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import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.layers import (
BatchNormalization, Conv2D, Dense, Dropout,
GlobalAveragePooling2D, MaxPooling2D, RandomRotation, RandomTranslation, RandomZoom
)
from tensorflow.keras.models import Sequential
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
PROCESSED_DIR = os.path.join(ROOT_DIR, 'dataset', 'processed')
MODEL_DIR = os.path.join(ROOT_DIR, 'model')
# Use all CPU cores
tf.config.threading.set_intra_op_parallelism_threads(12)
tf.config.threading.set_inter_op_parallelism_threads(12)
os.makedirs(MODEL_DIR, exist_ok=True)
X = np.load(os.path.join(PROCESSED_DIR, 'X_train.npy'))
y = np.load(os.path.join(PROCESSED_DIR, 'y_train.npy'))
X_val = np.load(os.path.join(PROCESSED_DIR, 'X_val.npy'))
y_val = np.load(os.path.join(PROCESSED_DIR, 'y_val.npy'))
print(f"Data range: {X.min():.3f} to {X.max():.3f}")
print(f"Data shape: {X.shape}")
print(f"Data type: {X.dtype}")
with open(os.path.join(MODEL_DIR, 'classes.json'), "r", encoding="utf-8") as f:
class_mapping = json.load(f)
num_classes = len(class_mapping)
X_train, y_train = X, y
def build_model(input_shape=(28, 28, 1), n_classes=20):
net = Sequential([
tf.keras.layers.Input(shape=input_shape),
# Data augmentation - active only during training, disabled at inference
RandomRotation(0.15),
RandomTranslation(0.1, 0.1),
RandomZoom(0.1),
# 28x28
Conv2D(32, (3, 3), padding='same', activation='relu', kernel_initializer='he_normal'),
BatchNormalization(),
MaxPooling2D(),
Dropout(0.25),
# 14x14
Conv2D(64, (3, 3), padding='same', activation='relu', kernel_initializer='he_normal'),
BatchNormalization(),
MaxPooling2D(),
Dropout(0.3),
# 7x7
Conv2D(128, (3, 3), padding='same', activation='relu', kernel_initializer='he_normal'),
BatchNormalization(),
MaxPooling2D(),
Dropout(0.3),
# 3x3
Conv2D(256, (3, 3), padding='same', activation='relu', kernel_initializer='he_normal'),
BatchNormalization(),
GlobalAveragePooling2D(),
Dropout(0.4),
Dense(256, activation='relu', kernel_initializer='he_normal'),
Dropout(0.4),
Dense(128, activation='relu', kernel_initializer='he_normal'),
Dropout(0.3),
Dense(n_classes, activation='softmax')
])
return net
model = build_model(input_shape=(28, 28, 1), n_classes=num_classes)
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
model.summary()
callbacks = [
ModelCheckpoint(
os.path.join(MODEL_DIR, 'best_model.keras'),
save_best_only=True,
monitor="val_accuracy",
mode="max",
verbose=1
),
EarlyStopping(monitor="val_loss", patience=15, restore_best_weights=True, verbose=1),
ReduceLROnPlateau(monitor="val_loss", factor=0.5, patience=7, min_lr=1e-6, verbose=1)
]
print("Starting training...")
history = model.fit(
X_train, y_train,
validation_data=(X_val, y_val),
batch_size=256,
epochs=80,
callbacks=callbacks,
verbose=1
)
final_val_acc = max(history.history['val_accuracy'])
print(f"Best validation accuracy: {final_val_acc:.4f} ({final_val_acc * 100:.2f}%)")
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