stone_scissors_paper.py

# -*- coding: utf-8 -*-
"""stone-scissors-paper

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1ANHoS1S4xZ2iFaqItzJoQIbrzoomkX9T
"""

!wget https://storage.googleapis.com/tensorflow-1-public/course2/week4/rps.zip

!wget https://storage.googleapis.com/tensorflow-1-public/course2/week4/rps-test-set.zip

import zipfile

local_zip = './rps.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('tmp/rps-train')
zip_ref.close()

local_zip = './rps-test-set.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('tmp/rps-test')
zip_ref.close()

import os

base_dir = 'tmp/rps-train/rps'

rock_dir = os.path.join(base_dir, 'rock')
paper_dir = os.path.join(base_dir, 'paper')
scissors_dir = os.path.join(base_dir, 'scissors')

print('total training rock images:', len(os.listdir(rock_dir)))
print('total training paper images:', len(os.listdir(paper_dir)))
print('total training scissors images:', len(os.listdir(scissors_dir)))

# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

pic_index = 2

next_rock = [os.path.join(rock_dir, fname)
                for fname in rock_files[pic_index-2:pic_index]]
next_paper = [os.path.join(paper_dir, fname)
                for fname in paper_files[pic_index-2:pic_index]]
next_scissors = [os.path.join(scissors_dir, fname)
                for fname in scissors_files[pic_index-2:pic_index]]

for i, img_path in enumerate(next_rock+next_paper+next_scissors):
  img = mpimg.imread(img_path)
  plt.imshow(img)
  plt.axis('Off')
  plt.show()

import tensorflow as tf

model = tf.keras.models.Sequential([

    tf.keras.layers.Conv2D(64, (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(128, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),

    tf.keras.layers.Flatten(),
    tf.keras.layers.Dropout(0.5),

    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(3, activation='softmax')
])

model.summary()

model.compile(loss = 'categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

from tensorflow.keras.preprocessing.image import ImageDataGenerator

TRAINING_DIR = "tmp/rps-train/rps"
training_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_DIR = "tmp/rps-test/rps-test-set"
validation_datagen = ImageDataGenerator(rescale = 1./255)

train_generator = training_datagen.flow_from_directory(
	TRAINING_DIR,
	target_size=(150,150),
	class_mode='categorical',
  batch_size=126
)

validation_generator = validation_datagen.flow_from_directory(
	VALIDATION_DIR,
	target_size=(150,150),
	class_mode='categorical',
  batch_size=126
)

history = model.fit(train_generator, epochs=25, steps_per_epoch=20, validation_data = validation_generator, verbose = 1, validation_steps=3)

import matplotlib.pyplot as plt

# Plot the results
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend(loc=0)
plt.figure()

plt.show()

import numpy as np
from google.colab import files
from tensorflow.keras.utils import load_img, img_to_array

uploaded = files.upload()

for fn in uploaded.keys():

  # predicting images
  path = fn
  img = load_img(path, target_size=(150, 150))
  x = img_to_array(img)
  x = np.expand_dims(x, axis=0)

  images = np.vstack([x])
  classes = model.predict(images, batch_size=10)
  print(fn)
  print(classes)