我想用Tensorflow建立一个高斯RBM模型。但该程序将使用太多的内存。张量流记忆问题
gaussian_rbm.py
import tensorflow as tf
import math
import input_data
import numpy as np
def sample_prob(probs):
return tf.nn.relu(
tf.sign(
probs - tf.random_uniform(tf.shape(probs))))
class RBM(object):
""" represents a sigmoidal rbm """
def __init__(self, name, input_size, output_size, gaussian_std_val=0.1):
with tf.name_scope("rbm_" + name):
self.weights = tf.Variable(
tf.truncated_normal([input_size, output_size],
stddev=1.0/math.sqrt(float(input_size))), name="weights")
self.v_bias = tf.Variable(tf.zeros([input_size]), name="v_bias")
self.h_bias = tf.Variable(tf.zeros([output_size]), name="h_bias")
self.input = tf.placeholder("float", shape=[None, 784])
#Gaussian
def_a = 1/(np.sqrt(2)*gaussian_std_val)
def_a = tf.constant(def_a, dtype=tf.float32)
self.a = tf.Variable(tf.ones(shape=[input_size]) * def_a,
name="a")
def propup(self, visible):
""" P(h|v) """
return tf.nn.sigmoid(tf.matmul(visible, self.weights) + self.h_bias)
def propdown(self, hidden):
""" P(v|h) """
# return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(self.weights)) + self.v_bias)
return (tf.matmul(hidden, tf.transpose(self.weights)) + self.v_bias)/(2 * (self.a * self.a))
def sample_h_given_v(self, v_sample):
""" Generate a sample from the hidden layer """
return sample_prob(self.propup(v_sample))
def sample_v_given_h(self, h_sample):
""" Generate a sample from the visible layer """
return self.sample_gaussian(self.propdown(h_sample))
def gibbs_hvh(self, h0_sample):
""" A gibbs step starting from the hidden layer """
v_sample = self.sample_v_given_h(h0_sample)
h_sample = self.sample_h_given_v(v_sample)
return [v_sample, h_sample]
def gibbs_vhv(self, v0_sample):
""" A gibbs step starting from the visible layer """
h_sample = self.sample_h_given_v(v0_sample)
v_sample = self.sample_v_given_h(h_sample)
return [h_sample, v_sample]
def sample_gaussian(self, mean_field):
return tf.random_normal(shape=tf.shape(mean_field),
mean=mean_field,
stddev=1.0/(np.sqrt(2) * self.a))
def cd1(self, learning_rate=0.1):
" One step of contrastive divergence, with Rao-Blackwellization "
h_start = self.sample_h_given_v(self.input)
v_end = self.sample_v_given_h(h_start)
h_end = self.sample_h_given_v(v_end)
w_positive_grad = tf.matmul(tf.transpose(self.input), h_start)
w_negative_grad = tf.matmul(tf.transpose(v_end), h_end)
update_w = self.weights + (learning_rate * (w_positive_grad - w_negative_grad)/tf.to_float(tf.shape(self.input)[0]))
update_vb = self.v_bias + (learning_rate * tf.reduce_mean(self.input - v_end, 0))
update_hb = self.h_bias + (learning_rate * tf.reduce_mean(h_start - h_end, 0))
return [update_w, update_vb, update_hb]
def cal_err(self):
err = self.input - self.gibbs_vhv(self.input)[1]
return tf.reduce_mean(err * err)
test_mnist.py
import tensorflow as tf
import input_data
from gaussian_RBM import RBM
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
rbm_modle = RBM(name="gaussian_rbm", input_size=784, output_size=1000)
sess = tf.Session()
init_op = tf.initialize_all_variables()
sess.run(init_op)
for i in range(100):
print "step: %s"%i
for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):
rbm_modle.weights, rbm_modle.v_bias, rbm_modle.h_bias = \
sess.run(rbm_modle.cd1(), feed_dict={rbm_modle.input : trX[start : end]})
if start % 1280 == 0:
print sess.run(rbm_modle.cal_err(), feed_dict={rbm_modle.input : teX})
输出是
运行test_mnist.py提取MNIST_data /列车图像 - 的idx3-ubyte.gz 提取MNIST_data/train-labels-idx1-ubyte.gz提取 MNIST_data/t10k-images-idx3-ubyte.g ž提取 MNIST_data/T10K-标签-IDX1-ubyte.gz我 tensorflow/stream_executor/CUDA/cuda_gpu_executor.cc:从SYSFS读900]成功 NUMA节点具有负值(-1),但必须至少是 一个NUMA节点,所以返回NUMA节点零点 tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:name:GeForce GTX 560 major:2 minor:1 memory_lockRate(GHz)1.62 pciBusID 0000:01:00.0总内存: 1018.69MiB空闲内存:916.73MiB I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA:0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:YI tensorflow/core/common_runtime/gpu/gpu_device.cc:684]忽略GPU 设备(设备:0,名称:GeForce GTX 560,PCI总线ID:0000:01:00.0) 与Cuda计算能力2.1。最低要求的Cuda能力为 。步骤:0 0.0911714 0.0781856 0.0773076 0.0770751 0.0776582 0.0764748 0.0755164 0.0741131 0.0726497 0.0712237 0.0701839 0.0686315 0.0664856 0.0658309 0.0646239 0.0626652 0.0616178 0.0610061 0.0598332 0.0588843 0.0587477 0.0572056 0.0561556 0.0554848死亡
有没有一些方法来监视内存? 有人可以帮我吗?
感谢您的帮助。我正在尝试用cpu-only tensorflow来运行此代码。但是我的程序仍然会中途死亡。 – MilkKnight