cnn_clean_vs_messy_room.py

# -*- coding: utf-8 -*-
"""CNN Clean vs Messy Room.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1dJbaufuxlhlq4DIXloAvI2BR4aQmjSF1
"""

import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator

!wget --no-check-certificate \
  https://github.com/dicodingacademy/assets/raw/main/ml_pemula_academy/messy-vs-clean-room.zip \
  -O /tmp/messy_vs_clean_room.zip

# melakukan ekstraksi pada file zip
import zipfile,os
local_zip = '/tmp/messy_vs_clean_room.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp')
zip_ref.close()

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

os.listdir('/tmp/images/train')

os.listdir('/tmp/images/val')

# membuat direktori ruangan rapi pada direktori data training
train_clean_dir = os.path.join(train_dir, 'clean')

# membuat direktori ruangan berantakan pada direktori data training
train_messy_dir = os.path.join(train_dir, 'messy')

# membuat direktori ruangan rapi pada direktori data validasi
validation_clean_dir = os.path.join(validation_dir, 'clean')

# membuat direktori ruangan berantakan pada direktori data validasi
validation_messy_dir = os.path.join(validation_dir, 'messy')

train_datagen = ImageDataGenerator(
                    rescale=1./255,
                    rotation_range=20,
                    horizontal_flip=True,
                    shear_range = 0.2,
                    fill_mode = 'nearest')

test_datagen = ImageDataGenerator(
                    rescale=1./255,
                    rotation_range=20,
                    horizontal_flip=True,
                    shear_range = 0.2,
                    fill_mode = 'nearest')

train_generator = train_datagen.flow_from_directory(
        train_dir,  # direktori data latih
        target_size=(150, 150),  # mengubah resolusi seluruh gambar menjadi 150x150 piksel
        batch_size=4,
        # karena ini merupakan masalah klasifikasi 2 kelas maka menggunakan class_mode = 'binary'
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        validation_dir, # direktori data validasi
        target_size=(150, 150), # mengubah resolusi seluruh gambar menjadi 150x150 piksel
        batch_size=4, # karena ini merupakan masalah klasifikasi 2 kelas maka menggunakan class_mode = 'binary'
        class_mode='binary')

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(512, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.summary()

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

history = model.fit(
      train_generator,
      steps_per_epoch=25,  # berapa batch yang akan dieksekusi pada setiap epoch
      epochs=25,
      validation_data=validation_generator, # menampilkan akurasi pengujian data validasi
      validation_steps=5,  # berapa batch yang akan dieksekusi pada setiap epoch
)

"""# Plotting Accuracy and Loss"""

import matplotlib.pyplot as plt

# Define accuracy
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

epochs = range(len(acc))

# Plotting accuracy
plt.figure(figsize=(8, 6))
plt.plot(epochs, acc, 'r', label='Training Accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()

# Define loss
loss = history.history['loss']
val_loss = history.history['val_loss']

# Plotting loss 
plt.figure(figsize=(8, 6))
plt.plot(epochs, loss, 'r', label='Training Loss')
plt.plot(epochs, val_loss, 'b', label='Validation Loss')
plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Lost')
plt.legend(loc='upper right')
plt.grid(False)
plt.show()

# Commented out IPython magic to ensure Python compatibility.
import numpy as np
from google.colab import files
from keras.preprocessing import image
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# %matplotlib inline

uploaded = files.upload()

for fn in uploaded.keys():
 
  # predicting images
  path = fn
  img = image.load_img(path, target_size=(150,150))
  imgplot = plt.imshow(img)
  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)

  images = np.vstack([x])
  classes = model.predict(images, batch_size=10)
  
  print(fn)
  if classes==0:
    print('The room is clean')
  else:
    print('The room is messy')