我试图使张量流教程模型适应svhn住房数据。
当我执行时我得到OOM错误，尽管我的卡应该有足够的（8gb）
我的代码中必须有一些结构错误，这会导致它重载。
import os.path
import urllib
import numpy as np
import scipy.io as scp
testfile = urllib.URLopener()
testfile2=urllib.URLopener()
if not os.path.isfile("test.mat"):
testfile.retrieve("http://ufldl.stanford.edu/housenumbers/test_32x32.mat", "test.mat")
if not os.path.isfile("train.mat"):
testfile.retrieve("http://ufldl.stanford.edu/housenumbers/train_32x32.mat", "train.mat")
testdata=scp.loadmat('test.mat')
traindata=scp.loadmat('train.mat')
trainDataX = traindata['X'].astype('float32') /256
testDataX = testdata['X'].astype('float32') / 256
trainDataY = traindata['y']
testDataY = testdata['y']
def OnehotEncoding(Y):
Ytr=[]
for el in Y:
temp=np.zeros(10)
if el==10:
temp[0]=1
elif el==1:
temp[1]=1
elif el==2:
temp[2]=1
elif el==3:
temp[3]=1
elif el==4:
temp[4]=1
elif el==5:
temp[5]=1
elif el==6:
temp[6]=1
elif el==7:
temp[7]=1
elif el==8:
temp[8]=1
elif el==9:
temp[9]=1
Ytr.append(temp)
return np.asarray(Ytr)
trainDataY = OnehotEncoding(trainDataY)
testDataY = OnehotEncoding(testDataY)
def transposeArray(data):
print 'started'
xtrain = []
trainLen = data.shape[3]
print trainLen
for x in xrange(trainLen):
xtrain.append(data[:,:,:,x])
xtrain = np.asarray(xtrain)
return xtrain
trainDataX = transposeArray(trainDataX)
testDataX = transposeArray(testDataX)
print trainDataX.shape
import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 32,32,3])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 3, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,32,32,3])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
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)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([8 * 8* 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
#y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
logits = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
y_conv = tf.nn.softmax(logits)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, y_))
#cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#print logits
with tf.Session() as sess:
#epoch=20000
epoch=0
batch_size=5
sess.run(tf.initialize_all_variables())
p = np.random.permutation(range(len(trainDataX)))
trX, trY = trainDataX[p], trainDataY[p]
print len(trainDataX)
start = 0
end = 0
for step in range(epoch):
start = end
end = start + batch_size
if start >= len(trainDataX):
start = 0
end = start + batch_size
if end >= len(trainDataX):
end = len(trainDataX) - 1
inX, outY = trX[start:end], trY[start:end]
#sess.run(optimizer, feed_dict= {x: inX, y_: outY, keep_prob:0.75})
train_step.run(feed_dict={x: inX, y_: outY, keep_prob: 0.5})
if step % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={x: inX, y_: outY, keep_prob:1})
print 'cost at each step :', step, 'is :', sess.run(cross_entropy, feed_dict={x: inX, y_: outY, keep_prob:1.0})
print("step %d, training accuracy %g"%(step, train_accuracy))
print("test accuracy %g"%accuracy.eval(feed_dict={
x: testDataX, y_:testDataY , keep_prob: 1.0}))
我收到一条很长的错误信息，其中一部分是：
W tensorflow/core/common_runtime/executor.cc:1102] 0x29bf8c0 Compute status: Resource exhausted: OOM when allocating tensor with shape[26032,32,32,32]
[[Node: Conv2D = Conv2D[T=DT_FLOAT, padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Variable/read)]]
W tensorflow/core/common_runtime/executor.cc:1102] 0x29e96b0 Compute status: Resource exhausted: OOM when allocating tensor with shape[26032,32,32,32]
[[Node: Conv2D = Conv2D[T=DT_FLOAT, padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Variable/read)]]
[[Node: range_1/_10 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_525_range_1", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]