cd_cat_dog.py

# -*- coding: utf-8 -*-
"""cd_cat_dog.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1oKwMjbxQGAQB7IJswrglFi8qS5BPFyJq
"""

# Commented out IPython magic to ensure Python compatibility.
try:
  # This command only in Colab.
#   %tensorflow_version 2.x
except Exception:
  pass
import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator

import os
import numpy as np
import matplotlib.pyplot as plt

# Get project files
!wget https://cdn.freecodecamp.org/project-data/cats-and-dogs/cats_and_dogs.zip

!unzip cats_and_dogs.zip

PATH = 'cats_and_dogs'

train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')
test_dir = os.path.join(PATH, 'test')

# Get number of files in each directory. The train and validation directories
# each have the subdirecories "dogs" and "cats".
total_train = sum([len(files) for r, d, files in os.walk(train_dir)])
total_val = sum([len(files) for r, d, files in os.walk(validation_dir)])
total_test = len(os.listdir(test_dir))

# Variables for pre-processing and training.
batch_size = 128
IMG_HEIGHT = 150
IMG_WIDTH = 150

# 3
tf.random.set_seed(42)
np.random.seed(42)
# Variables for pre-processing and training.
batch_size = 32
IMG_HEIGHT = 150
IMG_WIDTH = 150
epochs = 60

train_image_generator = ImageDataGenerator(rescale=1./255)
validation_image_generator = ImageDataGenerator(rescale=1./255)
test_image_generator = ImageDataGenerator(rescale=1./255)

train_data_gen = train_image_generator.flow_from_directory(
    train_dir,
    target_size=(IMG_HEIGHT, IMG_WIDTH),
    batch_size=batch_size,
    class_mode='binary'
)
val_data_gen = train_image_generator.flow_from_directory(
    validation_dir,
    target_size=(IMG_HEIGHT, IMG_WIDTH),
    batch_size=batch_size,
    class_mode='binary'
)
test_data_gen = train_image_generator.flow_from_directory(
    PATH,
    classes=['test'],
    target_size=(IMG_HEIGHT, IMG_WIDTH),
    batch_size=batch_size,
    class_mode='binary'
)

# 4
def plotImages(images_arr, probabilities = False):
    fig, axes = plt.subplots(len(images_arr), 1, figsize=(5,len(images_arr) * 3))
    if probabilities is False:
      for img, ax in zip( images_arr, axes):
          ax.imshow(img)
          ax.axis('off')
    else:
      for img, probability, ax in zip( images_arr, probabilities, axes):
          ax.imshow(img)
          ax.axis('off')
          if probability > 0.5:
              ax.set_title("%.2f" % (probability*100) + "% dog")
          else:
              ax.set_title("%.2f" % ((1-probability)*100) + "% cat")
    plt.show()

sample_training_images, _ = next(train_data_gen)
plotImages(sample_training_images[:5])

train_image_generator = ImageDataGenerator(
    rescale=1./255,
    rotation_range=30,
    width_shift_range=0.15,
    height_shift_range=0.15,
    shear_range=0.15,
    zoom_range=0.15,
    horizontal_flip=True,
    brightness_range=(0.8,1.2),
    fill_mode='nearest'
)

# Cell 6
train_data_gen = train_image_generator.flow_from_directory(
    directory=train_dir,
    target_size=(IMG_HEIGHT, IMG_WIDTH),
    batch_size=batch_size,
    class_mode='binary'
)

augmented_images = [train_data_gen[0][0][0] for i in range(5)]
plotImages(augmented_images)

model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),
    MaxPooling2D(2, 2),

    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(2, 2),

    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D(2, 2),

    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D(2, 2),

    Conv2D(256, (3, 3), activation='relu'),  # NEW 5th layer
    MaxPooling2D(2, 2),

    Flatten(),
    Dropout(0.5),
    Dense(512, activation='relu'),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
              loss='binary_crossentropy', metrics=['accuracy'])


model.summary()

# 8
history = model.fit(
    train_data_gen,
    steps_per_epoch=total_train // batch_size,
    epochs=epochs,
    validation_data=val_data_gen,
    validation_steps=total_val // batch_size
)

# 9
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

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

epochs_range = range(len(acc))

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

# 10

# Reset test generator to start from beginning
test_data_gen.reset()

# Get predictions for all test images
probabilities = model.predict(test_data_gen, steps=len(test_data_gen))
probabilities = probabilities.flatten()

# Collect test images from batches
test_images = []
test_data_gen.reset()

# Get images batch by batch until we have 50
for batch in test_data_gen:
    for img in batch[0]:
        test_images.append(img)
        if len(test_images) >= 50:
            break
    if len(test_images) >= 50:
        break

# Plot only the first 50
plotImages(test_images[:50], probabilities=probabilities[:50])

# 11
answers =  [1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,
            1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0,
            1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,
            1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,
            0, 0, 0, 0, 0, 0]

correct = 0

for probability, answer in zip(probabilities, answers):
  if round(probability) == answer:
    correct +=1

percentage_identified = (correct / len(answers)) * 100

passed_challenge = percentage_identified >= 63

print(f"Your model correctly identified {round(percentage_identified, 2)}% of the images of cats and dogs.")

if passed_challenge:
  print("You passed the challenge!")
else:
  print("You haven't passed yet. Your model should identify at least 63% of the images. Keep trying. You will get it!")