cats_and_dogs_classifier

PYTHON/cats_and_dogs_classifier.py

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+# ============================================================
+# Cat vs Dog Image Classifier - TensorFlow 2.0 / Keras CNN
+# freeCodeCamp Machine Learning Certification Project
+# ============================================================
+
+# ─── Cell 1: Import Libraries ───────────────────────────────
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import os
+
+# ─── Cell 2: Download Data & Set Variables ──────────────────
+# Run this in Google Colab
+# !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')
+
+# Image dimensions
+IMG_HEIGHT = 150
+IMG_WIDTH  = 150
+BATCH_SIZE = 32
+EPOCHS     = 15
+
+# ─── Cell 3: Create Image Generators ────────────────────────
+# Rescale pixel values from [0-255] to [0-1]
+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(
+    batch_size  = BATCH_SIZE,
+    directory   = train_dir,
+    target_size = (IMG_HEIGHT, IMG_WIDTH),
+    class_mode  = 'binary'
+)
+
+val_data_gen = validation_image_generator.flow_from_directory(
+    batch_size  = BATCH_SIZE,
+    directory   = validation_dir,
+    target_size = (IMG_HEIGHT, IMG_WIDTH),
+    class_mode  = 'binary'
+)
+
+test_data_gen = test_image_generator.flow_from_directory(
+    batch_size  = BATCH_SIZE,
+    directory   = test_dir,
+    target_size = (IMG_HEIGHT, IMG_WIDTH),
+    class_mode  = None,   # No labels for test set
+    shuffle     = False   # Keep order consistent for predictions
+)
+
+# ─── Cell 4: Plot Helper Function ───────────────────────────
+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()
+
+# Plot 5 sample training images
+sample_training_images, _ = next(train_data_gen)
+plotImages(sample_training_images[:5])
+
+# ─── Cell 5: Data Augmentation ──────────────────────────────
+train_image_generator = 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'
+)
+
+# ─── Cell 6: Recreate Train Generator with Augmentation ─────
+train_data_gen = train_image_generator.flow_from_directory(
+    batch_size  = BATCH_SIZE,
+    directory   = train_dir,
+    target_size = (IMG_HEIGHT, IMG_WIDTH),
+    class_mode  = 'binary'
+)
+
+# Plot a single image 5 times with different augmentations
+augmented_images = [train_data_gen[0][0][0] for _ in range(5)]
+plotImages(augmented_images)
+
+# ─── Cell 7: Build the CNN Model ────────────────────────────
+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),
+
+    Flatten(),
+    Dense(512, activation='relu'),
+    Dropout(0.5),
+    Dense(1, activation='sigmoid')   # Binary: cat=0, dog=1
+])
+
+model.compile(
+    optimizer = 'adam',
+    loss      = 'binary_crossentropy',
+    metrics   = ['accuracy']
+)
+
+model.summary()
+
+# ─── Cell 8: Train the Model ────────────────────────────────
+history = model.fit(
+    x               = train_data_gen,
+    steps_per_epoch = train_data_gen.samples // BATCH_SIZE,
+    epochs          = EPOCHS,
+    validation_data = val_data_gen,
+    validation_steps= val_data_gen.samples // BATCH_SIZE
+)
+
+# ─── Cell 9: Visualize Training History ─────────────────────
+acc     = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+loss    = history.history['loss']
+val_loss= history.history['val_loss']
+
+epochs_range = range(EPOCHS)
+
+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()
+
+# ─── Cell 10: Predict on Test Images ────────────────────────
+test_data_gen.reset()
+probabilities = model.predict(test_data_gen)
+probabilities = [p[0] for p in probabilities]   # Flatten to 1D list
+
+test_data_gen.reset()
+test_images = [test_data_gen[0][0][i] for i in range(len(test_data_gen[0][0]))]
+plotImages(test_images, probabilities=probabilities)
+
+# ─── Cell 11: Evaluate (Pass/Fail Check) ────────────────────
+# The freeCodeCamp test checks that accuracy >= 63%
+# Re-evaluate on validation set
+loss, accuracy = model.evaluate(val_data_gen)
+print(f"\nValidation Accuracy: {accuracy * 100:.2f}%")
+if accuracy >= 0.63:
+    print("✅ PASSED! You met the 63% accuracy threshold.")
+    if accuracy >= 0.70:
+        print("🌟 BONUS: You hit 70%+ accuracy!")
+else:
+    print("❌ Not yet passing. Try more epochs, augmentation, or a deeper model.")