LENGUAJE DE PROGRAMACION R

https://cran.itam.mx/

por default

C:\Program Files\R\R-3.4.0


LENGUAJE DE PROGRAMACION R

https://cran.r-project.org/mirrors.html

NEWS	R Documentation

• New features in this version

If you want to double-check that the package you have downloaded matches the package distributed by CRAN, you can compare the md5sum of the .exe to the fingerprint on the master server. You will need a version of md5sum for windows: both graphical and command line versions are available.

Frequently asked questions

• Does R run under my version of Windows?
• How do I update packages in my previous version of R?
• Should I run 32-bit or 64-bit R?

Please see the R FAQ for general information about R and the R Windows FAQ for Windows-specific information.

Other builds

• Patches to this release are incorporated in the r-patched snapshot build.
• A build of the development version (which will eventually become the next major release of R) is available in the r-devel snapshot build.
• Previous releases

Note to webmasters: A stable link which will redirect to the current Windows binary release is
<CRAN MIRROR>/bin/windows/base/release.htm.

R News

CHANGES IN R 3.4.0

SIGNIFICANT USER-VISIBLE CHANGES

• (Unix-alike) The default methods for download.file() and url() now choose "libcurl" except for file:// URLs. There will be small changes in the format and wording of messages, including in rare cases if an issue is a warning or an error. For example, when HTTP re-direction occurs, some messages refer to the final URL rather than the specified one.
  Those who use proxies should check that their settings are compatible (see ?download.file: the most commonly used forms work for both "internal" and "libcurl").
• table() has been amended to be more internally consistent and become back compatible to R <= 2.7.2 again. Consequently, table(1:2, exclude = NULL) no longer contains a zero count for <NA>, but useNA = "always" continues to do so.
• summary.default() no longer rounds, but its print method does resulting in less extraneous rounding, notably of numbers in the ten thousands.
• factor(x, exclude = L) behaves more rationally when x or L are character vectors. Further, exclude = <factor> now behaves as documented for long.
• Arithmetic, logic (&, |) and comparison (aka ‘relational’, e.g., <, ==) operations with arrays now behave consistently, notably for arrays of length zero.
  Arithmetic between length-1 arrays and longer non-arrays had silently dropped the array attributes and recycled. This now gives a warning and will signal an error in the future, as it has always for logic and comparison operations in these cases (e.g., compare matrix(1,1) + 2:3 and matrix(1,1) < 2:3).
• The JIT (‘Just In Time’) byte-code compiler is now enabled by default at its level 3. This means functions will be compiled on first or second use and top-level loops will be compiled and then run. (Thanks to Tomas Kalibera for extensive work to make this possible.)
  For now, the compiler will not compile code containing explicit calls to browser(): this is to support single stepping from the browser() call.
  JIT compilation can be disabled for the rest of the session using compiler::enableJIT(0) or by setting environment variable R_ENABLE_JIT to 0.
• xtabs() works more consistently with NAs, also in its result no longer setting them to 0. Further, a new logical option addNA allows to count NAs where appropriate. Additionally, for the case sparse = TRUE, the result's dimnames are identical to the default case's.
• Matrix products now consistently bypass BLAS when the inputs have NaN/Inf values. Performance of the check of inputs has been improved. Performance when BLAS is used is improved for matrix/vector and vector/matrix multiplication (DGEMV is now used instead of DGEMM).
  One can now choose from alternative matrix product implementations via options(matprod = ). The "internal" implementation is not optimized for speed but consistent in precision with other summations in R (using long double accumulators where available). "blas" calls BLAS directly for best speed, but usually with undefined behavior for inputs with NaN/Inf.
• factor() now uses order() to sort its levels, not sort.list(). This makes factor() support custom vector-like objects if methods for the appropriate generics are defined. This change has the side effect of making factor() succeed on empty or length-one non-atomic vector(-like) types (e.g., list), where it failed before.

NEW FEATURES

• User errors such as integrate(f, 0:1, 2) are now caught.
• Add signature argument to debug(), debugonce(), undebug() and isdebugged() for more conveniently debugging S3 and S4 methods. (Based on a patch by Gabe Becker.)
• Add utils::debugcall() and utils::undebugcall() for debugging the function that would be called by evaluating the given expression. When the call is to an S4 generic or standard S3 generic, debugcall() debugs the method that would be dispatched. A number of internal utilities were added to support this, most notably utils::isS3stdGeneric(). (Based on a patch by Gabe Becker.)
• Add utils::strcapture(). Given a character vector and a regular expression containing capture expressions, strcapture() will extract the captured tokens into a tabular data structure, typically a data.frame.
• str() and strOptions() get a new option drop.deparse.attr with improved but changed default behaviour for expressions. For expression objects x, str(x) now may remove extraneous white space and truncate long lines.
• str(<looooooooong_string>) is no longer very slow; inspired by Mikko Korpela's proposal in PR#16527.
• str(x)'s default method is more “accurate” and hence somewhat more generous in displaying character vectors; this will occasionally change R outputs (and need changes to some ‘*.Rout(.save)’ files).
  For a classed integer vector such as x <- xtabs(~ c(1,9,9,9)), str(x) now shows both the class and "int", instead of only the latter.
• isSymmetric(m) is much faster for large asymmetric matrices m via pre-tests and a new option tol1 (with which strict back compatibility is possible but not the default).
• The result of eigen() now is of class "eigen" in the default case when eigenvectors are computed.
• Zero-length date and date-time objects (of classes "POSIX[cl]?t") now print() “recognizably”.
• xy.coords() and xyz.coords() get a new setLab option.
• The method argument of sort.list(), order() and sort.int() gains an "auto" option (the default) which should behave the same as before when method was not supplied.
• stopifnot(E, ..) now reports differences when E is a call to all.equal() and that is not true.
• boxplot(<formula>, *) gain optional arguments drop, sep, and lex.order to pass to split.default() which itself gains an argument lex.order to pass to interaction() for more flexibility.
• The plot() method for ppr() has enhanced default labels (xmin and main).
• sample.int() gains an explicit useHash option (with a back compatible default).
• identical() gains an ignore.srcref option which drops "srcref" and similar attributes when true (as by default).
• diag(x, nrow = n) now preserves typeof(x), also for logical, integer and raw x (and as previously for complex and numeric).
• smooth.spline() now allows direct specification of lambda, gets a hatvalues() method and keeps tol in the result, and optionally parts of the internal matrix computations.
• addNA() is faster now, e.g. when applied twice. (Part of PR#16895.)
• New option rstandard(<lm>, type = "predicted") provides the “PRESS”–related leave-one-out cross-validation errors for linear models.
• After seven years of deprecation, duplicated factor levels now produce a warning when printed and an error in levels<- instead of a warning.
• Invalid factors, e.g., with duplicated levels (invalid but constructable) now give a warning when printed, via new function .valid.factor().
• sessionInfo() has been updated for Apple's change in OS naming as from ‘10.12’ (‘macOS Sierra’ vs ‘OS X El Capitan’).
  Its toLatex() method now includes the running component.
• options(interrupt=) can be used to specify a default action for user interrupts. For now, if this option is not set and the error option is set, then an unhandled user interrupt invokes the error option. (This may be dropped in the future as interrupt conditions are not error conditions.)
• In most cases user interrupt handlers will be called with a "resume" restart available. Handlers can invoke this restart to resume computation. At the browser prompt the r command will invoke a "resume" restart if one is available. Some read operations cannot be resumed properly when interrupted and do not provide a "resume" restart.
• Radix sort is now chosen by method = "auto" for sort.int() for double vectors (and hence used for sort() for unclassed double vectors), excluding ‘long’ vectors.
  sort.int(method = "radix") no longer rounds double vectors.
• The default and data.frame methods for stack() preserve the names of empty elements in the levels of the ind column of the return value. Set the new drop argument to TRUE for the previous behavior.
• Speedup in simplify2array() and hence sapply() and mapply() (for the case of names and common length > 1), thanks to Suharto Anggono's PR#17118.
• table(x, exclude = NULL) now sets useNA = "ifany" (instead of "always"). Together with the bug fixes for this case, this recovers more consistent behaviour compatible to older versions of R. As a consequence, summary() for a logical vector no longer reports (zero) counts for NA when there are no NAs.
• dump.frames() gets a new option include.GlobalEnv which allows to also dump the global environment, thanks to Andreas Kersting's proposal in PR#17116.
• system.time() now uses message() instead of cat() when terminated early, such that suppressMessages() has an effect; suggested by Ben Bolker.
• citation() supports ‘inst/CITATION’ files from package source trees, with lib.loc pointing to the directory containing the package.
• try() gains a new argument outFile with a default that can be modified via options(try.outFile = .), useful notably for Sweave.
• The unexported low-level functions in package parallel for passing serialized R objects to and from forked children now support long vectors on 64-bit platforms. This removes some limits on higher-level functions such as mclapply() (but returning gigabyte results from forked processes via serialization should be avoided if at all possible).
• Connections now print() without error even if invalid, e.g. after having been destroyed.
• apropos() and find(simple.words = FALSE) no longer match object names starting with . which are known to be internal objects (such as .__S3MethodsTable__.).
• Convenience function hasName() has been added; it is intended to replace the common idiom !is.null(x$name) without the usually unintended partial name matching.
• strcapture() no longer fixes column names nor coerces strings to factors (suggested by Bill Dunlap).
• strcapture() returns NA for non-matching values in x (suggested by Bill Dunlap).
• source() gets new optional arguments, notably exprs; this is made use of in the new utility function withAutoprint().
• sys.source() gets a new toplevel.env argument. This argument is useful for frameworks running package tests; contributed by Tomas Kalibera.
• Sys.setFileTime() and file.copy(copy.date = TRUE) will set timestamps with fractions of seconds on platforms/filesystems which support this.
• (Windows only.) file.info() now returns file timestamps including fractions of seconds; it has done so on other platforms since R 2.14.0. (NB: some filesystems do not record modification and access timestamps to sub-second resolution.)
• The license check enabled by options(checkPackageLicense = TRUE) is now done when the package's namespace is first loaded.
• ppr() and supsmu() get an optional trace argument, and ppr(.., sm.method = ..spline) is no longer limited to sample size n <= 2500.
• The POSIXct method for print() gets optional tz and usetz arguments, thanks to a report from Jennifer S. Lyon.
• New function check_packages_in_dir_details() in package tools for analyzing package-check log files to obtain check details.
• Package tools now exports function CRAN_package_db() for obtaining information about current packages in the CRAN package repository, and several functions for obtaining the check status of these packages.
• The (default) Stangle driver Rtangle allows annotate to be a function and gets a new drop.evalFALSE option.
• The default method for quantile(x, prob) should now be monotone in prob, even in border cases, see PR#16672.
• bug.report() now tries to extract an email address from a BugReports field, and if there is none, from a Contacts field.
• The format() and print() methods for object.size() results get new options standard and digits; notably, standard = "IEC" and standard = "SI" allow more standard (but less common) abbreviations than the default ones, e.g. for kilobytes. (From contributions by Henrik Bengtsson.)
• If a reference class has a validity method, validObject will be called automatically from the default initialization method for reference classes.
• tapply() gets new option default = NA allowing to change the previously hardcoded value.
• read.dcf() now consistently interprets any ‘whitespace’ to be stripped to include newlines.
• The maximum number of DLLs that can be loaded into R e.g. via dyn.load() can now be increased by setting the environment variable R_MAX_NUM_DLLS before starting R.
• Assigning to an element of a vector beyond the current length now over-allocates by a small fraction. The new vector is marked internally as growable, and the true length of the new vector is stored in the truelength field. This makes building up a vector result by assigning to the next element beyond the current length more efficient, though pre-allocating is still preferred. The implementation is subject to change and not intended to be used in packages at this time.
• Loading the parallel package namespace no longer sets or changes the .Random.seed, even if R_PARALLEL_PORT is unset.
  NB: This can break reproducibility of output, and did for a CRAN package.
• Methods "wget" and "curl" for download.file() now give an R error rather than a non-zero return value when the external command has a non-zero status.
• Encoding name "utf8" is mapped to "UTF-8". Many implementations of iconv accept "utf8", but not GNU libiconv (including the late 2016 version 1.15).
• sessionInfo() shows the full paths to the library or executable files providing the BLAS/LAPACK implementations currently in use (not available on Windows).
• The binning algorithm used by bandwidth selectors bw.ucv(), bw.bcv() and bw.SJ() switches to a version linear in the input size n for n > nb/2. (The calculations are the same, but for larger n/nb it is worth doing the binning in advance.)
• There is a new option PCRE_study which controls when grep(perl = TRUE) and friends ‘study’ the compiled pattern. Previously this was done for 11 or more input strings: it now defaults to 10 or more (but most examples need many more for the difference from studying to be noticeable).
• grep(perl = TRUE) and friends can now make use of PCRE's Just-In-Time mechanism, for PCRE >= 8.20 on platforms where JIT is supported. It is used by default whenever the pattern is studied (see the previous item). (Based on a patch from Mikko Korpela.)
  This is controlled by a new option PCRE_use_JIT.
  Note that in general this makes little difference to the speed, and may take a little longer: its benefits are most evident on strings of thousands of characters. As a side effect it reduces the chances of C stack overflow in the PCRE library on very long strings (millions of characters, but see next item).
  Warning: segfaults were seen using PCRE with JIT enabled on 64-bit Sparc builds.
• There is a new option PCRE_limit_recursion for grep(perl = TRUE) and friends to set a recursion limit taking into account R's estimate of the remaining C stack space (or 10000 if that is not available). This reduces the chance of C stack overflow, but because it is conservative may report a non-match (with a warning) in examples that matched before. By default it is enabled if any input string has 1000 or more bytes. (PR#16757)
• getGraphicsEvent() now works on X11(type = "cairo") devices. Thanks to Frederick Eaton (for reviving an earlier patch).
• There is a new argument onIdle for getGraphicsEvent(), which allows an R function to be run whenever there are no pending graphics events. This is currently only supported on X11 devices. Thanks to Frederick Eaton.
• The deriv() and similar functions now can compute derivatives of log1p(), sinpi() and similar one-argument functions, thanks to a contribution by Jerry Lewis.
• median() gains a formal ... argument, so methods with extra arguments can be provided.
• strwrap() reduces indent if it is more than half width rather than giving an error. (Suggested by Bill Dunlap.)
• When the condition code in if(.) or while(.) is not of length one, an error instead of a warning may be triggered by setting an environment variable, see the help page.
• Formatting and printing of bibliography entries (bibentry) is more flexible and better documented. Apart from setting options(citation.bibtex.max = 99) you can also use print(<citation>, bibtex=TRUE) (or format(..)) to get the BibTeX entries in the case of more than one entry. This also affects citation(). Contributions to enable style = "html+bibtex" are welcome. 

REDES NEURONALES CREANDO UNA MAQUINA VIRTUAL DE UBUNTU DESDE CERO

MI CURSO DE LA CARRERA, COMO OPTATIVA TENIA QUE PAGAR, Y SERVIR PARA ALGO

CREANDO UNA MAQUINA VIRTUAL DE UBUNTU DESDE CERO

AHORA VAMOS A INSTALAR DOCKER SIGUIENDO LA SIGUIENTE CONFIGURACION

The following information may help to resolve the situation:

The following packages have unmet dependencies:

 docker-engine : Depends: init-system-helpers (>= 1.18~) but 1.14 is to be installed

                 Depends: lsb-base (>= 4.1+Debian11ubuntu7) but 4.1+Debian11ubuntu6 is to be installed

                 Depends: libdevmapper1.02.1 (>= 2:1.02.97) but 2:1.02.77-6ubuntu2 is to be installed

                 Depends: libltdl7 (>= 2.4.6) but 2.4.2-1.7ubuntu1 is to be installed

                 Depends: libsystemd0 but it is not installable

                 Recommends: aufs-tools but it is not going to be installed

                 Recommends: cgroupfs-mount but it is not installable or

                             cgroup-lite but it is not going to be installed

                 Recommends: git

E: Unable to correct problems, you have held broken packages.

roberto@ubuntu:~$ sudo apt-get install -y docker-engine

Reading package lists... Done

Building dependency tree       

Reading state information... Done

Some packages could not be installed. This may mean that you have

requested an impossible situation or if you are using the unstable

distribution that some required packages have not yet been created

or been moved out of Incoming.

The following information may help to resolve the situation:

The following packages have unmet dependencies:

 docker-engine : Depends: init-system-helpers (>= 1.18~) but 1.14 is to be installed

                 Depends: lsb-base (>= 4.1+Debian11ubuntu7) but 4.1+Debian11ubuntu6 is to be installed

                 Depends: libdevmapper1.02.1 (>= 2:1.02.97) but 2:1.02.77-6ubuntu2 is to be installed

                 Depends: libltdl7 (>= 2.4.6) but 2.4.2-1.7ubuntu1 is to be installed

                 Depends: libsystemd0 but it is not installable

                 Recommends: aufs-tools but it is not going to be installed

                 Recommends: cgroupfs-mount but it is not installable or

                             cgroup-lite but it is not going to be installed

                 Recommends: git

E: Unable to correct problems, you have held broken packages.

roberto@ubuntu:~$ 

LAS VERSIONES DE UBUNTU QUE ESTAN AHORA 5-2017 EN LA PAGINA SON MUY INESTABLES, TRATE CON UNA ANTERIOR

How to train your monkey mind

Estoy instalando TensorFlow desde cero

Hola amigos

Estoy instalando TensorFlow desde cero, si gustan pueden seguir estos pasos, y si tienen alguna duda o comentario, me pueden dejar comentarios,

paso 1 , entrar a tensor flow y selecionar la opcion CPU si tu maquina no tiene procesador nVidia, y si tu procesador tiene nvidi es GPU,


Voy a empezar con la instalacion de Tensor FLOW con opcion GPU

https://www.tensorflow.org/install/install_windows

De entrada como requerimiento forzoso tienes que instalar python 3.5.2

Bájalo a tu maquina e instalarlo, es un ejecutable, no tiene mas ciencia

te da la opción de ver la documentación de python, lo cual siempre es de utilidad a la hora de troubleshooting

https://docs.python.org/3.5/tutorial/index.html

Posteriormente, abre una consola de comandos como administrador

y ejecuta el siguiente comando

pip3 install --upgrade tensorflow

el output de linea de comando es el siguiente>


Microsoft Windows [Versión 10.0.14393]
(c) 2016 Microsoft Corporation. Todos los derechos reservados.

C:\WINDOWS\system32>pip3 install --upgrade tensorflow
Collecting tensorflow
  Downloading tensorflow-1.1.0-cp35-cp35m-win_amd64.whl (19.4MB)
    100% |################################| 19.4MB 55kB/s
Collecting six>=1.10.0 (from tensorflow)
  Downloading six-1.10.0-py2.py3-none-any.whl
Collecting wheel>=0.26 (from tensorflow)
  Downloading wheel-0.29.0-py2.py3-none-any.whl (66kB)
    100% |################################| 71kB 2.6MB/s
Collecting protobuf>=3.2.0 (from tensorflow)
  Downloading protobuf-3.3.0.tar.gz (271kB)
    100% |################################| 276kB 1.9MB/s
Collecting numpy>=1.11.0 (from tensorflow)
  Downloading numpy-1.12.1-cp35-none-win_amd64.whl (7.7MB)
    100% |################################| 7.7MB 154kB/s
Collecting werkzeug>=0.11.10 (from tensorflow)
  Downloading Werkzeug-0.12.1-py2.py3-none-any.whl (312kB)
    100% |################################| 317kB 2.1MB/s
Collecting setuptools (from protobuf>=3.2.0->tensorflow)
  Downloading setuptools-35.0.2-py2.py3-none-any.whl (390kB)
    100% |################################| 399kB 1.0MB/s
Collecting packaging>=16.8 (from setuptools->protobuf>=3.2.0->tensorflow)
  Downloading packaging-16.8-py2.py3-none-any.whl
Collecting appdirs>=1.4.0 (from setuptools->protobuf>=3.2.0->tensorflow)
  Downloading appdirs-1.4.3-py2.py3-none-any.whl
Collecting pyparsing (from packaging>=16.8->setuptools->protobuf>=3.2.0->tensorflow)
  Downloading pyparsing-2.2.0-py2.py3-none-any.whl (56kB)
    100% |################################| 61kB 3.7MB/s
Installing collected packages: six, wheel, pyparsing, packaging, appdirs, setuptools, protobuf, numpy, werkzeug, tensorflow
  Found existing installation: setuptools 20.10.1
    Uninstalling setuptools-20.10.1:
      Successfully uninstalled setuptools-20.10.1
  Running setup.py install for protobuf ... done
Successfully installed appdirs-1.4.3 numpy-1.12.1 packaging-16.8 protobuf-3.3.0 pyparsing-2.2.0 setuptools-35.0.2 six-1.10.0 tensorflow-1.1.0 werkzeug-0.12.1 wheel-0.29.0
You are using pip version 8.1.1, however version 9.0.1 is available.
You should consider upgrading via the 'python -m pip install --upgrade pip' command.

C:\WINDOWS\system32>python

Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32

Type "help", "copyright", "credits" or "license" for more information.

>>>

get the following issue

C:\WINDOWS\system32>python
Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    return importlib.import_module(mname)
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 986, in _gcd_import
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 577, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 906, in create_module
  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed
ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    return importlib.import_module('_pywrap_tensorflow_internal')
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: No module named '_pywrap_tensorflow_internal'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\__init__.py", line 24, in <module>
    from tensorflow.python import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\__init__.py", line 51, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 52, in <module>
    raise ImportError(msg)
ImportError: Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    return importlib.import_module(mname)
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 986, in _gcd_import
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 577, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 906, in create_module
  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed
ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    return importlib.import_module('_pywrap_tensorflow_internal')
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: No module named '_pywrap_tensorflow_internal'


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.
>>> import tensorflow as tf
Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    return importlib.import_module(mname)
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 986, in _gcd_import
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 577, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 906, in create_module
  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed
ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    return importlib.import_module('_pywrap_tensorflow_internal')
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: No module named '_pywrap_tensorflow_internal'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\__init__.py", line 24, in <module>
    from tensorflow.python import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\__init__.py", line 51, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 52, in <module>
    raise ImportError(msg)
ImportError: Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    return importlib.import_module(mname)
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 986, in _gcd_import
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 577, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 906, in create_module
  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed
ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    return importlib.import_module('_pywrap_tensorflow_internal')
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: No module named '_pywrap_tensorflow_internal'


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.
>>> import tensorflow as tf
Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    return importlib.import_module(mname)
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 986, in _gcd_import
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 577, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 906, in create_module
  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed
ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    return importlib.import_module('_pywrap_tensorflow_internal')
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: No module named '_pywrap_tensorflow_internal'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\__init__.py", line 24, in <module>
    from tensorflow.python import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\__init__.py", line 51, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 52, in <module>
    raise ImportError(msg)
ImportError: Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    return importlib.import_module(mname)
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 986, in _gcd_import
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 577, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 906, in create_module
  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed
ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    return importlib.import_module('_pywrap_tensorflow_internal')
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: No module named '_pywrap_tensorflow_internal'


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.
>>> echo "Hello" | syntaxnet/demo.sh
  File "<stdin>", line 1
    echo "Hello" | syntaxnet/demo.sh
               ^
SyntaxError: invalid syntax
>>> bazel test
  File "<stdin>", line 1
    bazel test
             ^
SyntaxError: invalid syntax
>>> cd
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'cd' is not defined
>>> import tensorflow as tf
Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    return importlib.import_module(mname)
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 986, in _gcd_import
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 577, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 906, in create_module
  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed
ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    return importlib.import_module('_pywrap_tensorflow_internal')
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: No module named '_pywrap_tensorflow_internal'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\__init__.py", line 24, in <module>
    from tensorflow.python import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\__init__.py", line 51, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 52, in <module>
    raise ImportError(msg)
ImportError: Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper
    return importlib.import_module(mname)
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 986, in _gcd_import
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 577, in module_from_spec
  File "<frozen importlib._bootstrap_external>", line 906, in create_module
  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed
ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper
    return importlib.import_module('_pywrap_tensorflow_internal')
  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
ImportError: No module named '_pywrap_tensorflow_internal'


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.
>>> import tensorflow as tf



solving the issue

http://stackoverflow.com/questions/43942185/failed-to-load-the-native-tensorflow-runtime-python-3-5-2

https://developer.nvidia.com/cuda-downloads

up vote0down votefavorite

I'm trying to install Tensorflow in my PC, i installed Python 3.5.2 64-bit, cuda_8.0.61 for windows 10 and cudnn-8.0-windows10-x64-v6.0 I used "native" pip to install GPU version of Tensorflow, then i open IDLE and testing with "import tensorflow as tf" and i got the error bellow How can i fix this ? Thanks you in adva

up vote1down voteaccepted

I managed to run Tensorflow on Windows following these steps. Hope it helps! It may change depending in which folder you installed Python. Download and install Python 3.5.x Download and install Nvidia CUDA Toolkit Download CuDNN 5.1 Copy CuDNN files to Nvidia CUDA toolkit folder when 2. has completed (usually is located on C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0) copy cudnn\bin\cudnn64_5.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin\ copy cudnn\include\cudnn.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\ copy cudnn\lib\x64\cudnn.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64\ Install Tensorflow via pip command prompt 'pip install --upgrade tensorflow-gpu' 5.1 TensorFlow dependencies make sure that Visual C++ Redistributate 2015 x64 is installed. If not, download it move _curses_curses.cp35-win_amd64.pyd and _curses_panel.cp35-win_amd64.pyd to C:\Users\Username\AppData\Local\Programs\Python\Python35\Lib\site-packages (or you may need to download them as the next packages. just Ctrl+f for 'curses') download and install these packages via pip (move to the directory where they were downloaded, open a command prompt and make 'pip install package_name') numpy+mkl (http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy) download numpy‑1.12.1+mkl‑cp35‑cp35m‑win_amd64.whl pip install numpy‑1.12.1+mkl‑cp35‑cp35m‑win_amd64.whl scipy (http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy) download scipy‑0.19.0‑cp35‑cp35m‑win_amd64.whl pip install scipy‑0.19.0‑cp35‑cp35m‑win_amd64.whl open command prompt and make 'pip install matplotlib'

https://www.youtube.com/watch?v=BtDgICVvkHE


PARA DARLE LA VUELTA AL ERROR, VEMOS LA DOCUMENTACION EN:

Please check the NOTE under Pip Installation on Windows and follow up if you still have any doubts. Thanks

NOTE: TensorFlow requires MSVCP140.DLL, which may not be installed on your system. If, when you import tensorflow as tf, you see an error about No module named "_pywrap_tensorflow" and/or DLL load failed, check whether MSVCP140.DLL is in your %PATH% and, if not, you should install the Visual C++ 2015 redistributable (x64 version).

Both distributions include pip. To install the CPU-only version of TensorFlow, enter the following command at a command prompt:

C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl

To install the GPU version of TensorFlow, enter the following command at a command prompt:

C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-0.12.1-cp35-cp35m-win_amd64.whl

You can now test your installation.

You can also use Virtualenv or Anaconda environments to manage your installation of TensorFlow on Windows.

https://www.microsoft.com/en-us/download/confirmation.aspx?id=53587

Thank you for downloading

• Microsoft Visual C++ 2015 Redistributable Update 3

• If your download does not start after 30 seconds, 
  

  Click here

C:\WINDOWS\system32>cd C:\Users\admin\AppData\Local\Programs\Python\

C:\Users\admin\AppData\Local\Programs\Python>python

Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32

Type "help", "copyright", "credits" or "license" for more information.

>>> import tensorflow as tf

Traceback (most recent call last):

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper

    return importlib.import_module(mname)

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module

    return _bootstrap._gcd_import(name[level:], package, level)

  File "<frozen importlib._bootstrap>", line 986, in _gcd_import

  File "<frozen importlib._bootstrap>", line 969, in _find_and_load

  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked

  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked

  File "<frozen importlib._bootstrap>", line 577, in module_from_spec

  File "<frozen importlib._bootstrap_external>", line 906, in create_module

  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed

ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>

    from tensorflow.python.pywrap_tensorflow_internal import *

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>

    _pywrap_tensorflow_internal = swig_import_helper()

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper

    return importlib.import_module('_pywrap_tensorflow_internal')

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module

    return _bootstrap._gcd_import(name[level:], package, level)

ImportError: No module named '_pywrap_tensorflow_internal'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\__init__.py", line 24, in <module>

    from tensorflow.python import *

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\__init__.py", line 51, in <module>

    from tensorflow.python import pywrap_tensorflow

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 52, in <module>

    raise ImportError(msg)

ImportError: Traceback (most recent call last):

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 18, in swig_import_helper

    return importlib.import_module(mname)

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module

    return _bootstrap._gcd_import(name[level:], package, level)

  File "<frozen importlib._bootstrap>", line 986, in _gcd_import

  File "<frozen importlib._bootstrap>", line 969, in _find_and_load

  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked

  File "<frozen importlib._bootstrap>", line 666, in _load_unlocked

  File "<frozen importlib._bootstrap>", line 577, in module_from_spec

  File "<frozen importlib._bootstrap_external>", line 906, in create_module

  File "<frozen importlib._bootstrap>", line 222, in _call_with_frames_removed

ImportError: DLL load failed: No se puede encontrar el módulo especificado.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 41, in <module>

    from tensorflow.python.pywrap_tensorflow_internal import *

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 21, in <module>

    _pywrap_tensorflow_internal = swig_import_helper()

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 20, in swig_import_helper

    return importlib.import_module('_pywrap_tensorflow_internal')

  File "C:\Users\admin\AppData\Local\Programs\Python\Python35\lib\importlib\__init__.py", line 126, in import_module

    return _bootstrap._gcd_import(name[level:], package, level)

ImportError: No module named '_pywrap_tensorflow_internal'

Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

for some common reasons and solutions.  Include the entire stack trace

above this error message when asking for help.

>>> C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl^Z

  File "<stdin>", line 1

    C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl

     ^

SyntaxError: invalid syntax

>>> ^Z

C:\Users\admin\AppData\Local\Programs\Python>C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl

"C:\" no se reconoce como un comando interno o externo,

programa o archivo por lotes ejecutable.

C:\Users\admin\AppData\Local\Programs\Python>pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl

Collecting tensorflow==0.12.1 from https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl

  Downloading https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl (13.7MB)

    100% |################################| 13.8MB 84kB/s

Requirement already up-to-date: numpy>=1.11.0 in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==0.12.1)

Requirement already up-to-date: wheel>=0.26 in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==0.12.1)

Requirement already up-to-date: protobuf>=3.1.0 in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==0.12.1)

Requirement already up-to-date: six>=1.10.0 in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==0.12.1)

Requirement already up-to-date: setuptools in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from protobuf>=3.1.0->tensorflow==0.12.1)

Requirement already up-to-date: appdirs>=1.4.0 in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.1.0->tensorflow==0.12.1)

Requirement already up-to-date: packaging>=16.8 in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.1.0->tensorflow==0.12.1)

Requirement already up-to-date: pyparsing in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from packaging>=16.8->setuptools->protobuf>=3.1.0->tensorflow==0.12.1)

Installing collected packages: tensorflow

  Found existing installation: tensorflow 1.1.0

    Uninstalling tensorflow-1.1.0:

      Successfully uninstalled tensorflow-1.1.0

Successfully installed tensorflow-0.12.1

You are using pip version 8.1.1, however version 9.0.1 is available.

You should consider upgrading via the 'python -m pip install --upgrade pip' command.

BINGO!!! LA INSTALACION FUE CORRECTA

Requirement already up-to-date: packaging>=16.8 in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.1.0->tensorflow==0.12.1)

Requirement already up-to-date: pyparsing in c:\users\admin\appdata\local\programs\python\python35\lib\site-packages (from packaging>=16.8->setuptools->protobuf>=3.1.0->tensorflow==0.12.1)

Installing collected packages: tensorflow

  Found existing installation: tensorflow 1.1.0

    Uninstalling tensorflow-1.1.0:

      Successfully uninstalled tensorflow-1.1.0

Successfully installed tensorflow-0.12.1

You are using pip version 8.1.1, however version 9.0.1 is available.

You should consider upgrading via the 'python -m pip install --upgrade pip' command.

C:\Users\admin\AppData\Local\Programs\Python>PYTHON

Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32

Type "help", "copyright", "credits" or "license" for more information.

>>> import tensorflow as tf

>>> hello = tf.constant('Hello', TensorFlow!')

  File "<stdin>", line 1

    hello = tf.constant('Hello', TensorFlow!')

                                           ^

SyntaxError: invalid syntax

>>> hello = tf.constant('Hello, TensorFlow!')

>>> sess = tf.Session()

>>> print(sess.run(hello))

b'Hello, TensorFlow!'

>>> a=tf.constant(10)

>>> b=tf.constant(32)

>>> print(sess.run(a+b))

42

>>>

importante!! sobre el directorio 

C:\Users\admin\AppData\Local\Programs\Python>

C:\Users\admin\AppData\Local\Programs\Python>python -m tensorflow.models.image.mnist.convolutional
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data\train-images-idx3-ubyte.gz
Extracting data\train-labels-idx1-ubyte.gz
Extracting data\t10k-images-idx3-ubyte.gz
Extracting data\t10k-labels-idx1-ubyte.gz
Initialized!
Step 0 (epoch 0.00), 4.9 ms
Minibatch loss: 8.334, learning rate: 0.010000
Minibatch error: 85.9%
                                                                                                  Validation error: 84.6%
Step 100 (epoch 0.12), 255.5 ms
Minibatch loss: 3.254, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 7.8%
Step 200 (epoch 0.23), 256.5 ms
Minibatch loss: 3.373, learning rate: 0.010000
Minibatch error: 9.4%
Validation error: 4.3%
Step 300 (epoch 0.35), 260.8 ms
Minibatch loss: 3.150, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 3.1%
Step 400 (epoch 0.47), 263.9 ms
Minibatch loss: 3.192, learning rate: 0.010000
Minibatch error: 4.7%
Validation error: 2.9%
Step 500 (epoch 0.58), 313.4 ms
Minibatch loss: 3.176, learning rate: 0.010000
Minibatch error: 4.7%
Validation error: 2.4%
Step 600 (epoch 0.70), 317.5 ms
Minibatch loss: 3.115, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 2.0%
Step 700 (epoch 0.81), 293.3 ms
Minibatch loss: 2.969, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 2.1%
Step 800 (epoch 0.93), 296.6 ms
Minibatch loss: 3.072, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 2.1%
Step 900 (epoch 1.05), 307.5 ms
Minibatch loss: 2.898, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.5%
Step 1000 (epoch 1.16), 339.0 ms
Minibatch loss: 2.855, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.9%
Step 1100 (epoch 1.28), 315.8 ms
Minibatch loss: 2.824, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.5%
Step 1200 (epoch 1.40), 331.0 ms
Minibatch loss: 2.932, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.5%
Step 1300 (epoch 1.51), 337.1 ms
Minibatch loss: 2.781, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.8%
Step 1400 (epoch 1.63), 308.9 ms
Minibatch loss: 2.827, learning rate: 0.009500
Minibatch error: 3.1%
Validation error: 1.5%
Step 1500 (epoch 1.75), 291.0 ms
Minibatch loss: 2.908, learning rate: 0.009500
Minibatch error: 3.1%
Validation error: 1.5%
Step 1600 (epoch 1.86), 300.9 ms
Minibatch loss: 2.709, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.4%
Step 1700 (epoch 1.98), 317.0 ms
Minibatch loss: 2.653, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.6%
Step 1800 (epoch 2.09), 338.2 ms
Minibatch loss: 2.658, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.3%
Step 1900 (epoch 2.21), 342.0 ms
Minibatch loss: 2.646, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.2%
Step 2000 (epoch 2.33), 337.9 ms
Minibatch loss: 2.625, learning rate: 0.009025
Minibatch error: 3.1%
Validation error: 1.2%
Step 2100 (epoch 2.44), 337.4 ms
Minibatch loss: 2.578, learning rate: 0.009025
Minibatch error: 0.0%
Validation error: 1.1%
Step 2200 (epoch 2.56), 330.8 ms
Minibatch loss: 2.571, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.2%
Step 2300 (epoch 2.68), 326.2 ms
Minibatch loss: 2.573, learning rate: 0.009025
Minibatch error: 3.1%
Validation error: 1.2%
Step 2400 (epoch 2.79), 301.4 ms
Minibatch loss: 2.510, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.3%
Step 2500 (epoch 2.91), 298.8 ms
Minibatch loss: 2.468, learning rate: 0.009025
Minibatch error: 0.0%
Validation error: 1.2%
Step 2600 (epoch 3.03), 298.4 ms
Minibatch loss: 2.469, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.3%
Step 2700 (epoch 3.14), 295.7 ms
Minibatch loss: 2.503, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.2%
Step 2800 (epoch 3.26), 296.6 ms
Minibatch loss: 2.464, learning rate: 0.008574
Minibatch error: 3.1%

Validation error: 1.1%


corrida completa 1
Microsoft Windows [Versión 10.0.14393]
(c) 2016 Microsoft Corporation. Todos los derechos reservados.

C:\WINDOWS\system32>python -m tensorflow.models.image.mnist.convolutional
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data\train-images-idx3-ubyte.gz
Extracting data\train-labels-idx1-ubyte.gz
Extracting data\t10k-images-idx3-ubyte.gz
Extracting data\t10k-labels-idx1-ubyte.gz
Initialized!
Step 0 (epoch 0.00), 4.9 ms
Minibatch loss: 8.334, learning rate: 0.010000
Minibatch error: 85.9%
Validation error: 84.6%
Step 100 (epoch 0.12), 299.2 ms
Minibatch loss: 3.254, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 7.8%
Step 200 (epoch 0.23), 282.3 ms
Minibatch loss: 3.373, learning rate: 0.010000
Minibatch error: 9.4%
Validation error: 4.3%
Step 300 (epoch 0.35), 296.8 ms
Minibatch loss: 3.150, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 3.1%
Step 400 (epoch 0.47), 300.7 ms
Minibatch loss: 3.192, learning rate: 0.010000
Minibatch error: 4.7%
Validation error: 2.9%
Step 500 (epoch 0.58), 286.2 ms
Minibatch loss: 3.176, learning rate: 0.010000
Minibatch error: 4.7%
Validation error: 2.4%
Step 600 (epoch 0.70), 298.7 ms
Minibatch loss: 3.115, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 2.0%
Step 700 (epoch 0.81), 283.7 ms
Minibatch loss: 2.969, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 2.1%
Step 800 (epoch 0.93), 295.5 ms
Minibatch loss: 3.072, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 2.1%
Step 900 (epoch 1.05), 291.2 ms
Minibatch loss: 2.898, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.5%
Step 1000 (epoch 1.16), 287.2 ms
Minibatch loss: 2.855, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.9%
Step 1100 (epoch 1.28), 310.8 ms
Minibatch loss: 2.824, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.5%
Step 1200 (epoch 1.40), 292.1 ms
Minibatch loss: 2.932, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.5%
Step 1300 (epoch 1.51), 292.1 ms
Minibatch loss: 2.781, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.8%
Step 1400 (epoch 1.63), 303.1 ms
Minibatch loss: 2.827, learning rate: 0.009500
Minibatch error: 3.1%
Validation error: 1.5%
Step 1500 (epoch 1.75), 290.5 ms
Minibatch loss: 2.908, learning rate: 0.009500
Minibatch error: 3.1%
Validation error: 1.5%
Step 1600 (epoch 1.86), 300.9 ms
Minibatch loss: 2.709, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.4%
Step 1700 (epoch 1.98), 280.7 ms
Minibatch loss: 2.653, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.6%
Step 1800 (epoch 2.09), 287.0 ms
Minibatch loss: 2.658, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.3%
Step 1900 (epoch 2.21), 303.9 ms
Minibatch loss: 2.646, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.2%
Step 2000 (epoch 2.33), 305.7 ms
Minibatch loss: 2.625, learning rate: 0.009025
Minibatch error: 3.1%
Validation error: 1.2%
Step 2100 (epoch 2.44), 308.7 ms
Minibatch loss: 2.578, learning rate: 0.009025
Minibatch error: 0.0%
Validation error: 1.1%
Step 2200 (epoch 2.56), 308.2 ms
Minibatch loss: 2.571, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.2%
Step 2300 (epoch 2.68), 304.7 ms
Minibatch loss: 2.573, learning rate: 0.009025
Minibatch error: 3.1%
Validation error: 1.2%
Step 2400 (epoch 2.79), 316.3 ms
Minibatch loss: 2.510, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.3%
Step 2500 (epoch 2.91), 296.0 ms
Minibatch loss: 2.468, learning rate: 0.009025
Minibatch error: 0.0%
Validation error: 1.2%
Step 2600 (epoch 3.03), 323.3 ms
Minibatch loss: 2.469, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.3%
Step 2700 (epoch 3.14), 296.2 ms
Minibatch loss: 2.503, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.2%
Step 2800 (epoch 3.26), 301.2 ms
Minibatch loss: 2.464, learning rate: 0.008574
Minibatch error: 3.1%
Validation error: 1.1%
Step 2900 (epoch 3.37), 310.0 ms
Minibatch loss: 2.541, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.0%
Step 3000 (epoch 3.49), 292.1 ms
Minibatch loss: 2.397, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.0%
Step 3100 (epoch 3.61), 308.4 ms
Minibatch loss: 2.388, learning rate: 0.008574
Minibatch error: 3.1%
Validation error: 0.9%
Step 3200 (epoch 3.72), 292.1 ms
Minibatch loss: 2.357, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.2%
Step 3300 (epoch 3.84), 313.6 ms
Minibatch loss: 2.332, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.0%
Step 3400 (epoch 3.96), 307.6 ms
Minibatch loss: 2.292, learning rate: 0.008574
Minibatch error: 0.0%
Validation error: 1.0%
Step 3500 (epoch 4.07), 296.4 ms
Minibatch loss: 2.291, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 1.0%
Step 3600 (epoch 4.19), 313.4 ms
Minibatch loss: 2.254, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 1.0%
Step 3700 (epoch 4.31), 292.9 ms
Minibatch loss: 2.228, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 1.1%
Step 3800 (epoch 4.42), 316.1 ms
Minibatch loss: 2.217, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 0.9%
Step 3900 (epoch 4.54), 303.6 ms
Minibatch loss: 2.245, learning rate: 0.008145
Minibatch error: 1.6%
Validation error: 1.0%
Step 4000 (epoch 4.65), 305.1 ms
Minibatch loss: 2.249, learning rate: 0.008145
Minibatch error: 3.1%
Validation error: 1.0%
Step 4100 (epoch 4.77), 295.9 ms
Minibatch loss: 2.171, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 1.0%
Step 4200 (epoch 4.89), 300.8 ms
Minibatch loss: 2.162, learning rate: 0.008145
Minibatch error: 1.6%
Validation error: 1.1%
Step 4300 (epoch 5.00), 311.6 ms
Minibatch loss: 2.196, learning rate: 0.007738
Minibatch error: 3.1%
Validation error: 1.0%
Step 4400 (epoch 5.12), 300.5 ms
Minibatch loss: 2.120, learning rate: 0.007738
Minibatch error: 0.0%
Validation error: 1.1%
Step 4500 (epoch 5.24), 295.0 ms
Minibatch loss: 2.188, learning rate: 0.007738
Minibatch error: 4.7%
Validation error: 1.0%
Step 4600 (epoch 5.35), 305.2 ms
Minibatch loss: 2.082, learning rate: 0.007738
Minibatch error: 0.0%
Validation error: 1.0%
Step 4700 (epoch 5.47), 297.1 ms
Minibatch loss: 2.069, learning rate: 0.007738
Minibatch error: 0.0%
Validation error: 0.9%
Step 4800 (epoch 5.59), 314.3 ms
Minibatch loss: 2.078, learning rate: 0.007738
Minibatch error: 1.6%
Validation error: 0.9%
Step 4900 (epoch 5.70), 294.8 ms
Minibatch loss: 2.067, learning rate: 0.007738
Minibatch error: 3.1%
Validation error: 0.9%
Step 5000 (epoch 5.82), 301.4 ms
Minibatch loss: 2.096, learning rate: 0.007738
Minibatch error: 3.1%
Validation error: 0.8%
Step 5100 (epoch 5.93), 298.2 ms
Minibatch loss: 2.002, learning rate: 0.007738
Minibatch error: 0.0%
Validation error: 1.0%
Step 5200 (epoch 6.05), 294.8 ms
Minibatch loss: 2.078, learning rate: 0.007351
Minibatch error: 3.1%
Validation error: 0.9%
Step 5300 (epoch 6.17), 305.9 ms
Minibatch loss: 1.972, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.9%
Step 5400 (epoch 6.28), 291.3 ms
Minibatch loss: 1.966, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 1.0%
Step 5500 (epoch 6.40), 309.4 ms
Minibatch loss: 1.985, learning rate: 0.007351
Minibatch error: 3.1%
Validation error: 0.9%
Step 5600 (epoch 6.52), 294.0 ms
Minibatch loss: 1.930, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.9%
Step 5700 (epoch 6.63), 302.0 ms
Minibatch loss: 1.911, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.8%
Step 5800 (epoch 6.75), 298.2 ms
Minibatch loss: 1.900, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.8%
Step 5900 (epoch 6.87), 305.8 ms
Minibatch loss: 1.894, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.9%
Step 6000 (epoch 6.98), 283.5 ms
Minibatch loss: 1.887, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 1.0%
Step 6100 (epoch 7.10), 308.2 ms
Minibatch loss: 1.873, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6200 (epoch 7.21), 291.9 ms
Minibatch loss: 1.843, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6300 (epoch 7.33), 301.5 ms
Minibatch loss: 1.848, learning rate: 0.006983
Minibatch error: 1.6%
Validation error: 0.8%
Step 6400 (epoch 7.45), 308.0 ms
Minibatch loss: 1.827, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6500 (epoch 7.56), 312.2 ms
Minibatch loss: 1.811, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6600 (epoch 7.68), 290.3 ms
Minibatch loss: 1.817, learning rate: 0.006983
Minibatch error: 1.6%
Validation error: 0.9%
Step 6700 (epoch 7.80), 299.2 ms
Minibatch loss: 1.781, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.8%
Step 6800 (epoch 7.91), 283.9 ms
Minibatch loss: 1.771, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6900 (epoch 8.03), 307.7 ms
Minibatch loss: 1.758, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.9%
Step 7000 (epoch 8.15), 281.2 ms
Minibatch loss: 1.752, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.9%
Step 7100 (epoch 8.26), 295.8 ms
Minibatch loss: 1.734, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.8%
Step 7200 (epoch 8.38), 297.6 ms
Minibatch loss: 1.742, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.9%
Step 7300 (epoch 8.49), 298.7 ms
Minibatch loss: 1.775, learning rate: 0.006634
Minibatch error: 3.1%
Validation error: 0.8%
Step 7400 (epoch 8.61), 295.8 ms
Minibatch loss: 1.700, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.7%
Step 7500 (epoch 8.73), 319.3 ms
Minibatch loss: 1.692, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.8%
Step 7600 (epoch 8.84), 304.6 ms
Minibatch loss: 1.763, learning rate: 0.006634
Minibatch error: 1.6%
Validation error: 0.9%
Step 7700 (epoch 8.96), 296.8 ms
Minibatch loss: 1.666, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 1.0%
Step 7800 (epoch 9.08), 305.2 ms
Minibatch loss: 1.659, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Step 7900 (epoch 9.19), 310.1 ms
Minibatch loss: 1.645, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.8%
Step 8000 (epoch 9.31), 297.6 ms
Minibatch loss: 1.649, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.8%
Step 8100 (epoch 9.43), 318.3 ms
Minibatch loss: 1.630, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Step 8200 (epoch 9.54), 284.4 ms
Minibatch loss: 1.622, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Step 8300 (epoch 9.66), 317.0 ms
Minibatch loss: 1.611, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.7%
Step 8400 (epoch 9.77), 294.3 ms
Minibatch loss: 1.595, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.8%
Step 8500 (epoch 9.89), 301.3 ms
Minibatch loss: 1.619, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Test error: 0.8%

FINAL

QUE ES TENSOR FLOW

https://www.youtube.com/watch?v=oZikw5k_2FM

***********************************************************************

2da corrida es Computadora DELL

Microsoft Windows [Versión 10.0.10586]
(c) 2015 Microsoft Corporation. Todos los derechos reservados.

C:\WINDOWS\system32>pip3 install --upgrade tensorflow
Collecting tensorflow
  Downloading tensorflow-1.1.0-cp35-cp35m-win_amd64.whl (19.4MB)
    100% |################################| 19.4MB 50kB/s
Collecting werkzeug>=0.11.10 (from tensorflow)
  Downloading Werkzeug-0.12.1-py2.py3-none-any.whl (312kB)
    100% |################################| 317kB 1.4MB/s
Collecting protobuf>=3.2.0 (from tensorflow)
  Downloading protobuf-3.3.0.tar.gz (271kB)
    100% |################################| 276kB 1.4MB/s
Collecting numpy>=1.11.0 (from tensorflow)
  Downloading numpy-1.12.1-cp35-none-win_amd64.whl (7.7MB)
    100% |################################| 7.7MB 134kB/s
Collecting six>=1.10.0 (from tensorflow)
  Downloading six-1.10.0-py2.py3-none-any.whl
Collecting wheel>=0.26 (from tensorflow)
  Downloading wheel-0.29.0-py2.py3-none-any.whl (66kB)
    100% |################################| 71kB 1.9MB/s
Collecting setuptools (from protobuf>=3.2.0->tensorflow)
  Downloading setuptools-35.0.2-py2.py3-none-any.whl (390kB)
    100% |################################| 399kB 1.3MB/s
Collecting packaging>=16.8 (from setuptools->protobuf>=3.2.0->tensorflow)
  Downloading packaging-16.8-py2.py3-none-any.whl
Collecting appdirs>=1.4.0 (from setuptools->protobuf>=3.2.0->tensorflow)
  Downloading appdirs-1.4.3-py2.py3-none-any.whl
Collecting pyparsing (from packaging>=16.8->setuptools->protobuf>=3.2.0->tensorflow)
  Downloading pyparsing-2.2.0-py2.py3-none-any.whl (56kB)
    100% |################################| 61kB 84kB/s
Installing collected packages: werkzeug, six, pyparsing, packaging, appdirs, setuptools, protobuf, numpy, wheel, tensorflow
  Found existing installation: setuptools 20.10.1
    Uninstalling setuptools-20.10.1:
      Successfully uninstalled setuptools-20.10.1
  Running setup.py install for protobuf ... done
Successfully installed appdirs-1.4.3 numpy-1.12.1 packaging-16.8 protobuf-3.3.0 pyparsing-2.2.0 setuptools-35.0.2 six-1.10.0 tensorflow-1.1.0 werkzeug-0.12.1 wheel-0.29.0
You are using pip version 8.1.1, however version 9.0.1 is available.
You should consider upgrading via the 'python -m pip install --upgrade pip' command.

C:\WINDOWS\system32>python
Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess=tf.Session()
2017-05-13 23:07:07.019186: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-05-13 23:07:07.025610: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-13 23:07:07.032886: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-13 23:07:07.052675: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-13 23:07:07.069714: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-13 23:07:07.086110: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-05-13 23:07:07.101363: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-13 23:07:07.119127: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
>>> print(sess.run(hello))
b'Hello, TensorFlow!'
>>> a=tf.constant(10)
>>> b=tf.constant(32)
>>> print(sess.run(a+b))
42

***************************************************************************

COMPUTADORA DELL

C:\Users\rober\AppData\Local\Programs\Python>python -m tensorflow.models.image.mnist.convolutional
C:\Users\rober\AppData\Local\Programs\Python\Python35\python.exe: Error while finding spec for 'tensorflow.models.image.mnist.convolutional' (ImportError: No module named 'tensorflow.models')

C:\Users\rober\AppData\Local\Programs\Python> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl
Collecting tensorflow==0.12.1 from https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl
  Downloading https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl (13.7MB)
    100% |################################| 13.8MB 85kB/s
Requirement already up-to-date: wheel>=0.26 in c:\users\rober\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==0.12.1)
Requirement already up-to-date: six>=1.10.0 in c:\users\rober\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==0.12.1)
Requirement already up-to-date: numpy>=1.11.0 in c:\users\rober\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==0.12.1)
Requirement already up-to-date: protobuf>=3.1.0 in c:\users\rober\appdata\local\programs\python\python35\lib\site-packages (from tensorflow==0.12.1)
Requirement already up-to-date: setuptools in c:\users\rober\appdata\local\programs\python\python35\lib\site-packages (from protobuf>=3.1.0->tensorflow==0.12.1)
Requirement already up-to-date: packaging>=16.8 in c:\users\rober\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.1.0->tensorflow==0.12.1)
Requirement already up-to-date: appdirs>=1.4.0 in c:\users\rober\appdata\local\programs\python\python35\lib\site-packages (from setuptools->protobuf>=3.1.0->tensorflow==0.12.1)
Requirement already up-to-date: pyparsing in c:\users\rober\appdata\local\programs\python\python35\lib\site-packages (from packaging>=16.8->setuptools->protobuf>=3.1.0->tensorflow==0.12.1)
Installing collected packages: tensorflow
  Found existing installation: tensorflow 1.1.0
    Uninstalling tensorflow-1.1.0:
      Successfully uninstalled tensorflow-1.1.0
Successfully installed tensorflow-0.12.1
You are using pip version 8.1.1, however version 9.0.1 is available.
You should consider upgrading via the 'python -m pip install --upgrade pip' command.

C:\Users\rober\AppData\Local\Programs\Python>pip install --upgrade
You must give at least one requirement to install (see "pip help install")
You are using pip version 8.1.1, however version 9.0.1 is available.
You should consider upgrading via the 'python -m pip install --upgrade pip' command.

C:\Users\rober\AppData\Local\Programs\Python>python
Python 3.5.2 (v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow')
>>> sess = tf.Session()
>>> print(sess.run(hello))
b'Hello, TensorFlow'
>>> a= tf.constant(10)
>>> b=tf.constant(32)
>>> print(sess.run(a+b))
42
>>> python -m tensorflow.models.image.mnist.convolutional
  File "<stdin>", line 1
    python -m tensorflow.models.image.mnist.convolutional
                       ^
SyntaxError: invalid syntax
>>> ^Z


C:\Users\rober\AppData\Local\Programs\Python>python -m tensorflow.models.image.mnist.convolutional
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data\train-images-idx3-ubyte.gz
Extracting data\train-labels-idx1-ubyte.gz
Extracting data\t10k-images-idx3-ubyte.gz
Extracting data\t10k-labels-idx1-ubyte.gz
Initialized!
Step 0 (epoch 0.00), 3.6 ms
Minibatch loss: 8.334, learning rate: 0.010000
Minibatch error: 85.9%
Validation error: 84.6%
Step 100 (epoch 0.12), 151.8 ms
Minibatch loss: 3.254, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 7.8%
Step 200 (epoch 0.23), 151.0 ms
Minibatch loss: 3.373, learning rate: 0.010000
Minibatch error: 9.4%
Validation error: 4.3%
Step 300 (epoch 0.35), 151.1 ms
Minibatch loss: 3.150, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 3.1%
Step 400 (epoch 0.47), 150.1 ms
Minibatch loss: 3.192, learning rate: 0.010000
Minibatch error: 4.7%
Validation error: 2.9%
Step 500 (epoch 0.58), 151.4 ms
Minibatch loss: 3.176, learning rate: 0.010000
Minibatch error: 4.7%
Validation error: 2.4%
Step 600 (epoch 0.70), 151.4 ms
Minibatch loss: 3.115, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 2.0%
Step 700 (epoch 0.81), 150.5 ms
Minibatch loss: 2.969, learning rate: 0.010000
Minibatch error: 3.1%
Validation error: 2.1%
Step 800 (epoch 0.93), 149.0 ms
Minibatch loss: 3.072, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 2.1%
Step 900 (epoch 1.05), 149.3 ms
Minibatch loss: 2.898, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.5%
Step 1000 (epoch 1.16), 150.6 ms
Minibatch loss: 2.855, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.9%
Step 1100 (epoch 1.28), 150.8 ms
Minibatch loss: 2.824, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.5%
Step 1200 (epoch 1.40), 150.2 ms
Minibatch loss: 2.932, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.5%
Step 1300 (epoch 1.51), 150.6 ms
Minibatch loss: 2.781, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.8%
Step 1400 (epoch 1.63), 150.3 ms
Minibatch loss: 2.827, learning rate: 0.009500
Minibatch error: 3.1%
Validation error: 1.5%
Step 1500 (epoch 1.75), 150.4 ms
Minibatch loss: 2.908, learning rate: 0.009500
Minibatch error: 3.1%
Validation error: 1.5%
Step 1600 (epoch 1.86), 150.0 ms
Minibatch loss: 2.709, learning rate: 0.009500
Minibatch error: 1.6%
Validation error: 1.4%
Step 1700 (epoch 1.98), 149.4 ms
Minibatch loss: 2.653, learning rate: 0.009500
Minibatch error: 0.0%
Validation error: 1.6%
Step 1800 (epoch 2.09), 149.5 ms
Minibatch loss: 2.658, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.3%
Step 1900 (epoch 2.21), 153.4 ms
Minibatch loss: 2.646, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.2%
Step 2000 (epoch 2.33), 150.1 ms
Minibatch loss: 2.625, learning rate: 0.009025
Minibatch error: 3.1%
Validation error: 1.2%
Step 2100 (epoch 2.44), 150.3 ms
Minibatch loss: 2.578, learning rate: 0.009025
Minibatch error: 0.0%
Validation error: 1.1%
Step 2200 (epoch 2.56), 149.3 ms
Minibatch loss: 2.571, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.2%
Step 2300 (epoch 2.68), 150.7 ms
Minibatch loss: 2.573, learning rate: 0.009025
Minibatch error: 3.1%
Validation error: 1.2%
Step 2400 (epoch 2.79), 150.6 ms
Minibatch loss: 2.510, learning rate: 0.009025
Minibatch error: 1.6%
Validation error: 1.3%
Step 2500 (epoch 2.91), 152.3 ms
Minibatch loss: 2.468, learning rate: 0.009025
Minibatch error: 0.0%
Validation error: 1.2%
Step 2600 (epoch 3.03), 149.9 ms
Minibatch loss: 2.469, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.3%
Step 2700 (epoch 3.14), 152.1 ms
Minibatch loss: 2.503, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.2%
Step 2800 (epoch 3.26), 156.8 ms
Minibatch loss: 2.464, learning rate: 0.008574
Minibatch error: 3.1%
Validation error: 1.1%
Step 2900 (epoch 3.37), 150.6 ms
Minibatch loss: 2.541, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.0%
Step 3000 (epoch 3.49), 149.0 ms
Minibatch loss: 2.397, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.0%
Step 3100 (epoch 3.61), 150.5 ms
Minibatch loss: 2.388, learning rate: 0.008574
Minibatch error: 3.1%
Validation error: 0.9%
Step 3200 (epoch 3.72), 149.4 ms
Minibatch loss: 2.357, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.2%
Step 3300 (epoch 3.84), 152.4 ms
Minibatch loss: 2.332, learning rate: 0.008574
Minibatch error: 1.6%
Validation error: 1.0%
Step 3400 (epoch 3.96), 149.4 ms
Minibatch loss: 2.292, learning rate: 0.008574
Minibatch error: 0.0%
Validation error: 1.0%
Step 3500 (epoch 4.07), 149.0 ms
Minibatch loss: 2.291, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 1.0%
Step 3600 (epoch 4.19), 150.7 ms
Minibatch loss: 2.254, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 1.0%
Step 3700 (epoch 4.31), 148.9 ms
Minibatch loss: 2.228, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 1.1%
Step 3800 (epoch 4.42), 152.5 ms
Minibatch loss: 2.217, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 0.9%
Step 3900 (epoch 4.54), 150.0 ms
Minibatch loss: 2.245, learning rate: 0.008145
Minibatch error: 1.6%
Validation error: 1.0%
Step 4000 (epoch 4.65), 149.7 ms
Minibatch loss: 2.249, learning rate: 0.008145
Minibatch error: 3.1%
Validation error: 1.0%
Step 4100 (epoch 4.77), 150.9 ms
Minibatch loss: 2.171, learning rate: 0.008145
Minibatch error: 0.0%
Validation error: 1.0%
Step 4200 (epoch 4.89), 151.0 ms
Minibatch loss: 2.162, learning rate: 0.008145
Minibatch error: 1.6%
Validation error: 1.1%
Step 4300 (epoch 5.00), 151.3 ms
Minibatch loss: 2.196, learning rate: 0.007738
Minibatch error: 3.1%
Validation error: 1.0%
Step 4400 (epoch 5.12), 150.6 ms
Minibatch loss: 2.120, learning rate: 0.007738
Minibatch error: 0.0%
Validation error: 1.1%
Step 4500 (epoch 5.24), 150.4 ms
Minibatch loss: 2.188, learning rate: 0.007738
Minibatch error: 4.7%
Validation error: 1.0%
Step 4600 (epoch 5.35), 150.9 ms
Minibatch loss: 2.082, learning rate: 0.007738
Minibatch error: 0.0%
Validation error: 1.0%
Step 4700 (epoch 5.47), 150.0 ms
Minibatch loss: 2.069, learning rate: 0.007738
Minibatch error: 0.0%
Validation error: 0.9%
Step 4800 (epoch 5.59), 149.5 ms
Minibatch loss: 2.078, learning rate: 0.007738
Minibatch error: 1.6%
Validation error: 0.9%
Step 4900 (epoch 5.70), 151.1 ms
Minibatch loss: 2.067, learning rate: 0.007738
Minibatch error: 3.1%
Validation error: 0.9%
Step 5000 (epoch 5.82), 149.0 ms
Minibatch loss: 2.096, learning rate: 0.007738
Minibatch error: 3.1%
Validation error: 0.8%
Step 5100 (epoch 5.93), 149.9 ms
Minibatch loss: 2.002, learning rate: 0.007738
Minibatch error: 0.0%
Validation error: 1.0%
Step 5200 (epoch 6.05), 149.1 ms
Minibatch loss: 2.078, learning rate: 0.007351
Minibatch error: 3.1%
Validation error: 0.9%
Step 5300 (epoch 6.17), 150.1 ms
Minibatch loss: 1.972, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.9%
Step 5400 (epoch 6.28), 149.4 ms
Minibatch loss: 1.966, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 1.0%
Step 5500 (epoch 6.40), 149.6 ms
Minibatch loss: 1.985, learning rate: 0.007351
Minibatch error: 3.1%
Validation error: 0.9%
Step 5600 (epoch 6.52), 150.5 ms
Minibatch loss: 1.930, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.9%
Step 5700 (epoch 6.63), 155.3 ms
Minibatch loss: 1.911, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.8%
Step 5800 (epoch 6.75), 149.9 ms
Minibatch loss: 1.900, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.8%
Step 5900 (epoch 6.87), 150.9 ms
Minibatch loss: 1.894, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 0.9%
Step 6000 (epoch 6.98), 151.2 ms
Minibatch loss: 1.887, learning rate: 0.007351
Minibatch error: 0.0%
Validation error: 1.0%
Step 6100 (epoch 7.10), 150.6 ms
Minibatch loss: 1.873, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6200 (epoch 7.21), 150.2 ms
Minibatch loss: 1.843, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6300 (epoch 7.33), 150.7 ms
Minibatch loss: 1.848, learning rate: 0.006983
Minibatch error: 1.6%
Validation error: 0.8%
Step 6400 (epoch 7.45), 149.6 ms
Minibatch loss: 1.827, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6500 (epoch 7.56), 151.4 ms
Minibatch loss: 1.811, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6600 (epoch 7.68), 149.2 ms
Minibatch loss: 1.817, learning rate: 0.006983
Minibatch error: 1.6%
Validation error: 0.9%
Step 6700 (epoch 7.80), 149.7 ms
Minibatch loss: 1.781, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.8%
Step 6800 (epoch 7.91), 154.0 ms
Minibatch loss: 1.771, learning rate: 0.006983
Minibatch error: 0.0%
Validation error: 0.9%
Step 6900 (epoch 8.03), 154.3 ms
Minibatch loss: 1.758, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.9%
Step 7000 (epoch 8.15), 150.1 ms
Minibatch loss: 1.752, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.9%
Step 7100 (epoch 8.26), 150.8 ms
Minibatch loss: 1.734, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.8%
Step 7200 (epoch 8.38), 150.2 ms
Minibatch loss: 1.742, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.9%
Step 7300 (epoch 8.49), 150.1 ms
Minibatch loss: 1.775, learning rate: 0.006634
Minibatch error: 3.1%
Validation error: 0.8%
Step 7400 (epoch 8.61), 150.6 ms
Minibatch loss: 1.700, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.7%
Step 7500 (epoch 8.73), 150.1 ms
Minibatch loss: 1.692, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 0.8%
Step 7600 (epoch 8.84), 149.8 ms
Minibatch loss: 1.763, learning rate: 0.006634
Minibatch error: 1.6%
Validation error: 0.9%
Step 7700 (epoch 8.96), 150.3 ms
Minibatch loss: 1.666, learning rate: 0.006634
Minibatch error: 0.0%
Validation error: 1.0%
Step 7800 (epoch 9.08), 149.3 ms
Minibatch loss: 1.659, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Step 7900 (epoch 9.19), 149.5 ms
Minibatch loss: 1.645, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.8%
Step 8000 (epoch 9.31), 149.5 ms
Minibatch loss: 1.649, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.8%
Step 8100 (epoch 9.43), 151.1 ms
Minibatch loss: 1.630, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Step 8200 (epoch 9.54), 148.9 ms
Minibatch loss: 1.622, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Step 8300 (epoch 9.66), 149.0 ms
Minibatch loss: 1.611, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.7%
Step 8400 (epoch 9.77), 150.7 ms
Minibatch loss: 1.595, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.8%
Step 8500 (epoch 9.89), 149.7 ms
Minibatch loss: 1.619, learning rate: 0.006302
Minibatch error: 0.0%
Validation error: 0.9%
Test error: 0.8%

C:\Users\rober\AppData\Local\Programs\Python>