Video Summary — CNN Implementation on Fashion-MNIST (Arabic lecture) 🧠🧵

Overview

• Course: Final stage — implementing a CNN image classifier in Python using TensorFlow/Keras on the Fashion-MNIST dataset.
• Dataset: 70,000 grayscale images, 28×28 px, 10 classes (0–9). Typical split: 60,000 train, 10,000 test; in this session they use 55,000 train + 10,000 validation + 5,000? (adjusted explained).
• Goal: load data, preprocess, build CNN, train, save/restore model, predict and evaluate.

Libraries & setup

• Import: tensorflow as tf, from tensorflow import keras, numpy as np, matplotlib.pyplot as plt, pandas as pd (for history plotting).
• Check TensorFlow and Keras versions.

Data loading & preprocessing

• Load dataset:
  • (x_train_full, y_train_full), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
• Reshape inputs to 4D tensors:
  • x_train_full = x_train_full.reshape((-1, 28, 28, 1))
  • x_test = x_test.reshape((-1, 28, 28, 1))
• Data type and pixel range:
  • dtype: uint8 (0–255). Scale to [0,1] by dividing by 255.0.
• Create validation set:
  • Example in lecture: take first 10,000 from training as validation.
  • x_val = x_train_full[:10000] / 255.0
  • x_train = x_train_full[10000:] / 255.0
  • y_val = y_train_full[:10000]; y_train = y_train_full[10000:]
• Print shapes / lengths to confirm splits.

Visualize samples

• Plot single test image:
  • plt.figure(); plt.imshow(x_test[0].squeeze(), cmap='gray'); plt.show()
• Class names mapping:
  • 0: T-shirt/top, 1: Trouser, 2: Pullover, 3: Dress, 4: Coat, 5: Sandal, 6: Shirt, 7: Sneaker, 8: Bag, 9: Ankle boot
• Plot a 5×5 grid of 25 images with titles showing class names.

CNN model architecture (implemented with Keras Sequential)

• Utility: define a partial function for default Conv2D args:
  • kernel_size=3, activation='relu', padding='same' (padding='same' used? lecture mentions "pad = same" or zero-padding earlier)
• Example architecture used:
  • Conv2D(32, kernel_size=3, activation='relu', input_shape=(28,28,1))
  • MaxPooling2D(pool_size=2)
  • Conv2D(64, kernel_size=3, activation='relu')
  • Conv2D(128, kernel_size=3, activation='relu')
  • MaxPooling2D(pool_size=2)
  • (they doubled filters: 32 → 64 → 128)
  • Flatten()
  • Dense(64, activation='relu')
  • Dropout(0.5) # lecture used 50%
  • Dense(10, activation='softmax')
• Notes:
  • ReLU after conv layers.
  • Max pooling reduces spatial dimensions progressively.
  • Filter counts typically doubled each conv block.
  • Last Dense gives class probabilities (softmax).

Compile & train

• Compile:
  • loss = 'sparse_categorical_crossentropy'
  • optimizer = 'adam' (lecture used Adam; SGD could be tried)
  • metrics = ['accuracy']
• Train (example values):
  • history = model.fit(x_train, y_train, epochs=3, validation_data=(x_val, y_val))
  • Lecture used epochs=3 for demo (recommended to use more, e.g., 20).
• After training: evaluate on test set:
  • test_loss, test_acc = model.evaluate(x_test, y_test)

Save & load model

• Save:
  • model.save('path/to/model.h5', save_format='h5')
• Load:
  • model = keras.models.load_model('path/to/model.h5')

Predictions & inspection

• Predict on validation or test:
  • y_pred_probs = model.predict(x_val)
  • y_pred = np.argmax(y_pred_probs, axis=1)
• Compare sample predictions to true labels:
  • print(y_pred[:7], y_val[:7])
• Lecture showed some mismatches and mentioned ~86% accuracy with chosen settings.

Tips to improve performance

• Increase epochs (lecture suggested e.g., 20).
• Reduce dropout rate (less than 0.5) or remove if unnecessary.
• Use more training data / augmentation.
• Adjust network depth/filters or use different optimizers/hyperparameters.

Recap of learning path (context)

• Course covered: basics (math, ML), artificial neural networks, optimizers (SGD, Adam, RMSProp), hyperparameters (epochs, batch size), image classifiers with MLP, normalization/regularization/data augmentation, then deep learning with CNNs and architecture rationale (feature maps, pooling, flattening), ending with this implementation.

Key code snippets (conceptual)

• Load & reshape:
  • keras.datasets.fashion_mnist.load_data()
  • reshape to (-1,28,28,1)
  • normalize: /255.0
• Model building: sequential conv → pool blocks → flatten → dense → dropout → output
• Compile: sparse_categorical_crossentropy, Adam, accuracy
• Fit: model.fit(..., validation_data=(x_val,y_val), epochs=N)
• Save/load: model.save(...), keras.models.load_model(...)

Final notes ✅

• The lecture demonstrates a full CNN workflow: load, preprocess, visualize, build, train, evaluate, save/restore, predict.
• For production-quality results: use more epochs, tune dropout, consider data augmentation and architecture tuning.