使用sess.run（）時Tensorflow崩潰

Question

我在Python v2.7中使用tensorflow 0.8.0。 我的IDE是PyCharm，我的操作系統是Linux Ubuntu 14.04

我注意到以下代碼導致我的計算機凍結和/或崩潰：

# 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])

激光線導致崩潰：

print sess.run（mull [0,2]）

這基本上是一個非常大的二維陣列中的一個位置。 關於sess.run的一些事情正在引發它。 我也得到一個腳本問題彈出窗口...某種谷歌腳本（想想也許它是張量流？）。 我無法復制鏈接，因為我的計算機已完全凍結。

Answer 1

我懷疑問題出現是因為mull[0, 2]盡管其小的表觀大小 - 取決於非常大的計算，包括多個卷積，最大池和大的矩陣乘法; 因此，您的計算機要么長時間滿載，要么內存不足。 （您應該能夠通過運行top檢查哪個，並檢查運行TensorFlow的python進程使用了​​哪些資源。）

計算量非常大，因為您的TensorFlow圖是根據整個訓練數據集trainImages ，其中包含40000張圖像：

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

相反，根據tf.placeholder()來定義您的網絡會更有效，您可以tf.placeholder() 提供單個培訓示例或小批量示例。 有關更多信息，請參閱有關喂食的文檔 。 特別是，由於您只對第0行的mull感興趣，因此您只需要從trainImages提供第0個示例並對其執行計算以生成必要的值。 （在當前程序中，還會計算所有其他示例的結果，然后在最終切片運算符中將其丟棄。）

Answer 2

將會話設置為默認值，並在運行會話之前初始化變量可以解決您的問題。

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