#adjust learning rate policy by callbacks
def scheduler(epoch):
    if epoch == 5:
        model.lr.set_value(.02)
    return model.lr.get_value()

change_lr = LearningRateScheduler(scheduler)

model.fit(x_embed, y, nb_epoch=1, batch_size = batch_size, show_accuracy=True,
       callbacks=[chage_lr])

#adapt gpu memory usage when using tensorflow as backend
import os
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF

def get_session(gpu_fraction=0.3):
    '''Assume that you have 6GB of GPU memory and want to allocate ~2GB'''

    num_threads = os.environ.get('OMP_NUM_THREADS')
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)

    if num_threads:
        return tf.Session(config=tf.ConfigProto(
            gpu_options=gpu_options, intra_op_parallelism_threads=num_threads))
    else:
        return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

KTF.set_session(get_session())