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>Modell

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Eines der Modelle zur Untersuchung von Gesichtern ist der Large Scale Face Dataset (UTKFace)

Webpage: UTKFace

# setup
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
import os
from tqdm import tqdm

# import data
df = pd.read_csv('../../tensorflow_datasets/utkface/extracted_info.csv')
df.dropna(inplace=True)
df = df[df['Age'] <= 90]
df = df[df['Age'] >= 0]
df = df.reset_index()
df['Name'] = df['Name'].apply(lambda x: '../../tensorflow_datasets/utkface/UTKFace/' + str(x))

num_classes = len(df['Age'].unique())
df.head()

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Zum Erstellen des Modells importieren wir die folgenden Bibliotheken und definieren eine Routine zum Anzeigen der Bilder:

import tensorflow as tf
import keras
from keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint,EarlyStopping
from keras.layers import Dense, Activation, Dropout, Flatten, Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Activation
from keras.layers import Conv2D, AveragePooling2D
from keras.models import Model, Sequential

from sklearn.model_selection import train_test_split

from keras import metrics

from keras.models import model_from_json
import matplotlib.pyplot as plt

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Statistik des Alters der Stichprobe

# age statistics
df.hist('Age')

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Da das Alter in Jahren angegeben wird, ist der niedrigste Wert 1. Der Prozess ist jedoch zuverlässiger, wenn die Skala bei 0 beginnt, sodass das Alter durch Subtraktion von eins renormiert wird:

# renormalization
df['Age'] = df['Age'] - 1

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Strukturieren der Informationen für Training und Tests

# Zuweisung von Variablen
df_data = df.Name
y_data = df.Age
y2_data = df.Gender

# Array bilden
X_train, X_test, y_train, y_test = train_test_split(df_data, y_data, test_size=0.20, random_state=40)

# Daten anzeigen
d = {'Name':X_train,'Age':y_train}
df_train = pd.concat(d,axis=1)
df_train.head(3)

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Informationsstruktur zum Trainieren und Validieren

# Zuweisung von Variablen
df_data = df_train.Name
y_data = df_train.Age
y2_data = df.Gender

# Array bilden
X_train, X_val, y_train, y_val = train_test_split(df_data, y_data, test_size=0.1, random_state=42)

# Daten anzeigen
d = {'Name':X_train,'Age':y_train}
train = pd.concat(d,axis=1)
train.head(3)

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Die Bilder müssen verarbeitet werden, um den Lernprozess zu entwickeln:

# Umwandlung von Alter to String
train['Age'] = train['Age'].astype('str')
df_test['Age'] = df_test['Age'].astype('str')
val['Age'] = val['Age'].astype('str')

# Bild verarbeitung
batch = 512
train_gen = ImageDataGenerator(rescale=1./255)
test_gen = ImageDataGenerator(rescale=1./255)
train_data = train_gen.flow_from_dataframe(dataframe = train, 
                                           #directory = train_folder, 
                                           x_col = 'Name',
                                           y_col = 'Age', seed = 42,
                                           batch_size = batch,
                                           shuffle = True, 
                                           class_mode='sparse',
                                           target_size = (224, 224))

test_data = test_gen.flow_from_dataframe(dataframe = df_test,
                                         #directory = test_folder,
                                         x_col = 'Name',
                                         y_col = 'Age',
                                         batch_size = batch,
                                         shuffle = True,
                                         class_mode='sparse',
                                         target_size = (224, 224))

val_data = train_gen.flow_from_dataframe(dataframe = val, 
                                           #directory = train_folder, 
                                           x_col = 'Name',
                                           y_col = 'Age', seed = 42,
                                           batch_size = batch,
                                           shuffle = True, 
                                           class_mode='sparse',
                                           target_size = (224, 224))

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Um das Modell zusammenzubauen

# define model
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
 
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
 
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
 
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
 
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
 
model.add(Convolution2D(4096, (7, 7), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(4096, (1, 1), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(2622, (1, 1)))
model.add(Flatten())
model.add(Activation('softmax'))

# load weights
model.load_weights('../../tensorflow_datasets/utkface/vgg_face_weights.h5')

# block first layers
for layer in model.layers[:-6]:
    layer.trainable = False

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Die Vorhersageschichten werden mit dem Basismodell zusammengestellt und zusammengesetzt:

# build prediction section
base_model_output = Convolution2D(num_classes, (1, 1), name='predictions')(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)

# ensamble age model
age_model = Model(inputs=model.input, outputs=base_model_output)
age_model

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Um das Modell zu seigen kann die Funktion summary verwendet werden:

# Strukture anzeigen
age_model.summary()

Model: 'sequential'

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

conv2d_45 (Conv2D) (None, 30, 30, 32) 896

_________________________________________________________________

conv2d_46 (Conv2D) (None, 28, 28, 32) 9248

_________________________________________________________________

max_pooling2d_18 (MaxPooling (None, 14, 14, 32) 0

_________________________________________________________________

dropout_27 (Dropout) (None, 14, 14, 32) 0

_________________________________________________________________

conv2d_47 (Conv2D) (None, 12, 12, 64) 18496

_________________________________________________________________

conv2d_48 (Conv2D) (None, 10, 10, 64) 36928

_________________________________________________________________

max_pooling2d_19 (MaxPooling (None, 5, 5, 64) 0

_________________________________________________________________

dropout_28 (Dropout) (None, 5, 5, 64) 0

_________________________________________________________________

conv2d_49 (Conv2D) (None, 3, 3, 84) 48468

_________________________________________________________________

dropout_29 (Dropout) (None, 3, 3, 84) 0

_________________________________________________________________

flatten_9 (Flatten) (None, 756) 0

_________________________________________________________________

dense_9 (Dense) (None, 64) 48448

_________________________________________________________________

age_out (Dense) (None, 1) 65

=================================================================

Total params: 162,549

Trainable params: 162,549

Non-trainable params: 0

_________________________________________________________________

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Formularmodell:

age_model.compile(loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True)
                  , optimizer=keras.optimizers.Adam()
                  , metrics=['accuracy']
                 )
checkpointer = ModelCheckpoint(
    filepath='classification_age_model_utk.hdf5'
    , monitor = 'val_loss'
    , verbose=1
    , save_best_only=True
    , mode = 'auto'
)
target = df['Age'].values
target_classes = keras.utils.to_categorical(target, num_classes)
callback = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=3)

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Um das Training durchzuführen:

history_2 = age_model.fit(
    train_data,
    validation_data=val_data,
    epochs= 10,
    callbacks = [checkpointer],
    shuffle=False
)
eff_epochs_2 = len(history_2.history['loss'])

Bekommt

Epoch 1/200

67/67 [==============================] - 15s 215ms/step - loss: 22.6157 - val_loss: 18.4468

Epoch 2/200

67/67 [==============================] - 14s 206ms/step - loss: 15.1609 - val_loss: 16.1411

Epoch 3/200

67/67 [==============================] - 14s 203ms/step - loss: 13.2656 - val_loss: 11.5077

Epoch 4/200

67/67 [==============================] - 14s 203ms/step - loss: 11.9682 - val_loss: 15.2756

Epoch 5/200

67/67 [==============================] - 14s 205ms/step - loss: 11.3574 - val_loss: 10.7042

Epoch 6/200

67/67 [==============================] - 14s 206ms/step - loss: 10.3004 - val_loss: 15.9495

Epoch 7/200

67/67 [==============================] - 14s 205ms/step - loss: 10.3693 - val_loss: 9.1207

Epoch 8/200

67/67 [==============================] - 14s 206ms/step - loss: 9.6405 - val_loss: 12.4468

Epoch 9/200

67/67 [==============================] - 14s 205ms/step - loss: 9.5810 - val_loss: 11.9415

Epoch 10/200

67/67 [==============================] - 14s 208ms/step - loss: 9.7127 - val_loss: 10.0224

Epoch 11/200

67/67 [==============================] - 14s 207ms/step - loss: 9.0834 - val_loss: 12.9822

Epoch 12/200

67/67 [==============================] - 14s 205ms/step - loss: 9.1098 - val_loss: 11.2629

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Para evaluar se corre la validación:

B = age_model.predict(test_data)
output_indexes = np.array([i for i in range(0, num_classes)])
apparent_predictions = np.sum(B * output_indexes, axis = 1)

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Um die Entwicklung zu überprüfen, können Sie jede der Aufzeichnungen aufrufen und das geschätzte und das tatsächliche Alter überprüfen:

df_test['Weighted_Avg'] = apparent_predictions
argmax = []
for p in B: 
    predm = np.argmax(p)
    argmax.append(predm)
df_test['ArgMax'] = argmax
df_test.to_csv('Final__wtd_avg')

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Um ein Modell zu speichern, führen Sie den Befehl save aus:

# almacenar modelos
directory = 'F:/go/face_scrapper/faces/age_model_20210723'
age_model.save(directory)

INFO:tensorflow:Assets written to: F:/go/face_scrapper/faces/age_model_20210723\assets

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