我在Jupyter笔记本中使用tf.Print操作。它按要求工作,但只会将输出打印到控制台,而不是在笔记本中打印。有没有办法解决这个问题?
例如,在笔记本中可以尝试以下示例:
import tensorflow as tf
a = tf.constant(1.0)
a = tf.Print(a, [a], 'hi')
sess = tf.Session()
a.eval(session=sess)
那段代码将在控制台中打印'hi[1]',但是在笔记本中不会有任何输出。
我在Jupyter笔记本中使用tf.Print操作。它按要求工作,但只会将输出打印到控制台,而不是在笔记本中打印。有没有办法解决这个问题?
例如,在笔记本中可以尝试以下示例:
import tensorflow as tf
a = tf.constant(1.0)
a = tf.Print(a, [a], 'hi')
sess = tf.Session()
a.eval(session=sess)
那段代码将在控制台中打印'hi[1]',但是在笔记本中不会有任何输出。
更新于2017年2月3日 我已将此内容整合到memory_util包中。使用示例:
# install memory util
import urllib.request
response = urllib.request.urlopen("https://raw.githubusercontent.com/yaroslavvb/memory_util/master/memory_util.py")
open("memory_util.py", "wb").write(response.read())
import memory_util
sess = tf.Session()
a = tf.random_uniform((1000,))
b = tf.random_uniform((1000,))
c = a + b
with memory_util.capture_stderr() as stderr:
sess.run(c.op)
print(stderr.getvalue())
**旧的东西**
您可以从IPython核心中重用FD redirector。(来自Mark Sandler的想法)
import os
import sys
STDOUT = 1
STDERR = 2
class FDRedirector(object):
""" Class to redirect output (stdout or stderr) at the OS level using
file descriptors.
"""
def __init__(self, fd=STDOUT):
""" fd is the file descriptor of the outpout you want to capture.
It can be STDOUT or STERR.
"""
self.fd = fd
self.started = False
self.piper = None
self.pipew = None
def start(self):
""" Setup the redirection.
"""
if not self.started:
self.oldhandle = os.dup(self.fd)
self.piper, self.pipew = os.pipe()
os.dup2(self.pipew, self.fd)
os.close(self.pipew)
self.started = True
def flush(self):
""" Flush the captured output, similar to the flush method of any
stream.
"""
if self.fd == STDOUT:
sys.stdout.flush()
elif self.fd == STDERR:
sys.stderr.flush()
def stop(self):
""" Unset the redirection and return the captured output.
"""
if self.started:
self.flush()
os.dup2(self.oldhandle, self.fd)
os.close(self.oldhandle)
f = os.fdopen(self.piper, 'r')
output = f.read()
f.close()
self.started = False
return output
else:
return ''
def getvalue(self):
""" Return the output captured since the last getvalue, or the
start of the redirection.
"""
output = self.stop()
self.start()
return output
import tensorflow as tf
x = tf.constant([1,2,3])
a=tf.Print(x, [x])
redirect=FDRedirector(STDERR)
sess = tf.InteractiveSession()
redirect.start();
a.eval();
print "Result"
print redirect.stop()
我遇到了同样的问题,并通过在笔记本中使用类似于这样的函数来解决它:
def tf_print(tensor, transform=None):
# Insert a custom python operation into the graph that does nothing but print a tensors value
def print_tensor(x):
# x is typically a numpy array here so you could do anything you want with it,
# but adding a transformation of some kind usually makes the output more digestible
print(x if transform is None else transform(x))
return x
log_op = tf.py_func(print_tensor, [tensor], [tensor.dtype])[0]
with tf.control_dependencies([log_op]):
res = tf.identity(tensor)
# Return the given tensor
return res
# Now define a tensor and use the tf_print function much like the tf.identity function
tensor = tf_print(tf.random_normal([100, 100]), transform=lambda x: [np.min(x), np.max(x)])
# This will print the transformed version of the tensors actual value
# (which was summarized to just the min and max for brevity)
sess = tf.InteractiveSession()
sess.run([tensor])
sess.close()
有一个提示,对于我的自定义函数,使用日志记录器而不是调用“print”对我来说非常有效,因为标准输出通常会被Jupyter缓冲并且在出现“Loss is Nan”等错误之前不会显示 - 这正是我使用该函数的初衷。
您可以检查启动了 jupyter notebook
的终端以查看消息。
import tensorflow as tf
tf.InteractiveSession()
a = tf.constant(1)
b = tf.constant(2)
opt = a + b
opt = tf.Print(opt, [opt], message="1 + 2 = ")
opt.eval()
2018-01-02 23:38:07.691808: I tensorflow/core/kernels/logging_ops.cc:79] 1 + 2 = [3]
这是一种简单的方法,在常规的Python中尝试过,但还未在Jupyter中尝试过。
os.dup2(sys.stdout.fileno(), 1)
os.dup2(sys.stdout.fileno(), 2)
sess.run(opt)
或opt.eval()
选项对我来说不是解决方案。最好的方法是使用tf.Print()
并将日志重定向到外部文件。我使用了一个临时文件,然后将其转移到常规文件中,如下所示:STDERR=2
import os
import sys
import tempfile
class captured:
def __init__(self, fd=STDERR):
self.fd = fd
self.prevfd = None
def __enter__(self):
t = tempfile.NamedTemporaryFile()
self.prevfd = os.dup(self.fd)
os.dup2(t.fileno(), self.fd)
return t
def __exit__(self, exc_type, exc_value, traceback):
os.dup2(self.prevfd, self.fd)
with captured(fd=STDERR) as tmp:
...
classifier.evaluate(input_fn=input_fn, steps=100)
with open('log.txt', 'w') as f:
print(open(tmp.name).read(), file=f)
然后在我的评估中,我执行:
a = tf.constant(1)
a = tf.Print(a, [a], message="a: ")