Classification of Kitchen Tools & Materials

Introduction

Daftar Anggota:

Hiskia Anggi Puji Pratama - A11.2020.12730

Moh. Arda Fadly Robby - A11.2020.13087

Muhammad Ariq Pratama - A11.2020.12944

Satrio Arda Alfiansyah - A11.2020.12739

Data Preparation

import zipfile,os local_zip = 'kitset.zip' zip_ref = zipfile.ZipFile(local_zip, 'r') zip_ref.extractall('/tmp/kitchenset') zip_ref.close()

import splitfolders import shutil old_tmp_folder = "/tmp/kitchenset/" new_tmp_folder = "/tmp/images" splitfolders.ratio(old_tmp_folder, output=new_tmp_folder, seed=42, ratio=(.8, .2)) if os.path.isdir(old_tmp_folder): shutil.rmtree(old_tmp_folder)

base_dir = '/tmp/images' train_dir = os.path.join(base_dir, 'train') validation_dir = os.path.join(base_dir, 'val')

Data Preprocessing

from tensorflow.keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=20, zoom_range=0.2, shear_range=0.2, fill_mode='nearest', validation_split=0.1) test_datagen = ImageDataGenerator( rescale=1./255)

train_generator = train_datagen.flow_from_directory( train_dir, target_size=(150, 150), batch_size=16, class_mode='categorical') validation_generator = test_datagen.flow_from_directory( validation_dir, target_size=(150, 150), batch_size=16, class_mode='categorical')

train_generator.class_indices

Modelling

import tensorflow as tf from tensorflow.keras.layers import Input from tensorflow.keras.applications import ResNet50 from tensorflow.keras.applications import ResNet152V2 model = tf.keras.models.Sequential([ ResNet152V2(weights="imagenet", include_top=False, input_tensor=Input(shape=(150, 150, 3))), tf.keras.layers.Dropout(0.4), tf.keras.layers.Flatten(), tf.keras.layers.Dense(512, activation='relu'), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(14, activation='softmax') ]) model.layers[0].trainable = False

model.summary()

model.compile(optimizer=tf.optimizers.Adam(), loss='categorical_crossentropy', metrics = ['accuracy'])

history = model.fit(train_generator, validation_data=validation_generator, epochs=50, verbose=2)

Summary

import matplotlib.pyplot as plt plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.title('Akurasi Model') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show()

Deploy

Kode di bawah menunjukkan bagaimana menyimpan model pengklasifikasi citra yang telah dibuat dan dilatih menggunakan TensorFlow. Fungsi save() digunakan untuk menyimpan model ke dalam file dengan nama 'pcd-ta.h5'. Ini memungkinkan model untuk digunakan kembali pada waktu yang akan datang tanpa perlu melakukan pelatihan ulang. File yang disimpan menggunakan format HDF5, yang merupakan format yang umum digunakan untuk menyimpan model pembelajaran mesin.