TensorFlow Exercise: Judging gender and age based on big face

This post uses TensorFlow as an exercise to infer the age and gender of photos based on the face, and there are many similar apps online. Training Data –adience data set

The adience data set is from Flickr, which is used by users on the iphone or other smartphone devices, and is primarily intended for unfiltered face estimation of age and gender. At the same time, the corresponding landmark is also labeled, which contains 2,284 categories and 26580 images.

Adience Data Set Download address: Http://www.openu.ac.il/home/hassner/Adience/data.html#agegender

Because the data source FTP site is wall, I can only use the ladder, the download process is very long and painful. In order to protect you from torture, I sent a copy to the net disk.Code Import OS import glob import tensorflow as TF # 0.12 from tensorflow.contrib.layers import * from Tensorflow.contrib.slim . python.slim.nets.inception_v3 Import inception_v3_base Import numpy as NP from random import Shuffle age_table=[' (0, 2) ', ' (4, 6) ', ' (8, +) ', ' (+ +) ', ' (+ +) ', ' (+ +) ', ' (+) ', ' (+) ', ' (+ +) ', ' (+) ', ' ([+] ') ', ' (+ + M: Male # Age==true Training age model, false, training gender model: Age = False if ages = = True:lables_size = Len (age_table) # aged else:lables_size = Len ( sex_table) # Sex face_set_fold = ' adiencebenchmarkofunfilteredfacesforgenderandageclassification ' Fold_0_data = Os.path.join (Face_set_fold, ' fold_0_data.txt ') Fold_1_data = Os.path.join (face_set_fold, ' fold_1_data.txt ') fold_2_ data = Os.path.join (Face_set_fold, ' fold_2_data.txt ') Fold_3_data = Os.path.join (face_set_fold, ' fold_3_data.txt ') Fold_4_data = Os.path.join (face_set_fold, ' fold_4_data.txt ') Face_image_set = Os.path.join (face_set_fold, ' aligned ') def parse_data (fold_x_data): Data_set = [] with open (Fold_x_data, ' R ') as F:line_one = True for line in f:tmp = [] if Line_one = = True:line_one = False Continue Tmp.append (line.split (' \ t ') [0]) Tmp.append (Line.split (' \ t ') [1]) Tmp.append (line.split (' t ') [3]) Tmp.append (line.split (' \ t ') [4]) File_path = Os.path.join (Face_image_set, tmp[0]) if Os.path.exists (file_path): filenames = Glob.glob (File_path + "/*.jpg") for FileName in filenames:if tmp[1] "filename:break if age = = True:if tmp[2] in age_table:data_set.append ([FileName, age _table.index (Tmp[2])] else:if tmp[3] in sex_table:data_set.append ([FileName, Sex_table.index (tmp[3])]) return Data_ Set data_set_0 = Parse_data (fold_0_data) data_set_1 = Parse_data (fold_1_data) data_set_2 = Parse_data (fold_2_data) Data_ Set_3 = Parse_data (fold_3_data) Data_set_4 = Parse_data (fold_4_data) Data_set = data_set_0 + data_set_1 + data_set_2 + dat A_set_3 + Data_set_4 Shuffle (data_set) # scaled image size Image_height = 227 image_width = 227 # read scaled image jpg_data = Tf.placeholder (d type=tf.string) decode_jpg = Tf.image.decode_jpeg (Jpg_data, channels=3) resize = tf.image.resize_images (decode_jpg, [Image_height, image_width]) resize = tf.cast (resize, TF.UINT8)/255 def resize_image (file_name): With Tf.gfile.FastGFile (file_name, ' R ') as F:image_data = F.read () with TF.S Ession () as Sess:image = Sess.run (resize, feed_dict={jpg_data:image_data}) return image pointer = 0 # a bit slow (sleep first), you should first handle the picture or Using String_input_producer def get_next_batch (Data_set, batch_size=128): global pointer batch_x = [] batch_y = [] for i in RA Nge (batch_size): Batch_x.append (Resize_image (data_set[pointer][0])) Batch_y.append (data_set[pointer][1]) pointer + = 1 return batch_x, batch_y batch_size = Num_batch = Len (data_set)//Batch_size x = Tf.placeholder (Dtype=tf.float32, Shape=[batch_size, Image_height, Image_width, 3]) Y = Tf.placeholder (Dtype=tf.int32, shape=[batch_size]) def conv_net ( Nlabels, images, pkeep=1.0): Weights_regularizer = Tf.contrib.layers.l2_regularizer (0.0005) with Tf.variable_scope (" Conv_net "," conv_net ", [images]) as Scope:wITH Tf.contrib.slim.arg_scope ([convolution2d, fully_connected], Weights_regularizer=weights_regularizer, Biases_ Initializer=tf.constant_initializer (1.), Weights_initializer=tf.random_normal_initializer (stddev=0.005), Trainable=true): With Tf.contrib.slim.arg_scope ([convolution2d], Weights_initializer=tf.random_normal_initializer (stddev=0.01)): Conv1 = convolution2d (images,, [7,7], [4, 4], padding= ' VALID ', Biases_initializer=tf.constant_ Initializer (0.), scope= ' conv1 ') pool1 = max_pool2d (CONV1, 3, 2, padding= ' VALID ', scope= ' pool1 ') Norm1 = Tf.nn.local_respo Nse_normalization (Pool1, 5, alpha=0.0001, beta=0.75, name= ' Norm1 ') conv2 = convolution2d (Norm1,, [5, 5], [1, 1], Paddi Ng= ' same ', scope= ' conv2 ') pool2 = max_pool2d (Conv2, 3, 2, padding= ' VALID ', scope= ' pool2 ') Norm2 = Tf.nn.local_response_no Rmalization (Pool2, 5, alpha=0.0001, beta=0.75, name= ' norm2 ') conv3 = convolution2d (Norm2, 384, [3, 3], [1, 1], Biases_init Ializer=tf.constant_initializer (0.), padding= ' same ', scope= ' conv3 ') pooL3 = max_pool2d (Conv3, 3, 2, padding= ' VALID ', scope= ' pool3 ') flat = Tf.reshape (Pool3, [-1, 384*6*6], name= ' reshape ') full1 = Fully_connected (flat, scope= ' full1 ') Drop1 = Tf.nn.dropout (Full1, Pkeep, name= ' drop1 ') full2 = fully_connected (dro P1, scope= ' full2 ') Drop2 = Tf.nn.dropout (Full2, Pkeep, name= ' Drop2 ') with Tf.variable_scope (' output ') as Scope:weigh TS = tf. Variable (Tf.random_normal ([Nlabels], mean=0.0, stddev=0.01), name= ' weights ') biases = tf. Variable (tf.constant (0.0, shape=[nlabels], dtype=tf.float32), name= ' biases ') output = Tf.add (Tf.matmul (DROP2, weights ), biases, name=scope.name) return output DEF training (): Logits = Conv_net (Lables_size, X) def optimizer (ETA, LOSS_FN): G Lobal_step = tf. Variable (0, trainable=false) Optz = Lambda Lr:tf.train.MomentumOptimizer (LR, 0.9) Lr_decay_fn = Lambda lr,global_step:t F.train.exponential_decay (LR, global_step, 0.97, staircase=true) return Tf.contrib.layers.optimize_loss (LOSS_FN , Global_step, ETA, OPTZ, clip_gradientS=4., LEARNING_RATE_DECAY_FN=LR_DECAY_FN) def loss (logits, labels): cross_entropy = Tf.nn.sparse_softmax_cross_ Entropy_with_logits (logits, labels) Cross_entropy_mean = Tf.reduce_mean (cross_entropy) regularization_losses = Tf.get _collection (TF. graphkeys.regularization_losses) Total_loss = cross_entropy_mean + 0.01 * SUM (regularization_losses) loss_averages = Tf.train.ExponentialMovingAverage (0.9) Loss_averages_op = Loss_averages.apply ([Cross_entropy_mean] + [Total_loss]) With Tf.control_dependencies ([Loss_averages_op]): Total_loss = tf.identity (Total_loss) return Total_loss # loss Total_ Loss = loss (Logits, Y) # Optimizer train_op = Optimizer (0.001, total_loss) saver = Tf.train.Saver (Tf.global_variables ()) W ITH TF. Session () as Sess:sess.run (Tf.global_variables_initializer ()) global pointer epoch = 0 while true:pointer = 0 for batch In range (Num_batch): batch_x, batch_y = Get_next_batch (Data_set, Batch_size) _, Loss_value = Sess.run ([Train_op, Total_lo SS], feed_dict={x:batch_x, Y:batch_Y}) Print (Epoch, batch, Loss_value) Saver.save (sess, ' age.module ' if age = = True Else ' sex.module ') Epoch + = 1 Training () "" "# Detect gender and Age # change batch_size to 1 def detect_age_or_sex (image_path): Logits = Conv_net (lables_size, X) saver = Tf.train.Saver () with TF. Session () as Sess:saver.restore (Sess, './age.module ' if age = = True Else './sex.module ') Softmax_output = Tf.nn.softmax (l ogits) res = Sess.run (softmax_output, Feed_dict={x:[resize_image (Image_path)]}) res = Np.argmax (res) if age = = True:retur N Age_table[res] Else:return sex_table[res] "" "

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Import OS import glob import tensorflow as TF # 0.12 from TensorFlow. Contrib. Layers Import * from TensorFlow. Co Ntrib. Slim. Python. Slim. Nets. Inception_v3 Import inception_v3_base Import numpy as NP from random import shuffle   age_table = [' (0, 2) ', ' (4, 6 ) ', ' (8, +) ', ' ((+) ', ' (+ +) ', ' (+, +) ', ' ((+) ', ' (+ +) ', ' (+ +) ', ' ([+] ') ', ' [' f ', '] sex_table # f: female; M: Men's   # Age==true training age model, false, training gender model ages = false   if age = = True:lables_size = Len (age_table) # aged else: Lables_size = Len (sex_table) # sex   Face_set_fold = ' Adiencebenchmarkofunfilteredfacesforgenderandageclassificati On '   Fold_0_data = Os.path. Join (Face_set_fold, ' fold_0_data.txt ') Fold_1_data = Os.path. Join (Face_set_fold, ' fold_1_data.txt ') Fold_2_data = Os.path. Join (Face_set_fold, ' fold_2_data.txt ') fold_3_data