[Python] dropout suppresses overfitting

Tensorflow Realization Dropout Inhibit overfitting

import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Dropout Inhibit overfitting 
(train_image, train_label), (test_image, test_label) = tf.keras.datasets.fashion_mnist.load_data()
train_label_onehot = tf.keras.utils.to_categorical(train_label)
test_label_onehot = tf.keras.utils.to_categorical(test_label)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['acc'])
history = model.fit(train_image, train_label_onehot, epochs=10, validation_data=(test_image, test_label_onehot))
plt.plot(history.epoch, history.history.get('loss'), label='loss')
plt.plot(history.epoch, history.history.get('val_loss'), label='val_loss')
plt.legend()
plt.show()
plt.plot(history.epoch, history.history.get('acc'), label='acc')
plt.plot(history.epoch, history.history.get('val_acc'), label='val_acc')
# Load legend 
plt.legend()
plt.show()