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# model_builder.py
import tensorflow as tf
from tensorflow.keras import regularizers # Boa prática manter o import aqui também
# Importa as constantes do config.py
from config import LSTM_UNITS, DENSE_UNITS, DROPOUT_RATE, LEARNING_RATE, L2_REG
def build_lstm_model(input_shape): # LEARNING_RATE é pega do config.py agora
"""Constrói o modelo LSTM com regularização L2."""
model = tf.keras.Sequential()
# Camadas LSTM
for i, units in enumerate(LSTM_UNITS):
return_sequences = True if i < len(LSTM_UNITS) - 1 else False
layer_name_lstm = f'lstm_{i}' # Adicionar nomes às camadas é uma boa prática
if i == 0:
model.add(tf.keras.layers.LSTM(units,
return_sequences=return_sequences,
input_shape=input_shape,
kernel_regularizer=regularizers.l2(L2_REG),
name=layer_name_lstm
))
else:
model.add(tf.keras.layers.LSTM(units,
return_sequences=return_sequences,
kernel_regularizer=regularizers.l2(L2_REG),
name=layer_name_lstm
))
model.add(tf.keras.layers.Dropout(DROPOUT_RATE, name=f'dropout_lstm_{i}'))
# Camada Densa
model.add(tf.keras.layers.Dense(DENSE_UNITS,
activation='relu',
kernel_regularizer=regularizers.l2(L2_REG),
name='dense_main'
))
model.add(tf.keras.layers.Dropout(DROPOUT_RATE, name='dropout_dense'))
# Camada de Saída
model.add(tf.keras.layers.Dense(1, activation='sigmoid', name='output')) # CORRIGIDO: aspas simples
# O LEARNING_RATE é pego do config.py
optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE, amsgrad=True, clipvalue=1.0) # clipvalue é bom para RNNs
model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy']) # CORRIGIDO: aspas
model.summary()
return model |