有没有办法让tensorflow tf.Print输出出现在Jupyter Notebook输出中-Java 学习之路

我在Jupyter笔记本中使用tf.Print操作 . 它可以根据需要工作，但只会将输出打印到控制台，而无需在笔记本中打印 . 有没有办法解决这个问题？

一个例子如下（在笔记本中）：

导入张量流为tf

a = tf.constant（1.0）

a = tf.Print（a，[a]，'hi'）

sess = tf.Session（）

a.eval（会话= SESS）

该代码将在控制台中打印“hi [1]”，但笔记本中没有任何内容 .

5 回答

Update Feb 3, 2017 我把它包装成了memory_util包 . 用法示例

# 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())

** 老东西**

您可以从IPython核心重用FD redirector . （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()

回复于 2024-04-23T18:07:08+08:00

我遇到了同样的问题并通过在我的笔记本中使用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()

仅供参考，在我的自定义函数中使用 Logger 而不是调用“print”为我创造了奇迹，因为stdout通常由jupyter缓冲而未显示在“Loss is Nan”之前的错误 - 这是使用该函数的重点在我的情况下，首先 .

回复于 2024-04-23T18:07:08+08:00

您可以检查启动 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()

在终端，我可以看到：

2018-01-02 23:38:07.691808: I tensorflow/core/kernels/logging_ops.cc:79] 1 + 2 = [3]

回复于 2024-04-23T18:07:08+08:00

3

一个简单的方法，在常规python中尝试过，但还没有jupyter . os.dup2(sys.stdout.fileno(), 1) os.dup2(sys.stdout.fileno(), 2)

解释如下：In python, how to capture the stdout from a c++ shared library to a variable

回复于 2024-04-23T18:07:08+08:00

我遇到的问题是，为什么使用 sess.run(opt) 或 opt.eval() 的选项对我来说不是解决方案的原因 . 最好的方法是使用 tf.Print() 并将日志记录重定向到外部文件 . 我使用临时文件执行此操作，我将其转移到这样的常规文件：

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做到：

a = tf.constant(1)
a = tf.Print(a, [a], message="a: ")

回复于 2024-04-23T18:07:08+08:00

有没有办法让tensorflow tf.Print输出出现在Jupyter Notebook输出中

5 回答

相关问题