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cnn_generate_confmat.py
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cnn_generate_confmat.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.engine.topology import Layer
from keras import activations
from keras.callbacks import TensorBoard, ModelCheckpoint, CSVLogger
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 = 1e-5
model_homepath = '/home/wawan/git/'
data_homepath = '/mnt/Storage/'
filepath = 'cnn/unfrozen/cel012345_cnn_weights_best.hdf5'
modelname = model_homepath+'fce_gan/save/stat/unfrozen_cel012345'
def normalize_pixel(data):
return data/255-.5
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(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_train.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_train.hdf5', 'targets')
size = 162770
elif set_type=='valid':
X_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_valid.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_valid.hdf5', 'targets')
size = 19867
elif set_type=='test':
X_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_test.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_test.hdf5', 'targets')
size = 19962
if set_type=='train_cls5':
X_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_train_5cls.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_train_5cls.hdf5', 'targets')
size = 122077
elif set_type=='valid_cls5':
X_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_valid_5cls.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_valid_5cls.hdf5', 'targets')
size = 15138
elif set_type=='test_cls5':
X_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_test_5cls.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_test_5cls.hdf5', 'targets')
size = 14724
elif set_type=='all':
X_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped.hdf5', 'features', normalizer=normalize_pixel)
y_data = HDF5Matrix(data_homepath+'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
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
class ClassDependentCost(Layer):
def __init__(self, output_dim, trainable, **kwargs):
self.output_dim = output_dim
super(ClassDependentCost, self).__init__(**kwargs)
self.sigmoid = activations.get('sigmoid')
self.trainable = trainable
def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1],),
initializer='he_uniform',
regularizer=regularizers.l2(reg_val),
trainable=self.trainable)
super(ClassDependentCost, self).build(input_shape) # Be sure to call this somewhere!
def call(self, x):
return x * self.sigmoid(self.kernel)
def compute_output_shape(self, input_shape):
return (input_shape[0], self.output_dim)
def cosen_cnn_model(type):
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
if type=='cost':
cost_trainable = True
elif type=='normal':
cost_trainable = False
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)
i=ClassDependentCost(5,trainable=cost_trainable)(i)
predictions=Activation('sigmoid')(i)
model = Model(inputs=inp, outputs=predictions)
if type=='cost':
for i in range(34):
model.layers[i].trainable = False
return model
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'):
i = Conv2D(nop,kernel_size=(kw,kw),padding=bm,strides=(std,std), kernel_initializer='random_uniform')(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)
i = Flatten()(i)
i = Dense(200)(i)
i = Activation('relu',name='relu_dens1')(i)
# i = Dense(200)(i)
# i = Activation('relu',name='relu_dens2')(i)
i = Dense(num_classes)(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)
num_classes, input_shape = load_data_attr('CelebA_cls5')
# build the model
# model = gan_dis_model()
model = gan_dis_model_cel012345()
# model = cosen_cnn_model('normal')
model.load_weights(model_homepath+'fce_gan/save/'+filepath)
model.summary()
prediction=model.predict_generator(hdf5_generator('CelebA','test_cls5'), 14724)
prediction=numpy.transpose(prediction)
prediction[prediction<0.5]=0
prediction[prediction>=0.5]=1
prediction.astype(int)
prediction_flip=numpy.absolute(1-prediction)
y_data = HDF5Matrix(data_homepath+'Projects/data/CelebAHDF5/celeba_aligned_cropped_test_5cls.hdf5', 'targets')
y_data=y_data[:].astype(int)
y_data_flip=numpy.absolute(1-y_data)
confmat=numpy.matmul(prediction, y_data)
confmat_flip=numpy.matmul(prediction_flip, y_data_flip)
ap=numpy.count_nonzero(y_data[:,0])+numpy.count_nonzero(y_data[:,1])+numpy.count_nonzero(y_data[:,2])+numpy.count_nonzero(y_data[:,3])+numpy.count_nonzero(y_data[:,4])
an=numpy.count_nonzero(y_data_flip[:,0])+numpy.count_nonzero(y_data_flip[:,1])+numpy.count_nonzero(y_data_flip[:,2])+numpy.count_nonzero(y_data_flip[:,3])+numpy.count_nonzero(y_data_flip[:,4])
tp=confmat[0,0]+confmat[1,1]+confmat[2,2]+confmat[3,3]+confmat[4,4]
tn=confmat_flip[0,0]+confmat_flip[1,1]+confmat_flip[2,2]+confmat_flip[3,3]+confmat_flip[4,4]
fp=ap-tp
fn=an-tn
precision=tp/ap
recall=tp/(tp+fn)
acc=(tp+tn)/(ap+an)
ap0=numpy.count_nonzero(y_data[:,0])
an0=numpy.count_nonzero(y_data_flip[:,0])
tp0=confmat[0,0]
tn0=confmat_flip[0,0]
fp0=ap0-tp0
fn0=an0-tn0
precision0=tp0/ap0
recall0=tp0/(tp0+fn0)
acc0=(tp0+tn0)/(ap0+an0)
ap1=numpy.count_nonzero(y_data[:,1])
an1=numpy.count_nonzero(y_data_flip[:,1])
tp1=confmat[1,1]
tn1=confmat_flip[1,1]
fp1=ap1-tp1
fn1=an1-tn1
precision1=tp1/ap1
recall1=tp1/(tp1+fn1)
acc1=(tp1+tn1)/(ap1+an1)
ap2=numpy.count_nonzero(y_data[:,2])
an2=numpy.count_nonzero(y_data_flip[:,2])
tp2=confmat[2,2]
tn2=confmat_flip[2,2]
fp2=ap2-tp2
fn2=an2-tn2
precision2=tp2/ap2
recall2=tp2/(tp2+fn2)
acc2=(tp2+tn2)/(ap2+an2)
ap3=numpy.count_nonzero(y_data[:,3])
an3=numpy.count_nonzero(y_data_flip[:,3])
tp3=confmat[3,3]
tn3=confmat_flip[3,3]
fp3=ap3-tp3
fn3=an3-tn3
precision3=tp3/ap3
recall3=tp3/(tp3+fn3)
acc3=(tp3+tn3)/(ap3+an3)
ap4=numpy.count_nonzero(y_data[:,4])
an4=numpy.count_nonzero(y_data_flip[:,4])
tp4=confmat[4,4]
tn4=confmat_flip[4,4]
fp4=ap4-tp4
fn4=an4-tn4
precision4=tp4/ap4
recall4=tp4/(tp4+fn4)
acc4=(tp4+tn4)/(ap4+an4)
save = numpy.array([
[ap, ap0, ap1, ap2, ap3, ap4],
[an, an0, an1, an2, an3, an4],
[tp, tp0, tp1, tp2, tp3, tp4],
[tn, tn0, tn1, tn2, tn3, tn4],
[fp, fp0, fp1, fp2, fp3, fp4],
[fn, fn0, fn1, fn2, fn3, fn4],
[precision, precision0, precision1, precision2, precision3, precision4],
[recall, recall0, recall1, recall2, recall3, recall4],
[acc, acc0, acc1, acc2, acc3, acc4]
])
numpy.savetxt(modelname+'_cnn_stat.csv', save, delimiter=',')
# IPython.embed()