Neural network written with keras outputs only 0

my neural network written in keras, for the problem of binary image classification, after selecting hyperparameters using the keras tuner, produces only zeros.

import keras_tuner
from kerastuner import BayesianOptimization
from keras_tuner import Objective
from tensorflow.keras.models import Model
from tensorflow.keras.applications import Xception
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
        
  
from torch.nn.modules import activation
        
def build_model(hp):
        
    # create the base pre-trained model
    base_model = Xception(include_top=False, input_shape=(224, 224, 3))
          
    base_model.trainable  = False
        
    x = base_model.output
    x = Flatten()(x)
        
    hp_units = hp.Int('units', min_value=32, max_value=4096, step=32)
    x = Dense(units = hp_units, activation="relu")(x)
          
    hp_rate = hp.Float('rate', min_value = 0.01, max_value=0.9, step=0.01)
    x = Dropout(rate = hp_rate)(x)
        
    predictions = Dense(1, activation='sigmoid')(x)
        
    # this is the model we will train
    model = Model(inputs=base_model.input, outputs=predictions)
        
    hp_learning_rate = hp.Float('learning_rate', max_value = 1e-2, min_value = 1e-7, step = 0.0005)
    optimizer = hp.Choice('optimizer', ['adam', 'sgd', 'adagrad', 'rmsprop'])
        
    model.compile(optimizer,
                  loss=tf.keras.losses.BinaryCrossentropy(),
                  metrics=['accuracy'])
        
    return model
    
stop_early = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, min_delta= 0.0001)
    
tuner = BayesianOptimization(
        hypermodel = build_model,
        objective = Objective(name="val_accuracy",direction="max"),
        max_trials = 10,
        directory='/content/best_model_s',
        overwrite=False
        )
    
tuner.search(train_batches,
             validation_data = valid_batches,
             epochs = 100,
             callbacks=[stop_early]
                 )
    
best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]
    
model = tuner.hypermodel.build(best_hps)
history = model.fit(train_batches, validation_data = valid_batches ,epochs=50)
    
val_acc_per_epoch = history.history['val_accuracy']
best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1
print('Best epoch: %d' % (best_epoch,))
    
best_model = tuner.hypermodel.build(best_hps)
    
# Retrain the model
best_model.fit(train_batches, validation_data = valid_batches , epochs=best_epoch)
    
test_generator.reset()
predict = best_model.predict_generator(test_generator, steps = len(test_generator.filenames))

I'm guessing that maybe the problem is that the ImageDataGenerator is fed to train with 2 batches of 16 images each, and to test the ImageDataGenerator with 2 batches of 4 images (each batch has an equal number of class representatives).I also noticed that with a small number of epochs, the neural network produces values ​​from 0 to 1, but the more epochs, the closer the response of the neural network is to zero. For a solution, I tried to stop training as soon as the next 5 iterations do not decrease the loss on validation. Again, it seems to me that the matter is in the validation sample, it is very small.

Any advice?