Tensorflow崩溃时使用sess.run()

Question

我在pythonv2.7中使用tensorflow 0.8.0。我的IDE是PyCharm，操作系统是Linux ubuntu14.04

我注意到以下代码导致我的计算机冻结和/或崩溃：

# you will need these files!
# https://www.kaggle.com/c/digit-recognizer/download/train.csv
# https://www.kaggle.com/c/digit-recognizer/download/test.csv

import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.cm as cm

# read in the image data from the csv file
# the format is:    imagelabel  pixel0  pixel1 ... pixel783  (there are 42,000 rows like this)
data = pd.read_csv('../train.csv')
labels = data.iloc[:,:1].values.ravel()  # shape = (42000, 1)
labels_count = np.unique(labels).shape[0]  # = 10
images = data.iloc[:,1:].values   # shape = (42000, 784)
images = images.astype(np.float64)
image_size = images.shape[1]
image_width = image_height = np.sqrt(image_size).astype(np.int32)  # since these images are sqaure... hieght = width


# turn all the gray-pixel image-values into percentages of 255
# a 1.0 means a pixel is 100% black, and 0.0 would be a pixel that is 0% black (or white)
images = np.multiply(images, 1.0/255)


# create oneHot vectors from the label #s
oneHots = tf.one_hot(labels, labels_count, 1, 0)  #shape = (42000, 10)


#split up the training data even more (into validation and train subsets)
VALIDATION_SIZE = 3167

validationImages = images[:VALIDATION_SIZE]
validationLabels = labels[:VALIDATION_SIZE]

trainImages = images[VALIDATION_SIZE:]
trainLabels = labels[VALIDATION_SIZE:]






# -------------  Building the NN -----------------

# set up our weights (or kernals?) and biases for each pixel
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(.1, shape=shape, dtype=tf.float32)
    return tf.Variable(initial)


# convolution
def conv2d(x, W):
    return tf.nn.conv2d(x, W, [1,1,1,1], 'SAME')

# pooling
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# placeholder variables
# images
x = tf.placeholder('float', shape=[None, image_size])
# labels
y_ = tf.placeholder('float', shape=[None, labels_count])



# first convolutional layer
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

# turn shape(40000,784)  into   (40000,28,28,1)
image = tf.reshape(trainImages, [-1,image_width , image_height,1])
image = tf.cast(image, tf.float32)
# print (image.get_shape()) # =>(40000,28,28,1)




h_conv1 = tf.nn.relu(conv2d(image, W_conv1) + b_conv1)
# print (h_conv1.get_shape()) # => (40000, 28, 28, 32)
h_pool1 = max_pool_2x2(h_conv1)
# print (h_pool1.get_shape()) # => (40000, 14, 14, 32)





# second convolutional layer
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
#print (h_conv2.get_shape()) # => (40000, 14,14, 64)
h_pool2 = max_pool_2x2(h_conv2)
#print (h_pool2.get_shape()) # => (40000, 7, 7, 64)




# densely connected layer
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

# (40000, 7, 7, 64) => (40000, 3136)
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
#print (h_fc1.get_shape()) # => (40000, 1024)





# dropout
keep_prob = tf.placeholder('float')
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
print h_fc1_drop.get_shape()


#readout layer for deep neural net
W_fc2 = weight_variable([1024,labels_count])
b_fc2 = bias_variable([labels_count])
print b_fc2.get_shape()
mull= tf.matmul(h_fc1_drop, W_fc2)
print mull.get_shape()
print
mull2 = mull + b_fc2
print mull2.get_shape()

y = tf.nn.softmax(mull2)



# dropout
keep_prob = tf.placeholder('float')
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)


sess = tf.Session()
sess.run(tf.initialize_all_variables())

print sess.run(mull[0,2])

激光线导致崩溃：

打印sess.运行（毛重[0,2]）

这基本上是一个非常大的二维阵列中的一个位置。关于sess.运行是造成它的原因。我也得到一个脚本问题弹出。。。某种谷歌脚本（想想可能是tensorflow？）。我不能复制链接，因为我的电脑完全冻结了。在

Answer 1

将会话设置为默认值，并在运行会话之前初始化变量可以解决问题。在

import tensorflow as tf

sess = tf.Session()
g = tf.ones([25088])

sess.as_default():
    tf.initialize_all_variables().run()
    results = sess.run(g)

    print results

Answer 2

我怀疑问题的出现是因为mull[0, 2]-尽管它的表观大小很小，但它依赖于一个非常大的计算，包括多重卷积、最大池和一个大的矩阵乘法；因此，要么你的计算机长时间处于满负荷状态，要么内存不足。（您应该能够通过运行top并检查运行TensorFlow的python进程使用了哪些资源来判断是哪个。）

计算量如此之大，因为张量流图是根据整个训练数据集trainImages定义的，该数据集包含40000幅图像：

image = tf.reshape(trainImages, [-1,image_width , image_height,1])
image = tf.cast(image, tf.float32)

相反，用一个tf.placeholder()来定义你的网络会更有效，你可以把单个的训练例子，或者小批量的例子输入其中。有关详细信息，请参阅documentation on feeding。特别是，由于您只对mull的第0行感兴趣，所以您只需要从trainImages输入第0个示例并对其执行计算以生成所需的值。（在当前程序中，所有其他示例的结果也将被计算，然后在最终的“切片”操作符中丢弃。）