纯粹搬运工，接受英语的请看原网址：Keras Tensorflow backend automatically allocates all GPU memory。

通过设置Keras的Tensorflow后端的全局变量达到。

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))

使用过程中显示的设置session:

import keras.backend.tensorflow_backend as KTF
KTF.set_session(get_session())