人脸识别算法-人脸老化预测（含数据集）

from __future__ import division import os import time from glob import glob import tensorflow as tf import numpy as np from scipy.io import savemat from ops import * class FaceAging(object): def __init__(self, session, # TensorFlow session size_image=128, # size the input images size_kernel=5, # size of the kernels in convolution and deconvolution size_batch=100, # mini-batch size for training and testing, must be square of an integer num_input_channels=3, # number of channels of input images num_encoder_channels=64, # number of channels of the first conv layer of encoder num_z_channels=50, # number of channels of the layer z (noise or code) num_categories=10, # number of categories (age segments) in the training dataset num_gen_channels=1024, # number of channels of the first deconv layer of generator enable_tile_label=True, # enable to tile the label tile_ratio=1.0, # ratio of the length between tiled label and z is_training=True, # flag for training or testing mode save_dir='./save', # path to save checkpoints, samples, and summary dataset_name='UTKFace' # name of the dataset in the folder ./data ): self.session = session self.image_value_range = (-1, 1) self.size_image = size_image self.size_kernel = size_kernel self.size_batch = size_batch self.num_input_channels = num_input_channels self.num_encoder_channels = num_encoder_channels self.num_z_channels = num_z_channels self.num_categories = num_categories self.num_gen_channels = num_gen_channels self.enable_tile_label = enable_tile_label self.tile_ratio = tile_ratio self.is_training = is_training self.save_dir = save_dir self.dataset_name = dataset_name # ************************************* input to graph ******************************************************** self.input_image = tf.placeholder( tf.float32, [self.size_batch, self.size_image, self.size_image, self.num_input_channels], name='input_images' ) self.age = tf.placeholder( tf.float32, [self.size_batch, self.num_categories], name='age_labels' ) self.gender = tf.placeholder( tf.float32, [self.size_batch, 2], name='gender_labels' ) self.z_prior = tf.placeholder( tf.float32, [self.size_batch, self.num_z_channels], name='z_prior' ) # ************************************* build the graph ******************************************************* print '\n\tBuilding graph ...' # encoder: input image --> z self.z = self.encoder( image=self.input_image ) # generator: z + label --> generated image self.G = self.generator( z=self.z, y=self.age, gender=self.gender, enable_tile_label=self.enable_tile_label, tile_ratio=self.tile_ratio ) # discriminator on z self.D_z, self.D_z_logits = self.discriminator_z( z=self.z, is_training=self.is_training ) # discriminator on G self.D_G, self.D_G_logits = self.discriminator_img( image=self.G, y=self.age, gender=self.gender, is_training=self.is_training ) # discriminator on z_prior self.D_z_prior, self.D_z_prior_logits = self.discriminator_z( z=self.z_prior, is_training=self.is_training, reuse_variables=True ) # discriminator on input image self.D_input, self.D_input_logits = self.discriminator_img( image=self.input_image, y=self.age, gender=self.gender, is_training=self.is_training, reuse_variables=True ) # ************************************* loss functions ******************************************************* # loss function of encoder + generator #self.EG_loss = tf.nn.l2_loss(self.input_image - self.G) / self.size_batch # L2 loss self.EG_loss = tf.reduce_mean(tf.abs(self.input_image - self.G)) # L1 loss # loss function of discriminator on z self.D_z_loss_prior = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_z_prior_logits, labels=tf.ones_like(self.D_z_prior_logits)) ) self.D_z_loss_z = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_z_logits, labels=tf.zeros_like(self.D_z_logits)) ) self.E_z_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_z_logits, labels=tf.ones_like(self.D_z_logits)) ) # loss function of discriminator on image self.D_img_loss_input = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_input_logits, labels=tf.ones_like(self.D_input_logits)) ) self.D_img_loss_G = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_G_logits, labels=tf.zeros_like(self.D_G_logits)) ) self.G_img_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=self.D_G_logits, labels=tf.ones_like(self.D_G_logits)) ) # total variation to smooth the generated image tv_y_size = self.size_image tv_x_size = self.size_image self.tv_loss = ( (tf.nn.l2_loss(self.G[:, 1:, :, :] - self.G[:, :self.size_image - 1, :, :]) / tv_y_size) + (tf.nn.l2_loss(self.G[:, :, 1:, :] - self.G[:, :, :self.size_image - 1, :]) / tv_x_size)) / self.size_batch # *********************************** trainable variables **************************************************** trainable_variables = tf.trainable_variables() # variables of encoder self.E_variables = [var for var in trainable_variables if 'E_' in var.name] # variables of generator self.G_variables = [var for var in trainable_variables if 'G_' in var.name] # variables of discriminator on z self.D_z_variables = [var for var in trainable_variables if 'D_z_' in var.name] # variables of discriminator on image self.D_img_variables = [var for var in trainable_variables if 'D_img_' in var.name] # ************************************* collect the summary *************************************** self.z_summary = tf.summary.histogram('z', self.z) self.z_prior_summary = tf.summary.histogram('z_prior', self.z_prior) self.EG_loss_summary = tf.summary.scalar('EG_loss', self.EG_loss) self.D_z_loss_z_summary = tf.summary.scalar('D_z_loss_z', self.D_z_loss_z) self.D_z_loss_prior_summary = tf.summary.scalar('D_z_loss_prior', self.D_z_loss_prior) self.E_z_loss_summary = tf.summary.scalar('E_z_loss', self.E_z_loss) self.D_z_logits_summary = tf.summary.histogram('D_z_logits', self.D_z_logits) self.D_z_prior_logits_summary = tf.summary.histogram('D_z_prior_logits', self.D_z_prior_logits) self.D_img_loss_input_summary = tf.summary.scalar('D_img_loss_input', self.D_img_loss_input) self.D_img_loss_G_summary = tf.summary.scalar('D_img_loss_G', self.D_img_loss_G) self.G_img_loss_summary = tf.summary.scalar('G_img_loss', self.G_img_loss) self.D_G_logits_summary = tf.summary.histogram