I have a memory leak with TensorFlow. I referred to Tensorflow: memory leak even when closing a session? to solve my problem, and I followed the advice of an answer that seemed to solve the problem. However, this does not work here.
To recreate a memory leak, I created a simple example. Firstly, I use this function (which I got here: How to find out the current processor and RAM usage in Python? ) To check the memory usage of the python process:
def memory():
import os
import psutil
pid = os.getpid()
py = psutil.Process(pid)
memoryUse = py.memory_info()[0]/2.**30
print('memory use:', memoryUse)
Then, every time I build_modelfunction build_model, memory usage increases.
Here is build_modela memory leak function :
def build_model():
'''Model'''
tf.reset_default_graph()
with tf.Graph().as_default(), tf.Session() as sess:
tf.contrib.keras.backend.set_session(sess)
labels = tf.placeholder(tf.float32, shape=(None, 1))
input = tf.placeholder(tf.float32, shape=(None, 1))
x = tf.contrib.keras.layers.Dense(30, activation='relu', name='dense1')(input)
x1 = tf.contrib.keras.layers.Dropout(0.5)(x)
x2 = tf.contrib.keras.layers.Dense(30, activation='relu', name='dense2')(x1)
y = tf.contrib.keras.layers.Dense(1, activation='sigmoid', name='dense3')(x2)
loss = tf.reduce_mean(tf.contrib.keras.losses.binary_crossentropy(labels, y))
train_step = tf.train.AdamOptimizer(0.004).minimize(loss)
init_op = tf.global_variables_initializer()
sess.run(init_op)
sess.close()
tf.reset_default_graph()
return
, with tf.Graph().as_default(), tf.Session() as sess: tf.reset_default_graph , TensorFlow. .
:
memory()
build_model()
memory()
build_model()
memory()
( ):
memory use: 0.1794891357421875
memory use: 0.184417724609375
memory use: 0.18923568725585938
, , TensorFlow, . ?
100 build_model, :

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