tensorflow实现多层感知机，出现内存泄漏！

Question

 # coding: UTF-8
# TensorFlow实现Softmax Regression识别手写数字（多层感知机）
import tensorflow as tf

########加载数据集########
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

sess = tf.InteractiveSession()
in_units = 784
h1_units = 300

w1 = tf.Variable(tf.truncated_normal([in_units,h1_units],stddev=0.1))

b1 = tf.Variable(tf.zeros([h1_units]))

w2 = tf.Variable(tf.zeros([h1_units,10]))
b2 = tf.Variable(tf.zeros([10]))

x = tf.placeholder(tf.float32,[None,in_units])
keep_prob = tf.placeholder(tf.float32)

########定义模型结构########
hidden1 = tf.nn.relu(tf.matmul(x,w1)+b1)
hidden1_drop = tf.nn.dropout(hidden1,keep_prob)
y = tf.nn.softmax(tf.matmul(hidden1_drop,w2) + b2)

########定义损失函数和选择优化器来优化loss########
y_ = tf.placeholder(tf.float32,[None,10]) 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y),reduction_indices = [1]))
train_step = tf.train.AdagradOptimizer(0,3).minimize(cross_entropy)

tf.global_variables_initializer().run()
for i in range(3000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    train_step.run({x: batch_xs, y_: batch_ys,keep_prob:0.75})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))  
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))

Answer 1

哥们你代码里重复编译了