我想利用张量流来实现完全卷积网络.有一个功能
tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding, name),
可以用来进行双线性上采样.但是,我很困惑如何使用它？输入是具有单个通道的图像,输出也是具有单个通道的图像,其大小是输入的两倍.我尝试使用如下函数,但得到了一个IndexError: list index out of range:
with tf.name_scope('deconv') as scope:
deconv = tf.nn.conv2d_transpose(conv6, [3, 3, 1, 1],
[1, 26, 20, 1], 2, padding='SAME', name=None)
我使用输入管道方法将数据提供给图形,并tf.train.shuffle_batch实现生成批处理数据.然而,随着训练的进行,张量流变得越来越慢,以后的迭代.我很困惑导致它的根本原因是什么？非常感谢!我的代码片段是:
def main(argv=None):
# define network parameters
# weights
# bias
# define graph
# graph network
# define loss and optimization method
# data = inputpipeline('*')
# loss
# optimizer
# Initializaing the variables
init = tf.initialize_all_variables()
# 'Saver' op to save and restore all the variables
saver = tf.train.Saver()
# Running session
print "Starting session... "
with tf.Session() as sess:
# initialize the variables
sess.run(init)
# initialize the queue threads to start to shovel data
coord …我想设计一个基于预训练网络和tensorflow构建的网络,例如以Reset50为例. ry发布了一个模型,然而,我不知道如何用它来用他们的检查点构建我的模型？resnet的定义可以在resnet.py中找到.谁能帮我？非常感谢你!
def inference(x, is_training,
num_classes=1000,
num_blocks=[3, 4, 6, 3], # defaults to 50-layer network
use_bias=False, # defaults to using batch norm
bottleneck=True):
c = Config()
c['bottleneck'] = bottleneck
c['is_training'] = tf.convert_to_tensor(is_training,
dtype='bool',
name='is_training')
c['ksize'] = 3
c['stride'] = 1
c['use_bias'] = use_bias
c['fc_units_out'] = num_classes
c['num_blocks'] = num_blocks
c['stack_stride'] = 2
with tf.variable_scope('scale1'):
c['conv_filters_out'] = 64
c['ksize'] = 7
c['stride'] = 2
x = conv(x, c)
x = bn(x, c)
x = activation(x)
with tf.variable_scope('scale2'):
x = _max_pool(x, …我已经安装了TensorFlow.当我下载MNISTdataset时,存在一个错误.谁能告诉我什么是错的？非常感谢!错误详情如下:
Python 2.7.9 (default, Apr 2 2015, 15:33:21)
[GCC 4.9.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import input_data
>>> mnist = input_data.read_data_sets("MNIST_data/", False, False)
Extracting MNIST_data/train-images-idx3-ubyte.gz
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "input_data.py", line 162, in read_data_sets
local_file = maybe_download(TRAIN_LABELS, train_dir)
File "input_data.py", line 22, in maybe_download
filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
File "/usr/lib/python2.7/urllib.py", line 98, in urlretrieve
return opener.retrieve(url, filename, reporthook, data)
File "/usr/lib/python2.7/urllib.py", line …