Add python file

rockpaperscissors.py

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+# -*- coding: utf-8 -*-
+"""rockpaperscissors.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/17GrZhVdEjUOOy78uTjcWCHh0MSjla5D3
+
+# Import Libraries
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# import libraries
+import numpy as np
+import zipfile
+import os
+import matplotlib.pyplot as plt
+from google.colab import files
+from keras.preprocessing import image
+import matplotlib.image as mpimg
+# %matplotlib inline
+
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.callbacks import Callback
+# Check tensorflow version
+print(tf.__version__)
+
+"""# Import Dataset"""
+
+# import datasets
+!wget --no-check-certificate \
+    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip \
+    -O /tmp/rps.zip
+
+!wget --no-check-certificate \
+    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip \
+    -O /tmp/rps-test-set.zip
+
+"""# Extract file"""
+
+# Extract zip file
+local_zip = '/tmp/rps.zip'
+zip_ref = zipfile.ZipFile(local_zip, 'r')
+zip_ref.extractall('/tmp/')
+zip_ref.close()
+
+local_zip = '/tmp/rps-test-set.zip'
+zip_ref = zipfile.ZipFile(local_zip, 'r')
+zip_ref.extractall('/tmp/')
+zip_ref.close()
+
+# Check rps directory
+os.listdir('/tmp/rps')
+
+# Check rps-test-set direcroty
+os.listdir('/tmp/rps-test-set')
+
+"""# Image Augmentation"""
+
+# image augmentation
+train_datagen = ImageDataGenerator(
+    rescale = 1./255,
+	  rotation_range=40,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest',
+)
+
+validation_datagen = ImageDataGenerator(
+    rescale = 1./255,
+)
+
+TRAINING_DIR = "/tmp/rps/"
+train_generator = train_datagen.flow_from_directory(
+    TRAINING_DIR,
+    target_size = (150, 150),
+    batch_size=32,
+    class_mode='categorical',
+)
+
+VALIDATION_DIR = "/tmp/rps-test-set/"
+validation_generator = validation_datagen.flow_from_directory(
+    VALIDATION_DIR,
+    target_size = (150, 150),
+    batch_size = 32,
+    class_mode = 'categorical',
+)
+
+"""# Convolution Neural Network"""
+
+model = tf.keras.models.Sequential([
+    # Note the input shape is the desired size of the image 150x150 with 3 bytes color
+    # This is the first convolution
+    tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150, 150, 3)),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    # The second convolution
+    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2,2),
+    # The third convolution
+    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2,2),
+    # The fourth convolution
+    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2,2),
+    # Flatten the results to feed into a DNN
+    tf.keras.layers.Flatten(),
+    tf.keras.layers.Dropout(0.5),
+    # 512 neuron hidden layer
+    tf.keras.layers.Dense(512, activation='relu'),
+    tf.keras.layers.Dense(3, activation='softmax')
+])
+
+# Tampilkan summary model
+model.summary()
+
+# Compile model
+model.compile(loss = 'categorical_crossentropy', 
+              optimizer='rmsprop',
+              metrics=['accuracy'])
+
+"""# Create Callbacks"""
+
+# Create callbacks
+class CustomCallback(Callback):
+    def on_train_begin(self, logs=None):
+        print("Starting training")
+
+    def on_train_end(self, logs=None):
+        print("Training has been stopped")
+        print('Accuracy = %2.2f%%' %(logs['accuracy']*100))
+
+reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(
+    monitor='val_loss', 
+    factor=0.2,
+    patience=5, 
+    min_lr=1.5e-5
+)
+
+early_stopping = tf.keras.callbacks.EarlyStopping(
+    monitor="val_loss",
+    min_delta=0,
+    patience=12,
+    verbose=0,
+    mode="auto",
+    baseline=None,
+    restore_best_weights=True
+)
+
+history = model.fit(train_generator, 
+                    epochs=30,
+                    steps_per_epoch=20,
+                    validation_data = validation_generator,
+                    verbose = 1,
+                    validation_steps=3,
+                    callbacks=[CustomCallback(), reduce_lr, early_stopping]
+                    )
+
+"""# Model Evaluate"""
+
+# Evaluation of training data
+print("Evaluation of training data")
+results = model.evaluate(train_generator, batch_size=32)
+print('Loss: {:.4f}'.format(results[0]))
+print('Accuracy: {:.2f}%'.format(results[1]*100))
+
+# Evaluation of training data
+print("Evaluation of validation data")
+results = model.evaluate(validation_generator, batch_size=32)
+print('Loss: {:.4f}'.format(results[0]))
+print('Accuracy: {:.2f}%'.format(results[1]*100))
+
+"""# Plotting Loss and Accuracy"""
+
+fig, ax = plt.subplots(1, 2)
+fig.set_size_inches(12,4)
+
+# Define accuracy
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+
+# Define loss
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+
+epochs = range(len(acc))
+
+# Plotting accuracy
+ax[0].plot(epochs, acc, 'r', label='Training Accuracy')
+ax[0].plot(epochs, val_acc, 'b', label='Validation Accuracy')
+ax[0].set_title('Training and Validation Accuracy')
+ax[0].set_xlabel('Epoch')
+ax[0].set_ylabel('Accuracy')
+ax[0].legend(loc='upper left')
+
+# Plotting loss 
+ax[1].plot(epochs, loss, 'r', label='Training Loss')
+ax[1].plot(epochs, val_loss, 'b', label='Validation Loss')
+ax[1].set_title('Training and Validation Loss')
+ax[1].set_xlabel('Epoch')
+ax[1].set_ylabel('Loss')
+ax[1].legend(loc='upper left')
+
+plt.show()
+
+"""# Model Prediction"""
+
+def predict_image(image_upload, model = model):
+  im = image_upload
+  im_array = np.asarray(im)
+  im_array = im_array*(1/225)
+  im_input = tf.reshape(im_array, shape = [1, 150, 150, 3])
+
+  predict_array = model.predict(im_input)[0]
+
+  import pandas as pd
+  df = pd.DataFrame(predict_array)
+  df = df.rename({0:'Probability'}, axis = 'columns')
+  prod = ['Paper', 'Rock', 'Scissors']
+  df['Product'] = prod
+  df = df[['Product', 'Probability']]
+
+  predict_label = np.argmax(model.predict(im_input))
+
+  if predict_label == 0:
+      predict_product = 'Paper'
+  elif predict_label == 1:
+      predict_product = 'Rock'
+  else:
+      predict_product = 'Scissor'
+
+  return predict_product, df
+
+uploaded = files.upload()
+
+for fn in uploaded.keys():
+  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)
+  img = np.vstack([x])
+
+label, df = predict_image(img)
+
+print('\n')
+plt.show()
+print("\nThe image is detected as " + label)
+print('\n')
+print(df)
+print('\n')
+