, losses, regularizers
#sess = K.get_session()
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
#K.set_session(sess)
reg_val = 5e-6
def normalize_pixel(data):
return data/255-.5
def load_data(dataset):
if dataset=='mnist':
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
#IPython.embed()
# reshape to be [samples][pixels][width][height]
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')
#IPython.embed()
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
X_val = X_test
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
y_val = y_test
num_classes = y_test.shape[1]
input_shape=(28, 28, 1)
elif dataset=='CelebA':
# load data CelebA
f=h5py.File('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped.hdf5','r')
X_train=f['features'][0:1000]
X_val=f['features'][1000:1100]
X_test=f['features'][1100:1200]
y_train=f['targets'][0:1000]
y_val=f['targets'][1000:1100]
y_test=f['targets'][1100:1200]
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
num_classes = y_test.shape[1]
input_shape=(218, 178, 3)
elif dataset=='CelebAHDF5':
# load data CelebA
X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_normalized.hdf5', 'features')
y_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_normalized.hdf5', 'targets')
# X_train=X_data[0:162770]
# X_val=X_data[162770:182637]
# X_test=X_data[182637:202599]
# y_train=y_data[0:162770]
# y_val=y_data[162770:182637]
# y_test=y_data[182637:202599]
X_train=X_data
X_val=X_data
X_test=X_data
y_train=y_data
y_val=y_data
y_test=y_data
# normalize inputs from 0-255 to 0-1
# X_train = X_train / 255
# X_test = X_test / 255
num_classes = y_test.shape[1]
input_shape=(218, 178, 3)
return (X_train, y_train), (X_val, y_val), (X_test, y_test), num_classes, input_shape
def hdf5_generator(dataset,set_type):
i=0
# CelebA data size: train = 162770, valid = 19867, test = 19962
if dataset=='CelebA':
if set_type=='train':
X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_train.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_train.hdf5', 'targets')
size = 162770
elif set_type=='valid':
X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_valid.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_valid.hdf5', 'targets')
size = 19867
elif set_type=='test':
X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_test.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_test.hdf5', 'targets')
size = 19962
if set_type=='train_cls5':
X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_train_5cls.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_train_5cls.hdf5', 'targets')
size = 122077
elif set_type=='valid_cls5':
X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_valid_5cls.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_valid_5cls.hdf5', 'targets')
size = 15138
elif set_type=='test_cls5':
X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_test_5cls.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped_test_5cls.hdf5', 'targets')
size = 14724
elif set_type=='all':
X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped.hdf5', 'targets')
size = 202599
elif set_type=='try':
X_data = HDF5Matrix('mytestfile.hdf5', 'features')
y_data = HDF5Matrix('mytestfile.hdf5', 'targets')
size = 20
while 1:
X_single = X_data[i%size].reshape((1, 218, 178, 3))
# y_single = y_data[i%size].reshape((1, 40))
y_single = y_data[i%size].reshape((1, 5))
# X_single = X_data[i:i+1]
# y_single = y_data[i:i+1]
yield(X_single, y_single)
i+=1
def load_data_attr(dataset):
if dataset=='mnist':
num_classes = 10
input_shape=(28, 28, 1)
elif dataset=='CelebA':
num_classes = 40
input_shape=(218, 178, 3)
elif dataset=='CelebA_cls5':
num_classes = 5
input_shape=(218, 178, 3)
return num_classes, input_shape
# load data CelebA
#f=h5py.File('../../data/CelebAHDF5/celeba_aligned_cropped.hdf5','r')
#X_train=f['features'][0:1000]
#X_test=f['features'][1000:1100]
#y_train=f['targets'][0:1000]
#y_test=f['targets'][1000:1100]
#
#
## normalize inputs from 0-255 to 0-1
#X_train = X_train / 255
#X_test = X_test / 255
#num_classes = y_test.shape[1]
def concat_diff(i): # batch discrimination - increase generation diversity.
# return i
bv = Lambda(lambda i:K.mean(K.abs(i[:] - K.mean(i,axis=0)),axis=-1,keepdims=True))(i)
i = Concatenate()([i,bv])
return i
def gan_dis_model_original():
inp = Input(shape=(218,178,3))
i = inp
ndf=24
def conv(i,nop,kw,std=1,usebn=True,bm='same'):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val))(i)
if usebn:
i = BatchNormalization()(i)
i = Activation('relu')(i)
return i
i = conv(i,ndf*1,4,std=2,usebn=False)
i = concat_diff(i)
i = conv(i,ndf*2,4,std=2)
i = concat_diff(i)
i = conv(i,ndf*4,4,std=2)
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2)
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2)
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2)
i = concat_diff(i)
# # 1x1
i = Conv2D(1,(2,2),padding='valid')(i)
i = Activation('linear',name='conv_exit')(i)
i = Activation('sigmoid')(i)
# i = Reshape((1,))(i)
m = Model(input=inp,output=i)
return m
def gan_dis_model():
inp = Input(shape=(218,178,3))
i = inp
ndf=24
def conv(i,nop,kw,std=1,usebn=True,bm='same',name=''):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv'+name)(i)
if usebn:
i = BatchNormalization(name='bn'+name)(i)
i = Activation('relu',name='relu'+name)(i)
return i
i = conv(i,ndf*1,4,std=2,name='0',usebn=False)
i = concat_diff(i)
i = conv(i,ndf*2,4,std=2,name='1')
i = concat_diff(i)
i = conv(i,ndf*4,4,std=2,name='2')
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2,name='3')
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2,name='4')
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2,name='5')
i = concat_diff(i)
i = Flatten()(i)
i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense1')(i)
i = Activation('relu',name='relu_dens1')(i)
# i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense2')(i)
# i = Activation('relu',name='relu_dens2')(i)
i = Dense(num_classes, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='last_dense')(i)
predictions=Activation('sigmoid')(i)
model = Model(inputs=inp, outputs=predictions)
return model
def gan_dis_model_cel5():
inp = Input(shape=(218,178,3))
i = inp
ndf=24
def conv(i,nop,kw,std=1,usebn=True,bm='same',name=''):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv'+name)(i)
if usebn:
i = BatchNormalization(name='bn'+name)(i)
i = Activation('relu',name='relu'+name)(i)
return i
# only 5
i = conv(i,ndf*1,4,std=2,name='0',usebn=False)
i = concat_diff(i)
i = conv(i,ndf*2,4,std=2,name='1')
i = concat_diff(i)
i = conv(i,ndf*4,4,std=2,name='2')
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2,name='3')
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2,name='4')
i = concat_diff(i)
i0 = conv(i,ndf*8,4,std=2,name='5_cel0')
i0 = concat_diff(i0)
i0 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel0')(i0)
i1 = conv(i,ndf*8,4,std=2,name='5_cel1')
i1 = concat_diff(i1)
i1 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel1')(i1)
i2 = conv(i,ndf*8,4,std=2,name='5_cel2')
i2 = concat_diff(i2)
i2 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel2')(i2)
i3 = conv(i,ndf*8,4,std=2,name='5_cel3')
i3 = concat_diff(i3)
i3 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel3')(i3)
i4 = conv(i,ndf*8,4,std=2,name='5_cel4')
i4 = concat_diff(i4)
i4 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel4')(i4)
i = Concatenate()([i0,i1,i2,i3,i4])
i = concat_diff(i)
i = Flatten()(i)
i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense1')(i)
i = Activation('relu',name='relu_dens1')(i)
# i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense2')(i)
# i = Activation('relu',name='relu_dens2')(i)
i = Dense(num_classes, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='last_dense')(i)
predictions=Activation('sigmoid')(i)
model = Model(inputs=inp, outputs=predictions)
return model
def gan_dis_model_cel45():
inp = Input(shape=(218,178,3))
i = inp
ndf=24
def conv(i,nop,kw,std=1,usebn=True,bm='same',name=''):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv'+name)(i)
if usebn:
i = BatchNormalization(name='bn'+name)(i)
i = Activation('relu',name='relu'+name)(i)
return i
# 4 and 5
i = conv(i,ndf*1,4,std=2,name='0',usebn=False)
i = concat_diff(i)
i = conv(i,ndf*2,4,std=2,name='1')
i = concat_diff(i)
i = conv(i,ndf*4,4,std=2,name='2')
i = concat_diff(i)
i = conv(i,ndf*8,4,std=2,name='3')
i = concat_diff(i)
i0 = conv(i,ndf*8,4,std=2,name='4_cel0')
i0 = concat_diff(i0)
i1 = conv(i,ndf*8,4,std=2,name='4_cel1')
i1 = concat_diff(i1)
i2 = conv(i,ndf*8,4,std=2,name='4_cel2')
i2 = concat_diff(i2)
i3 = conv(i,ndf*8,4,std=2,name='4_cel3')
i3 = concat_diff(i3)
i4 = conv(i,ndf*8,4,std=2,name='4_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='5_cel0')
i0 = concat_diff(i0)
i0 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel0')(i0)
i1 = conv(i1,ndf*8,4,std=2,name='5_cel1')
i1 = concat_diff(i1)
i1 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel1')(i1)
i2 = conv(i2,ndf*8,4,std=2,name='5_cel2')
i2 = concat_diff(i2)
i2 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel2')(i2)
i3 = conv(i3,ndf*8,4,std=2,name='5_cel3')
i3 = concat_diff(i3)
i3 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel3')(i3)
i4 = conv(i4,ndf*8,4,std=2,name='5_cel4')
i4 = concat_diff(i4)
i4 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel4')(i4)
i = Concatenate()([i0,i1,i2,i3,i4])
i = concat_diff(i)
i = Flatten()(i)
i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense1')(i)
i = Activation('relu',name='relu_dens1')(i)
# i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense2')(i)
# i = Activation('relu',name='relu_dens2')(i)
i = Dense(num_classes, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='last_dense')(i)
predictions=Activation('sigmoid')(i)
model = Model(inputs=inp, outputs=predictions)
return model
def gan_dis_model_cel345():
inp = Input(shape=(218,178,3))
i = inp
ndf=24
def conv(i,nop,kw,std=1,usebn=True,bm='same',name=''):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv'+name)(i)
if usebn:
i = BatchNormalization(name='bn'+name)(i)
i = Activation('relu',name='relu'+name)(i)
return i
# 3, 4, and 5
i = conv(i,ndf*1,4,std=2,name='0',usebn=False)
i = concat_diff(i)
i = conv(i,ndf*2,4,std=2,name='1')
i = concat_diff(i)
i = conv(i,ndf*4,4,std=2,name='2')
i = concat_diff(i)
i0 = conv(i,ndf*8,4,std=2,name='3_cel0')
i0 = concat_diff(i0)
i1 = conv(i,ndf*8,4,std=2,name='3_cel1')
i1 = concat_diff(i1)
i2 = conv(i,ndf*8,4,std=2,name='3_cel2')
i2 = concat_diff(i2)
i3 = conv(i,ndf*8,4,std=2,name='3_cel3')
i3 = concat_diff(i3)
i4 = conv(i,ndf*8,4,std=2,name='3_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='4_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*8,4,std=2,name='4_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*8,4,std=2,name='4_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*8,4,std=2,name='4_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*8,4,std=2,name='4_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='5_cel0')
i0 = concat_diff(i0)
i0 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel0')(i0)
i1 = conv(i1,ndf*8,4,std=2,name='5_cel1')
i1 = concat_diff(i1)
i1 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel1')(i1)
i2 = conv(i2,ndf*8,4,std=2,name='5_cel2')
i2 = concat_diff(i2)
i2 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel2')(i2)
i3 = conv(i3,ndf*8,4,std=2,name='5_cel3')
i3 = concat_diff(i3)
i3 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel3')(i3)
i4 = conv(i4,ndf*8,4,std=2,name='5_cel4')
i4 = concat_diff(i4)
i4 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel4')(i4)
i = Concatenate()([i0,i1,i2,i3,i4])
i = concat_diff(i)
i = Flatten()(i)
i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense1')(i)
i = Activation('relu',name='relu_dens1')(i)
# i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense2')(i)
# i = Activation('relu',name='relu_dens2')(i)
i = Dense(num_classes, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='last_dense')(i)
predictions=Activation('sigmoid')(i)
model = Model(inputs=inp, outputs=predictions)
return model
def gan_dis_model_cel2345():
inp = Input(shape=(218,178,3))
i = inp
ndf=24
def conv(i,nop,kw,std=1,usebn=True,bm='same',name=''):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv'+name)(i)
if usebn:
i = BatchNormalization(name='bn'+name)(i)
i = Activation('relu',name='relu'+name)(i)
return i
# 2, 3, 4, and 5
i = conv(i,ndf*1,4,std=2,name='0',usebn=False)
i = concat_diff(i)
i = conv(i,ndf*2,4,std=2,name='1')
i = concat_diff(i)
i0 = conv(i,ndf*4,4,std=2,name='2_cel0')
i0 = concat_diff(i0)
i1 = conv(i,ndf*4,4,std=2,name='2_cel1')
i1 = concat_diff(i1)
i2 = conv(i,ndf*4,4,std=2,name='2_cel2')
i2 = concat_diff(i2)
i3 = conv(i,ndf*4,4,std=2,name='2_cel3')
i3 = concat_diff(i3)
i4 = conv(i,ndf*4,4,std=2,name='2_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='3_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*8,4,std=2,name='3_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*8,4,std=2,name='3_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*8,4,std=2,name='3_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*8,4,std=2,name='3_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='4_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*8,4,std=2,name='4_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*8,4,std=2,name='4_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*8,4,std=2,name='4_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*8,4,std=2,name='4_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='5_cel0')
i0 = concat_diff(i0)
i0 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel0')(i0)
i1 = conv(i1,ndf*8,4,std=2,name='5_cel1')
i1 = concat_diff(i1)
i1 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel1')(i1)
i2 = conv(i2,ndf*8,4,std=2,name='5_cel2')
i2 = concat_diff(i2)
i2 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel2')(i2)
i3 = conv(i3,ndf*8,4,std=2,name='5_cel3')
i3 = concat_diff(i3)
i3 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel3')(i3)
i4 = conv(i4,ndf*8,4,std=2,name='5_cel4')
i4 = concat_diff(i4)
i4 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel4')(i4)
i = Concatenate()([i0,i1,i2,i3,i4])
i = concat_diff(i)
i = Flatten()(i)
i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense1')(i)
i = Activation('relu',name='relu_dens1')(i)
# i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense2')(i)
# i = Activation('relu',name='relu_dens2')(i)
i = Dense(num_classes, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='last_dense')(i)
predictions=Activation('sigmoid')(i)
model = Model(inputs=inp, outputs=predictions)
return model
def gan_dis_model_cel12345():
inp = Input(shape=(218,178,3))
i = inp
ndf=24
def conv(i,nop,kw,std=1,usebn=True,bm='same',name=''):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv'+name)(i)
if usebn:
i = BatchNormalization(name='bn'+name)(i)
i = Activation('relu',name='relu'+name)(i)
return i
# 2, 3, 4, and 5
i = conv(i,ndf*1,4,std=2,name='0',usebn=False)
i = concat_diff(i)
i0 = conv(i,ndf*2,4,std=2,name='1_cel0')
i0 = concat_diff(i0)
i1 = conv(i,ndf*2,4,std=2,name='1_cel1')
i1 = concat_diff(i1)
i2 = conv(i,ndf*2,4,std=2,name='1_cel2')
i2 = concat_diff(i2)
i3 = conv(i,ndf*2,4,std=2,name='1_cel3')
i3 = concat_diff(i3)
i4 = conv(i,ndf*2,4,std=2,name='1_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*4,4,std=2,name='2_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*4,4,std=2,name='2_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*4,4,std=2,name='2_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*4,4,std=2,name='2_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*4,4,std=2,name='2_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='3_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*8,4,std=2,name='3_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*8,4,std=2,name='3_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*8,4,std=2,name='3_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*8,4,std=2,name='3_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='4_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*8,4,std=2,name='4_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*8,4,std=2,name='4_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*8,4,std=2,name='4_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*8,4,std=2,name='4_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='5_cel0')
i0 = concat_diff(i0)
i0 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel0')(i0)
i1 = conv(i1,ndf*8,4,std=2,name='5_cel1')
i1 = concat_diff(i1)
i1 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel1')(i1)
i2 = conv(i2,ndf*8,4,std=2,name='5_cel2')
i2 = concat_diff(i2)
i2 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel2')(i2)
i3 = conv(i3,ndf*8,4,std=2,name='5_cel3')
i3 = concat_diff(i3)
i3 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel3')(i3)
i4 = conv(i4,ndf*8,4,std=2,name='5_cel4')
i4 = concat_diff(i4)
i4 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel4')(i4)
i = Concatenate()([i0,i1,i2,i3,i4])
i = concat_diff(i)
i = Flatten()(i)
i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense1')(i)
i = Activation('relu',name='relu_dens1')(i)
# i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense2')(i)
# i = Activation('relu',name='relu_dens2')(i)
i = Dense(num_classes, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='last_dense')(i)
predictions=Activation('sigmoid')(i)
model = Model(inputs=inp, outputs=predictions)
return model
def gan_dis_model_cel012345():
inp = Input(shape=(218,178,3))
i = inp
ndf=24
def conv(i,nop,kw,std=1,usebn=True,bm='same',name=''):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv'+name)(i)
if usebn:
i = BatchNormalization(name='bn'+name)(i)
i = Activation('relu',name='relu'+name)(i)
return i
# 2, 3, 4, and 5
i0 = conv(i,ndf*1,4,std=2,name='0_cel0',usebn=False)
i0 = concat_diff(i0)
i1 = conv(i,ndf*1,4,std=2,name='0_cel1',usebn=False)
i1 = concat_diff(i1)
i2 = conv(i,ndf*1,4,std=2,name='0_cel2',usebn=False)
i2 = concat_diff(i2)
i3 = conv(i,ndf*1,4,std=2,name='0_cel3',usebn=False)
i3 = concat_diff(i3)
i4 = conv(i,ndf*1,4,std=2,name='0_cel4',usebn=False)
i4 = concat_diff(i4)
i0 = conv(i0,ndf*2,4,std=2,name='1_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*2,4,std=2,name='1_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*2,4,std=2,name='1_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*2,4,std=2,name='1_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*2,4,std=2,name='1_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*4,4,std=2,name='2_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*4,4,std=2,name='2_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*4,4,std=2,name='2_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*4,4,std=2,name='2_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*4,4,std=2,name='2_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='3_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*8,4,std=2,name='3_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*8,4,std=2,name='3_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*8,4,std=2,name='3_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*8,4,std=2,name='3_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='4_cel0')
i0 = concat_diff(i0)
i1 = conv(i1,ndf*8,4,std=2,name='4_cel1')
i1 = concat_diff(i1)
i2 = conv(i2,ndf*8,4,std=2,name='4_cel2')
i2 = concat_diff(i2)
i3 = conv(i3,ndf*8,4,std=2,name='4_cel3')
i3 = concat_diff(i3)
i4 = conv(i4,ndf*8,4,std=2,name='4_cel4')
i4 = concat_diff(i4)
i0 = conv(i0,ndf*8,4,std=2,name='5_cel0')
i0 = concat_diff(i0)
i0 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel0')(i0)
i1 = conv(i1,ndf*8,4,std=2,name='5_cel1')
i1 = concat_diff(i1)
i1 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel1')(i1)
i2 = conv(i2,ndf*8,4,std=2,name='5_cel2')
i2 = concat_diff(i2)
i2 = Conv2D(38,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel2')(i2)
i3 = conv(i3,ndf*8,4,std=2,name='5_cel3')
i3 = concat_diff(i3)
i3 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel3')(i3)
i4 = conv(i4,ndf*8,4,std=2,name='5_cel4')
i4 = concat_diff(i4)
i4 = Conv2D(39,kernel_size=(1,1),padding='valid',strides=(1,1), kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='conv5.5_cel4')(i4)
i = Concatenate()([i0,i1,i2,i3,i4])
i = concat_diff(i)
i = Flatten()(i)
i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense1')(i)
i = Activation('relu',name='relu_dens1')(i)
# i = Dense(200, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='dense2')(i)
# i = Activation('relu',name='relu_dens2')(i)
i = Dense(num_classes, kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(reg_val), bias_regularizer=regularizers.l2(reg_val),name='last_dense')(i)
predictions=Activation('sigmoid')(i)
model = Model(inputs=inp, outputs=predictions)
return model
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
#X_data = HDF5Matrix('/mnt/Storage/Projects/data/CelebAHDF5/celeba_aligned_cropped.hdf5', 'features', normalizer=normalize_pixel)
#IPython.embed()
#(X_train, y_train), (X_val, y_val), (X_test, y_test), num_classes, input_shape = load_data('CelebAHDF5')
num_classes, input_shape = load_data_attr('CelebA_cls5')
# build the model
#model = Xception(include_top=True, weights=None, input_shape=input_shape, classes=num_classes)
#fce-gan
model0 = gan_dis_model_original()
model0.load_weights('/home/wawan/git/fce_gan/save/dm_fce_0.hdf5')
model1 = gan_dis_model_original()
model1.load_weights('/home/wawan/git/fce_gan/save/dm_fce_1.hdf5')
model2 = gan_dis_model_original()
model2.load_weights('/home/wawan/git/fce_gan/save/dm_fce_2.hdf5')
model3 = gan_dis_model_original()
model3.load_weights('/home/wawan/git/fce_gan/save/dm_fce_3.hdf5')
model4 = gan_dis_model_original()
model4.load_weights('/home/wawan/git/fce_gan/save/dm_fce_4.hdf5')
model = gan_dis_model_cel45()
# # IPython.embed()
# # only 5
# weights_conv5_cel0 = model0.layers[25].get_weights()
# weights_bn5_cel0 = model0.layers[26].get_weights()
# weights_conv5_cel1 = model1.layers[25].get_weights()
# weights_bn5_cel1 = model1.layers[26].get_weights()
# weights_conv5_cel2 = model2.layers[25].get_weights()
# weights_bn5_cel2 = model2.layers[26].get_weights()
# weights_conv5_cel3 = model3.layers[25].get_weights()
# weights_bn5_cel3 = model3.layers[26].get_weights()
# weights_conv5_cel4 = model4.layers[25].get_weights()
# weights_bn5_cel4 = model4.layers[26].get_weights()
# model.layers[25].set_weights(weights_conv5_cel0)
# model.layers[26].set_weights(weights_conv5_cel1)
# model.layers[27].set_weights(weights_conv5_cel2)
# model.layers[28].set_weights(weights_conv5_cel3)
# model.layers[29].set_weights(weights_conv5_cel4)
# model.layers[30].set_weights(weights_bn5_cel0)
# model.layers[31].set_weights(weights_bn5_cel1)
# model.layers[32].set_weights(weights_bn5_cel2)
# model.layers[33].set_weights(weights_bn5_cel3)
# model.layers[34].set_weights(weights_bn5_cel4)
# model.layers[25].trainable = False
# model.layers[26].trainable = False
# model.layers[27].trainable = False
# model.layers[28].trainable = False
# model.layers[29].trainable = False
# model.layers[30].trainable = False
# model.layers[31].trainable = False
# model.layers[32].trainable = False
# model.layers[33].trainable = False
# model.layers[34].trainable = False
# 4 and 5
# weights_conv4_cel0 = model0.layers[20].get_weights()
# weights_bn4_cel0 = model0.layers[21].get_weights()
# weights_conv4_cel1 = model1.layers[20].get_weights()
# weights_bn4_cel1 = model1.layers[21].get_weights()
# weights_conv4_cel2 = model2.layers[20].get_weights()
# weights_bn4_cel2 = model2.layers[21].get_weights()
# weights_conv4_cel3 = model3.layers[20].get_weights()
# weights_bn4_cel3 = model3.layers[21].get_weights()
# weights_conv4_cel4 = model4.layers[20].get_weights()
# weights_bn4_cel4 = model4.layers[21].get_weights()
# model.layers[20].set_weights(weights_conv4_cel0)
# model.layers[21].set_weights(weights_conv4_cel1)
# model.layers[22].set_weights(weights_conv4_cel2)
# model.layers[23].set_weights(weights_conv4_cel3)
# model.layers[24].set_weights(weights_conv4_cel4)
# model.layers[25].set_weights(weights_bn4_cel0)
# model.layers[26].set_weights(weights_bn4_cel1)
# model.layers[27].set_weights(weights_bn4_cel2)
# model.layers[28].set_weights(weights_bn4_cel3)
# model.layers[29].set_weights(weights_bn4_cel4)
# # model.layers[20].trainable = False
# # model.layers[21].trainable = False
# # model.layers[22].trainable = False
# # model.layers[23].trainable = False
# # model.layers[24].trainable = False
# # model.layers[25].trainable = False
# # model.layers[26].trainable = False
# # model.layers[27].trainable = False
# # model.layers[28].trainable = False
# # model.layers[29].trainable = False
# weights_conv5_cel0 = model0.layers[25].get_weights()
# weights_bn5_cel0 = model0.layers[26].get_weights()
# weights_conv5_cel1 = model1.layers[25].get_weights()
# weights_bn5_cel1 = model1.layers[26].get_weights()
# weights_conv5_cel2 = model2.layers[25].get_weights()
# weights_bn5_cel2 = model2.layers[26].get_weights()
# weights_conv5_cel3 = model3.layers[25].get_weights()
# weights_bn5_cel3 = model3.layers[26].get_weights()
# weights_conv5_cel4 = model4.layers[25].get_weights()
# weights_bn5_cel4 = model4.layers[26].get_weights()
# model.layers[45].set_weights(weights_conv5_cel0)
# model.layers[46].set_weights(weights_conv5_cel1)
# model.layers[47].set_weights(weights_conv5_cel2)
# model.layers[48].set_weights(weights_conv5_cel3)
# model.layers[49].set_weights(weights_conv5_cel4)
# model.layers[50].set_weights(weights_bn5_cel0)
# model.layers[51].set_weights(weights_bn5_cel1)
# model.layers[52].set_weights(weights_bn5_cel2)
# model.layers[53].set_weights(weights_bn5_cel3)
# model.layers[54].set_weights(weights_bn5_cel4)
# # model.layers[45].trainable = False
# # model.layers[46].trainable = False
# # model.layers[47].trainable = False
# # model.layers[48].trainable = False
# # model.layers[49].trainable = False
# # model.layers[50].trainable = False
# # model.layers[51].trainable = False
# # model.layers[52].trainable = False
# # model.layers[53].trainable = False
# # model.layers[54].trainable = False
# 3, 4, and 5
# weights_conv3_cel0 = model0.layers[15].get_weights()
# weights_bn3_cel0 = model0.layers[16].get_weights()
# weights_conv3_cel1 = model1.layers[15].get_weights()
# weights_bn3_cel1 = model1.layers[16].get_weights()
# weights_conv3_cel2 = model2.layers[15].get_weights()
# weights_bn3_cel2 = model2.layers[16].get_weights()
# weights_conv3_cel3 = model3.layers[15].get_weights()
# weights_bn3_cel3 = model3.layers[16].get_weights()
# weights_conv3_cel4 = model4.layers[15].get_weights()
# weights_bn3_cel4 = model4.layers[16].get_weights()
# model.layers[15].set_weights(weights_conv3_cel0)
# model.layers[16].set_weights(weights_conv3_cel1)
# model.layers[17].set_weights(weights_conv3_cel2)
# model.layers[18].set_weights(weights_conv3_cel3)
# model.layers[19].set_weights(weights_conv3_cel4)
# model.layers[20].set_weights(weights_bn3_cel0)
# model.layers[21].set_weights(weights_bn3_cel1)
# model.layers[22].set_weights(weights_bn3_cel2)
# model.layers[23].set_weights(weights_bn3_cel3)
# model.layers[24].set_weights(weights_bn3_cel4)
# # model.layers[15].trainable = False
# # model.layers[16].trainable = False
# # model.layers[17].trainable = False
# # model.layers[18].trainable = False
# # model.layers[19].trainable = False
# # model.layers[20].trainable = False
# # model.layers[21].trainable = False
# # model.layers[22].trainable = False
# # model.layers[23].trainable = False
# # model.layers[24].trainable = False
# weights_conv4_cel0 = model0.layers[20].get_weights()
# weights_bn4_cel0 = model0.layers[21].get_weights()
# weights_conv4_cel1 = model1.layers[20].get_weights()
# weights_bn4_cel1 = model1.layers[21].get_weights()
# weights_conv4_cel2 = model2.layers[20].get_weights()
# weights_bn4_cel2 = model2.layers[21].get_weights()
# weights_conv4_cel3 = model3.layers[20].get_weights()
# weights_bn4_cel3 = model3.layers[21].get_weights()
# weights_conv4_cel4 = model4.layers[20].get_weights()
# weights_bn4_cel4 = model4.layers[21].get_weights()
# model.layers[40].set_weights(weights_conv4_cel0)
# model.layers[41].set_weights(weights_conv4_cel1)
# model.layers[42].set_weights(weights_conv4_cel2)
# model.layers[43].set_weights(weights_conv4_cel3)
# model.layers[44].set_weights(weights_conv4_cel4)
# model.layers[45].set_weights(weights_bn4_cel0)
# model.layers[46].set_weights(weights_bn4_cel1)
# model.layers[47].set_weights(weights_bn4_cel2)
# model.layers[48].set_weights(weights_bn4_cel3)
# model.layers[49].set_weights(weights_bn4_cel4)
# # model.layers[40].trainable = False
# # model.layers[41].trainable = False
# # model.layers[42].trainable = False
# # model.layers[43].trainable = False
# # model.layers[44].trainable = False
# # model.layers[45].trainable = False
# # model.layers[46].trainable = False
# # model.layers[47].trainable = False
# # model.layers[48].trainable = False
# # model.layers[49].trainable = False
# weights_conv5_cel0 = model0.layers[25].get_weights()
# weights_bn5_cel0 = model0.layers[26].get_weights()
# weights_conv5_cel1 = model1.layers[25].get_weights()
# weights_bn5_cel1 = model1.layers[26].get_weights()
# weights_conv5_cel2 = model2.layers[25].get_weights()
# weights_bn5_cel2 = model2.layers[26].get_weights()
# weights_conv5_cel3 = model3.layers[25].get_weights()
# weights_bn5_cel3 = model3.layers[26].get_weights()
# weights_conv5_cel4 = model4.layers[25].get_weights()
# weights_bn5_cel4 = model4.layers[26].get_weights()
# model.layers[65].set_weights(weights_conv5_cel0)
# model.layers[66].set_weights(weights_conv5_cel1)
# model.layers[67].set_weights(weights_conv5_cel2)
# model.layers[68].set_weights(weights_conv5_cel3)
# model.layers[69].set_weights(weights_conv5_cel4)
# model.layers[70].set_weights(weights_bn5_cel0)
# model.layers[71].set_weights(weights_bn5_cel1)
# model.layers[72].set_weights(weights_bn5_cel2)
# model.layers[73].set_weights(weights_bn5_cel3)
# model.layers[74].set_weights(weights_bn5_cel4)
# # model.layers[65].trainable = False
# # model.layers[66].trainable = False
# # model.layers[67].trainable = False
# # model.layers[68].trainable = False
# # model.layers[69].trainable = False
# # model.layers[70].trainable = False
# # model.layers[71].trainable = False
# # model.layers[72].trainable = False
# # model.layers[73].trainable = False
# # model.layers[74].trainable = False
# # 2, 3, 4, and 5
# weights_conv2_cel0 = model0.layers[10].get_weights()
# weights_bn2_cel0 = model0.layers[11].get_weights()
# weights_conv2_cel1 = model1.layers[10].get_weights()
# weights_bn2_cel1 = model1.layers[11].get_weights()
# weights_conv2_cel2 = model2.layers[10].get_weights()
# weights_bn2_cel2 = model2.layers[11].get_weights()
# weights_conv2_cel3 = model3.layers[10].get_weights()
# weights_bn2_cel3 = model3.layers[11].get_weights()
# weights_conv2_cel4 = model4.layers[10].get_weights()
# weights_bn2_cel4 = model4.layers[11].get_weights()
# model.layers[10].set_weights(weights_conv2_cel0)
# model.layers[11].set_weights(weights_conv2_cel1)
# model.layers[12].set_weights(weights_conv2_cel2)
# model.layers[13].set_weights(weights_conv2_cel3)
# model.layers[14].set_weights(weights_conv2_cel4)
# model.layers[15].set_weights(weights_bn2_cel0)
# model.layers[16].set_weights(weights_bn2_cel1)
# model.layers[17].set_weights(weights_bn2_cel2)
# model.layers[18].set_weights(weights_bn2_cel3)
# model.layers[19].set_weights(weights_bn2_cel4)
# # model.layers[10].trainable = False
# # model.layers[11].trainable = False
# # model.layers[12].trainable = False
# # model.layers[13].trainable = False
# # model.layers[14].trainable = False
# # model.layers[15].trainable = False
# # model.layers[16].trainable = False
# # model.layers[17].trainable = False
# # model.layers[18].trainable = False
# # model.layers[19].trainable = False
# weights_conv3_cel0 = model0.layers[15].get_weights()
# weights_bn3_cel0 = model0.layers[16].get_weights()
# weights_conv3_cel1 = model1.layers[15].get_weights()
# weights_bn3_cel1 = model1.layers[16].get_weights()
# weights_conv3_cel2 = model2.layers[15].get_weights()
# weights_bn3_cel2 = model2.layers[16].get_weights()
# weights_conv3_cel3 = model3.layers[15].get_weights()
# weights_bn3_cel3 = model3.layers[16].get_weights()
# weights_conv3_cel4 = model4.layers[15].get_weights()
# weights_bn3_cel4 = model4.layers[16].get_weights()
# model.layers[35].set_weights(weights_conv3_cel0)
# model.layers[36].set_weights(weights_conv3_cel1)
# model.layers[37].set_weights(weights_conv3_cel2)
# model.layers[38].set_weights(weights_conv3_cel3)
# model.layers[39].set_weights(weights_conv3_cel4)
# model.layers[40].set_weights(weights_bn3_cel0)
# model.layers[41].set_weights(weights_bn3_cel1)
# model.layers[42].set_weights(weights_bn3_cel2)
# model.layers[43].set_weights(weights_bn3_cel3)
# model.layers[44].set_weights(weights_bn3_cel4)
# # model.layers[35].trainable = False
# # model.layers[36].trainable = False
# # model.layers[37].trainable = False
# # model.layers[38].trainable = False
# # model.layers[39].trainable = False
# # model.layers[40].trainable = False
# # model.layers[41].trainable = False