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import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import ModelCheckpoint
from utils import load_dataset
# 1. Cargar datos
X, y = load_dataset("data")
print(f"✅ Datos cargados: {X.shape[0]} audios (formato {X.shape[1:]}")
# 2. Dividir en entrenamiento/validación
from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
# 3. Crear modelo (MobileNetV2 + fine-tuning)
base_model = MobileNetV2(
weights='imagenet',
include_top=False,
input_shape=(128, 128, 3)
)
# Congelar capas base (opcional para pocos datos)
for layer in base_model.layers:
layer.trainable = False
# Añadir capas personalizadas
x = GlobalAveragePooling2D()(base_model.output)
x = Dense(128, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x) # Salida binaria
model = Model(inputs=base_model.input, outputs=predictions)
# 4. Compilar (énfasis en Recall)
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy', tf.keras.metrics.Recall()]
)
# 5. Callbacks (guardar mejor modelo)
checkpoint = ModelCheckpoint(
"model.h5",
monitor='val_recall', # Priorizar recall en validación
mode='max',
save_best_only=True,
verbose=1
)
# 6. Entrenar
history = model.fit(
X_train, y_train,
epochs=15,
batch_size=32,
validation_data=(X_val, y_val),
callbacks=[checkpoint],
class_weight={0: 1, 1: 4} # Ajustar si hay desbalance
)
print("✅ Entrenamiento completado. Modelo guardado como 'model.h5'") |