2016年6月的git问题（https://github.com/tensorflow/tensorflow/issues/1727）表示存在以下问题：
当前，GPUDevice中的分配器属于ProcessState，它本质上是全局单例。使用GPU的第一个会话将其初始化，并在进程关闭时释放自身。
因此，唯一的解决方法是使用进程并在计算后关闭它们。
示例代码：
import tensorflow as tfimport multiprocessingimport numpy as npdef run_tensorflow(): n_input = 10000 n_classes = 1000 # Create model def multilayer_perceptron(x, weight): # Hidden layer with RELU activation layer_1 = tf.matmul(x, weight) return layer_1 # Store layers weight & bias weights = tf.Variable(tf.random_normal([n_input, n_classes])) x = tf.placeholder("float", [None, n_input]) y = tf.placeholder("float", [None, n_classes]) pred = multilayer_perceptron(x, weights) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for i in range(100): batch_x = np.random.rand(10, 10000) batch_y = np.random.rand(10, 1000) sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) print "finished doing stuff with tensorflow!"if __name__ == "__main__": # option 1: execute pre with extra process p = multiprocessing.Process(target=run_tensorflow) p.start() p.join() # wait until user presses enter key raw_input() # option 2: just execute the function run_tensorflow() # wait until user presses enter key raw_input()因此，如果要
run_tensorflow()在创建的进程中调用该函数并关闭该进程（选项1），则会释放内存。如果仅运行
run_tensorflow()（选项2），则函数调用后不会释放内存。
