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

!wget --no-check-certificate \\
https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip \\
-O /tmp/rockpaperscissors.zip

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

!pip install split-folders
import split_folders
split_folders.ratio('/tmp/rockpaperscissors/rps-cv-images', output="/tmp/rockpaperscissors/hasil_split", seed=1337, ratio=(.8, .2))

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

# Train
train_paper_dir = os.path.join(train_dir, 'paper')
train_scissors_dir = os.path.join(train_dir, 'scissors')
train_rock_dir = os.path.join(train_dir, 'rock')
# Validation
validation_paper_dir = os.path.join(validation_dir, 'paper')
validation_scissors_dir = os.path.join(validation_dir, 'scissors')
validation_rock_dir = os.path.join(validation_dir, 'rock')

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,
    target_size=(150, 150),
    batch_size=4,
    class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
    validation_dir,
    target_size=(150, 150),
    batch_size=4,
    class_mode='categorical')

Hasil => Found 1749 images belonging to 3 classes.
Found 439 images belonging to 3 classes.

model = tf.keras.models.Sequential([
          tf.keras.layers.Conv2D(32, (3,3), padding='same', activation='relu',
                                input_shape=(150, 150, 3)),
          tf.keras.layers.MaxPooling2D(),
          tf.keras.layers.Dropout(0.2),
          tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
          tf.keras.layers.MaxPooling2D(),
          tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
          tf.keras.layers.MaxPooling2D(),
          tf.keras.layers.Dropout(0.2),
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(512, activation='relu'),
          tf.keras.layers.Dense(3, activation='softmax')

])

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

model.fit(
    train_generator,
    steps_per_epoch=35,
    epochs=10,
    validation_data=validation_generator,
    validation_steps=5,
    verbose=2)

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():
  #predic
  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=32)

  print(fn)
  hasil = np.argmax(classes)
  if classes[0][0]==0:
    print('rock')
  elif classes[0][1]==1:
    print('paper')
  elif classes[0][2]==2:
    print('scissors')