Age, Gender & Ethnicity Prediction

import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.layers as L
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from sklearn.model_selection import train_test_split

Loading Dataset

data = pd.read_csv("/root/yj/yj/Kaggle/data/age_gender/age_gender.csv")

data['pixels'] = data['pixels'].apply(lambda x : np.array(x.split(), dtype = "float32"))

data.head()

print("Total rows : {}".format(len(data)))
print("Totla columns: {}".format(len(data.columns)))

Distributions

## normalizing pixels data
data['pixels'] = data['pixels'].apply(lambda x: x/255)

## calculating distributions
age_dist = data['age'].value_counts()
ethnicity_dist = data['ethnicity'].value_counts()
gender_dist = data['gender'].value_counts().rename(index={0:'Male',1:'Female'})

def ditribution_plot(x,y,name):
    fig = go.Figure([
        go.Bar(x=x, y=y)
    ])

    fig.update_layout(title_text=name)
    fig.show()

ditribution_plot(x=age_dist.index, y=age_dist.values, name='Age Distribution')

ditribution_plot(x=ethnicity_dist.index, y=ethnicity_dist.values, name='Ethnicity Distribution')

ditribution_plot(x=gender_dist.index, y=gender_dist.values, name='Gender Distribution')

X = np.array((data['pixels']).tolist())

X = X.reshape(X.shape[0], 48, 48, 1)

plt.figure(figsize=(16,16))
for i in range(1500,1520):
    plt.subplot(5,5,(i%25)+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(data['pixels'].iloc[i].reshape(48,48))
    plt.xlabel(
        "Age:"+str(data['age'].iloc[i])+
        "  Ethnicity:"+str(data['ethnicity'].iloc[i])+
        "  Gender:"+ str(data['gender'].iloc[i])
    )
plt.show()

Model for Gender Prediction

y = data['gender']

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.22, random_state=3)

Build and train model

model = tf.keras.Sequential([
    L.InputLayer(input_shape=(48,48,1)),
    L.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    L.BatchNormalization(),
    L.MaxPooling2D((2, 2)),
    L.Conv2D(64, (3, 3), activation='relu'),
    L.MaxPooling2D((2, 2)),
    L.Flatten(),
    L.Dense(64, activation='relu'),
    L.Dropout(rate=0.5),
    L.Dense(1, activation='sigmoid')
])

model.compile(optimizer='sgd',
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=['accuracy'])

## Stop training when validation loss reach 0.2700
class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if(logs.get('val_loss')<0.2700):
            print("\nReached 0.2700 val_loss so cancelling training!")
            self.model.stop_training = True
        
callback = myCallback()

model.summary()

history = model.fit(
    X_train, y_train, epochs=20, validation_split=0.1, batch_size=64, callbacks=[callback]
)

 

fig = px.line(
    history.history, y=['loss', 'val_loss'],
    labels={'index': 'epoch', 'value': 'loss'}, 
    title='Training History')
fig.show()

loss, acc = model.evaluate(X_test,y_test,verbose=0)
print('Test loss: {}'.format(loss))
print('Test Accuracy: {}'.format(acc))