Training Neural Networks với TensorFlow/Keras

1import tensorflow as tf
2print(f"TensorFlow version: {tf.__version__}")
3print(f"Keras version: {tf.keras.__version__}")
4print(f"GPU available: {tf.config.list_physical_devices('GPU')}")

1from tensorflow import keras
2from tensorflow.keras import layers
3
4model = keras.Sequential([
5    layers.Dense(64, activation='relu', input_shape=(10,)),
6    layers.Dense(32, activation='relu'),
7    layers.Dense(1, activation='sigmoid')
8])

1model = keras.Sequential()
2model.add(layers.Dense(64, activation='relu', input_shape=(10,)))
3model.add(layers.Dense(32, activation='relu'))
4model.add(layers.Dense(1, activation='sigmoid'))

1model.summary()

1model.compile(
2    optimizer='adam',              # Thuật toán tối ưu
3    loss='binary_crossentropy',    # Hàm loss
4    metrics=['accuracy']           # Metrics theo dõi
5)

1from tensorflow.keras.optimizers import Adam
2
3optimizer = Adam(learning_rate=0.001)  # Default
4optimizer = Adam(learning_rate=0.0001) # Nhỏ hơn nếu unstable
5
6model.compile(optimizer=optimizer, loss='binary_crossentropy')

1history = model.fit(
2    X_train, y_train,
3    epochs=50,                     # Số vòng lặp
4    batch_size=32,                 # Kích thước batch
5    validation_split=0.2,          # 20% data cho validation
6    verbose=1                      # Hiển thị progress
7)

1from tensorflow.keras.callbacks import EarlyStopping
2
3early_stop = EarlyStopping(
4    monitor='val_loss',           # Theo dõi val_loss
5    patience=10,                  # Chờ 10 epochs
6    restore_best_weights=True,    # Khôi phục weights tốt nhất
7    verbose=1
8)

1from tensorflow.keras.callbacks import ModelCheckpoint
2
3checkpoint = ModelCheckpoint(
4    'best_model.keras',           # Tên file
5    monitor='val_accuracy',       # Theo dõi val_accuracy
6    save_best_only=True           # Chỉ lưu khi tốt hơn
7)

1from tensorflow.keras.callbacks import ReduceLROnPlateau
2
3reduce_lr = ReduceLROnPlateau(
4    monitor='val_loss',
5    factor=0.2,                   # Giảm còn 20%
6    patience=5
7)

1history = model.fit(
2    X_train, y_train,
3    epochs=100,
4    validation_split=0.2,
5    callbacks=[early_stop, checkpoint, reduce_lr]
6)

1# Đánh giá model
2test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)
3
4print(f"Test Loss: {test_loss:.4f}")
5print(f"Test Accuracy: {test_accuracy:.4f}")

1# Predict xác suất
2y_pred_proba = model.predict(X_test)
3
4# Chuyển thành nhãn 0/1 (binary)
5y_pred = (y_pred_proba > 0.5).astype(int)
6
7# Cho multi-class
8y_pred = np.argmax(y_pred_proba, axis=1)

1import matplotlib.pyplot as plt
2
3fig, axes = plt.subplots(1, 2, figsize=(14, 5))
4
5# Loss
6axes[0].plot(history.history['loss'], label='Training')
7axes[0].plot(history.history['val_loss'], label='Validation')
8axes[0].set_title('Model Loss')
9axes[0].legend()
10
11# Accuracy
12axes[1].plot(history.history['accuracy'], label='Training')
13axes[1].plot(history.history['val_accuracy'], label='Validation')
14axes[1].set_title('Model Accuracy')
15axes[1].legend()
16
17plt.show()

1import numpy as np
2import tensorflow as tf
3from tensorflow import keras
4from tensorflow.keras import layers
5from tensorflow.keras.callbacks import EarlyStopping
6from sklearn.datasets import make_classification
7from sklearn.model_selection import train_test_split
8from sklearn.preprocessing import StandardScaler
9from sklearn.metrics import classification_report
10
11# 1. Tạo dữ liệu
12X, y = make_classification(
13    n_samples=5000,
14    n_features=10,
15    n_classes=2,
16    weights=[0.7, 0.3],
17    random_state=42
18)
19
20# 2. Chia train/test
21X_train, X_test, y_train, y_test = train_test_split(
22    X, y, test_size=0.2, random_state=42, stratify=y
23)
24
25# 3. Chuẩn hóa
26scaler = StandardScaler()
27X_train = scaler.fit_transform(X_train)
28X_test = scaler.transform(X_test)
29
30# 4. Xây dựng model
31model = keras.Sequential([
32    layers.Dense(64, activation='relu', input_shape=(10,)),
33    layers.BatchNormalization(),
34    layers.Dropout(0.3),
35    
36    layers.Dense(32, activation='relu'),
37    layers.BatchNormalization(),
38    layers.Dropout(0.2),
39    
40    layers.Dense(16, activation='relu'),
41    layers.Dense(1, activation='sigmoid')
42])
43
44# 5. Compile
45model.compile(
46    optimizer=keras.optimizers.Adam(learning_rate=0.001),
47    loss='binary_crossentropy',
48    metrics=['accuracy']
49)
50
51# 6. Train
52early_stop = EarlyStopping(
53    monitor='val_loss',
54    patience=10,
55    restore_best_weights=True
56)
57
58history = model.fit(
59    X_train, y_train,
60    epochs=100,
61    batch_size=32,
62    validation_split=0.2,
63    callbacks=[early_stop],
64    verbose=1
65)
66
67# 7. Evaluate
68test_loss, test_acc = model.evaluate(X_test, y_test)
69print(f"\nTest Accuracy: {test_acc:.4f}")
70
71# 8. Predict và Report
72y_pred = (model.predict(X_test) > 0.5).astype(int)
73print("\nClassification Report:")
74print(classification_report(y_test, y_pred))