Python 尝试使用MNIST进行训练时出现ResourceExhausterRor
当我试图训练MNIST时，我感到资源枯竭 我发现我可以更改批处理大小以避免出现问题，但不幸的是，我不知道在我的代码中该在哪里执行该操作 回溯： ResourceExhustederRor（回溯见上文）：当使用形状[238305,32,28,28]和类型float on/job:localhost/replica:0/task:0/device:GPU:0分配程序GPU\U bfc分配张量时，OOM [[Node:Conv2D=Conv2D[T=DT\u FLOAT，data\u format=“NCHW”，dillations=[1,1,1]，padding=“SAME”，strips=[1,1,1]，在\u gpu=true上使用\u cudnn\u，\u device=“/job:localhost/replica:0/task:0/device:gpu:0”]（Conv2D-0-transpsenhwctonchw-layoutuoptimizer，Variable/read）]] 提示：如果您想在OOM发生时查看已分配的张量列表，请在OOM上添加report_tensor_allocations_on_to RunOptions以获取当前分配信息。 [[Node:add_3/\u 39=\u Recvclient_terminated=false，recv_device=“/job:localhost/replica:0/task:0/device:CPU:0”，send_device=“/job:localhost/replica:0/task:0/device:GPU:0”，send_device_化身=1，tensor_name=“edge_51_add_3”，tensor_type=DT_FLOAT，\u device=“/job:localhost/replica:0/task:0/device:CPU:0”] 我的CNN模型：Python 尝试使用MNIST进行训练时出现ResourceExhausterRor,python,tensorflow,machine-learning,deep-learning,mnist,Python,Tensorflow,Machine Learning,Deep Learning,Mnist,当我试图训练MNIST时，我感到资源枯竭 我发现我可以更改批处理大小以避免出现问题，但不幸的是，我不知道在我的代码中该在哪里执行该操作 回溯： ResourceExhustederRor（回溯见上文）：当使用形状[238305,32,28,28]和类型float on/job:localhost/replica:0/task:0/device:GPU:0分配程序GPU\U bfc分配张量时，OOM [[Node:Conv2D=Conv2D[T=DT\u FLOAT，data\u format=“
from __future__ import print_function
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# number 1 to 10 data
# from tensorflow.examples.tutorials.mnist import input_data
# mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)
import mnist2.mnist as mn
mnist = mn.read_data_sets('MNIST/', one_hot=True, num_classes=9)
def compute_accuracy(v_xs, v_ys):
global prediction
y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
return result
def weight_variable(shape):
inital = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(inital)
def bias_variable(shape):
inital = tf.constant(0.1, shape=shape)
return tf.Variable(inital)
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')
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784, 1]) # 28x28
ys = tf.placeholder(tf.float32, [None, 9])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])
## conv1 layer ##
W_conv1 = weight_variable([5, 5, 1, 32]) #patch 5x5, in channel size 1, out size 32
## pool1 layer ##
b_conv1 = bias_variable([32])
#Combine
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) #output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1) #output size 14x14x32
## conv2 layer ##
W_conv2 = weight_variable([5, 5, 32, 64]) #patch 5x5, in channel size 32, out size 64
## pool2 layer ##
b_conv2 = bias_variable([64])
#Combine
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) #output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2) #output size 7x7x64
## fc1 layer ##
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) #[n_samples, 7,7,64] => [n_samples, 7*7*64]
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
## output layer ##
W_fc2 = weight_variable([1024, 9])
b_fc2 = bias_variable([9])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
for i in range(10001):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob:0.5})
if i % 50 == 0:
print(compute_accuracy(
mnist.test.images, mnist.test.labels))
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import numpy
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import random_seed
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
def _read32(bytestream):
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(f, channels=1):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].
Args:
f: A file object that can be passed into a gzip reader.
Returns:
data: A 4D uint8 numpy array [index, y, x, depth].
Raises:
ValueError: If the bytestream does not start with 2051.
"""
print('Extracting', f.name)
with gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError('Invalid magic number %d in MNIST image file: %s' %
(magic, f.name))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images * channels)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, channels)
return data
def dense_to_one_hot(labels_dense, num_classes):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = numpy.arange(num_labels) * num_classes
labels_one_hot = numpy.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
def extract_labels(f, one_hot=False, num_classes=10):
"""Extract the labels into a 1D uint8 numpy array [index].
Args:
f: A file object that can be passed into a gzip reader.
one_hot: Does one hot encoding for the result.
num_classes: Number of classes for the one hot encoding.
Returns:
labels: a 1D uint8 numpy array.
Raises:
ValueError: If the bystream doesn't start with 2049.
"""
print('Extracting', f.name)
with gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2049:
raise ValueError('Invalid magic number %d in MNIST label file: %s' %
(magic, f.name))
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
if one_hot:
return dense_to_one_hot(labels, num_classes)
return labels
class DataSet(object):
def __init__(self,
images,
labels,
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
seed=None):
"""Construct a DataSet.
one_hot arg is used only if fake_data is true. `dtype` can be either
`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
`[0, 1]`. Seed arg provides for convenient deterministic testing.
"""
seed1, seed2 = random_seed.get_seed(seed)
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seed1 if seed is None else seed2)
dtype = dtypes.as_dtype(dtype).base_dtype
if dtype not in (dtypes.uint8, dtypes.float32):
raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
dtype)
if fake_data:
self._num_examples = 10000
self.one_hot = one_hot
else:
assert images.shape[0] == labels.shape[0], (
'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
#iassert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2], images.shape[3])
if dtype == dtypes.float32:
# Convert from [0, 255] -> [0.0, 1.0].
num, dim, channels = images.shape
count = num * dim * channels
images.setflags(write=1)
index = 1000
images = images.astype(numpy.float32)
for i in range(0,count,index):
if not (count - i) < index:
images[i:i+index] = numpy.multiply(images[i:i+index], 1.0 / 255.0)
else:
images[i:count] = numpy.multiply(images[i:count], 1.0 / 255.0)
#images = images.astype(numpy.float32)
#images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0
@property
def images(self):
return self._images
@property
def labels(self):
return self._labels
@property
def num_examples(self):
return self._num_examples
@property
def epochs_completed(self):
return self._epochs_completed
def next_batch(self, batch_size, fake_data=False, shuffle=True):
"""Return the next `batch_size` examples from this data set."""
if fake_data:
fake_image = [1] * 784
if self.one_hot:
fake_label = [1] + [0] * 9
else:
fake_label = 0
return [fake_image for _ in xrange(batch_size)], [
fake_label for _ in xrange(batch_size)
]
start = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
perm0 = numpy.arange(self._num_examples)
numpy.random.shuffle(perm0)
self._images = self.images[perm0]
self._labels = self.labels[perm0]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
rest_num_examples = self._num_examples - start
images_rest_part = self._images[start:self._num_examples]
labels_rest_part = self._labels[start:self._num_examples]
# Shuffle the data
if shuffle:
perm = numpy.arange(self._num_examples)
numpy.random.shuffle(perm)
self._images = self.images[perm]
self._labels = self.labels[perm]
# Start next epoch
start = 0
self._index_in_epoch = batch_size - rest_num_examples
end = self._index_in_epoch
images_new_part = self._images[start:end]
labels_new_part = self._labels[start:end]
return numpy.concatenate((images_rest_part, images_new_part), axis=0) , numpy.concatenate((labels_rest_part, labels_new_part), axis=0)
else:
self._index_in_epoch += batch_size
end = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir,
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
validation_size=24,
seed=None,
num_classes=10,
channels=1):
if fake_data:
def fake():
return DataSet(
[], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed)
train = fake()
validation = fake()
test = fake()
return base.Datasets(train=train, validation=validation, test=test)
TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
local_file = base.maybe_download(TRAIN_IMAGES, train_dir,
SOURCE_URL + TRAIN_IMAGES)
with open(local_file, 'rb') as f:
train_images = extract_images(f, channels)
local_file = base.maybe_download(TRAIN_LABELS, train_dir,
SOURCE_URL + TRAIN_LABELS)
with open(local_file, 'rb') as f:
train_labels = extract_labels(f, one_hot=one_hot, num_classes=num_classes)
local_file = base.maybe_download(TEST_IMAGES, train_dir,
SOURCE_URL + TEST_IMAGES)
with open(local_file, 'rb') as f:
test_images = extract_images(f, channels)
local_file = base.maybe_download(TEST_LABELS, train_dir,
SOURCE_URL + TEST_LABELS)
with open(local_file, 'rb') as f:
test_labels = extract_labels(f, one_hot=one_hot, num_classes=num_classes)
if not 0 <= validation_size <= len(train_images):
raise ValueError(
'Validation size should be between 0 and {}. Received: {}.'
.format(len(train_images), validation_size))
validation_images = train_images[:validation_size]
validation_labels = train_labels[:validation_size]
train_images = train_images[validation_size:]
train_labels = train_labels[validation_size:]
options = dict(dtype=dtype, reshape=reshape, seed=seed)
train = DataSet(train_images, train_labels, **options)
validation = DataSet(validation_images, validation_labels, **options)
test = DataSet(test_images, test_labels, **options)
return base.Datasets(train=train, validation=validation, test=test)
def load_mnist(train_dir='MNIST-data'):
return read_data_sets(train_dir)
#TensorFlow作者版权所有2016。版权所有。
#
#根据Apache许可证2.0版（以下简称“许可证”）获得许可；
#除非遵守许可证，否则不得使用此文件。
#您可以通过以下方式获得许可证副本：
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#除非适用法律要求或书面同意，软件
#根据许可证进行的分发是按“原样”进行分发的，
#无任何明示或暗示的保证或条件。
#请参阅许可证以了解管理权限和权限的特定语言
#许可证下的限制。
# ==============================================================================
“”“用于下载和读取MNIST数据的函数。”“”
从未来导入绝对导入
来自未来进口部
来自未来导入打印功能
导入gzip
进口numpy
from six.moves导入xrange#pylint:disable=重新定义的内置
从tensorflow.contrib.learn.python.learn.datasets导入库
从tensorflow.python.framework导入数据类型
从tensorflow.python.framework导入随机种子
#CVDF反射镜http://yann.lecun.com/exdb/mnist/
来源(URL)https://storage.googleapis.com/cvdf-datasets/mnist/'
def_read32（ByTestStream）：
dt=numpy.dtype（numpy.uint32）.newbyteorder（“>”）
返回numpy.frombuffer（bytestream.read（4），dtype=dt）[0]
def提取图像（f，通道=1）：
“”“将图像提取到4D uint8 numpy数组中[索引，y，x，深度]。
Args：
f：可以传递到gzip读取器的文件对象。
返回：
数据：4D uint8 numpy数组[索引，y，x，深度]。
提出：
ValueError：如果ByTestStream不是从2051开始的。
"""
打印（'提取'，f.name）
使用gzip.gzip文件（fileobj=f）作为ByTestStream：
magic=_read32（bytestream）
如果有魔法！=2051:
raise VALUERROR（'MNIST映像文件中无效的幻数%d:%s'%
（魔术，f.名字）
num_images=_read32（bytestream）
行=_read32（bytestream）
cols=_read32（bytestream）
buf=bytestream.read（行*cols*num_图像*通道）
data=numpy.frombuffer（buf，dtype=numpy.uint8）
数据=数据。重塑（图像、行、列、通道的数量）
返回数据
def densite_到one_hot（标签密集，num类）：
“”“将类标签从标量转换为一个热向量。”“”
num\u labels=标签密度。形状[0]
索引偏移=numpy.arange（num\u标签）*num\u类
labels\u one\u hot=numpy.zero（（num\u标签，num\u类））
labels\u one\u hot.flat[index\u offset+labels\u densite.ravel（）]=1
返回标签\u一个\u热
def extract_标签（f，one_hot=False，num_classes=10）：
“”“将标签提取到1D uint8 numpy数组[索引]中。”。
Args：
f：可以传递到gzip读取器的文件对象。
one_hot：对结果进行一次热编码。
num_classes：一个热编码的类数。
返回：
标签：一个1D uint8 numpy数组。
提出：
ValueError：如果bystream不是从2049开始的。
"""
打印（'提取'，f.name）
使用gzip.gzip文件（fileobj=f）作为ByTestStream：
magic=_read32（bytestream）
如果有魔法！=2049:
raise VALUERROR（'MNIST标签文件中的无效幻数%d:%s'%
（魔术，f.名字）
num\u items=\u read32（bytestream）
buf=bytestream.read（num\u项）
labels=numpy.frombuffer（buf，dtype=numpy.uint8）
如果其中一个热：
返回稠密的\u到\u一个\u热（标签、num\u类）
退货标签
类数据集（对象）：
定义初始化（自我，
图像，
标签，
假数据=假，
一热=假，
dtype=dtypes.float32，
重塑=真，
种子=无）：
“”“构造数据集。
仅当伪数据为真时才使用一个热参数。`dtype`可以是
`uint8`将输入保留为“[0，255]”，或'float32`重新缩放到
`[0，1]`。Seed arg提供了方便的确定性测试。
"""
种子1，种子2=随机种子。获取种子（种子）
#如果未设置op级别种子，则使用返回的任何图形级别种子
numpy.random.seed（如果seed不是其他seed，则seed 1为seed 2）
dtype=dtypes.as\u dtype（dtype）.base\u dtype
如果数据类型不在（dtypes.uint8，dtypes.float32）：
raise TypeError（'无效的图像数据类型%r，应为uint8或float32'%
数据类型）
如果数据是假的：
self.\u num\u示例=10000
self.one_hot=one_hot
其他：
断言图像。形状[0]==标签。形状[0](
'images.shape:%s labels.shape:%s'（images.shape，labels.shape））
self.\u num\u examples=images.shape[0]
#从[num示例、行、列、深度]转换形状
#至[num示例，行*列]（假设深度==1）
如果重塑：
#最后一批
for i in range(10001):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob:0.5})
size_batch=50
for i in range(10001):
batch_xs, batch_ys = mnist.train.next_batch(size_batch)
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob:0.5})