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session.run completed in 0.01 sec with .0.500000 acc session.run completed in 0.02 sec with .0.000000 acc ^CTraceback (most recent call last): File "train.py", line 247, in <module> a,_ = sess.run([train_acc,optimizer], feed_dict) File "/home/yaroslav/.conda/envs/tim-jan17/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 767, in run run_metadata_ptr) File "/home/yaroslav/.con
NOTE: Only file GitHub issues for bugs and feature requests. All other topics will be closed. For general support from the community, see StackOverflow. To make bugs and feature requests more easy to find and organize, we close issues that are deemed out of scope for GitHub Issues and point people to StackOverflow. For bugs or installation issues, please provide the following information. The more
I tried the cnn in the tutorial for MNIST data, but initialize the parameters with stddev=1 (instead of stddev=0.1). Error message: I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 4 I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 4 0 0.098 W tensorflow/core/common_runtime/executor.cc:1027] 0x4472710 C
Hello, @maxcuda has recently got tensorflow running on the tk1 as documented in blogpost http://cudamusing.blogspot.de/2015/11/building-tensorflow-for-jetson-tk1.html but since then been unable to repeatedly build it. I am now trying to get tensorflow running on a tx1 tegra platform and need some support. Much trouble seems to come from Eigen variadic templates and using C++11 initializer lists, b
Try out in mybinder: 3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL. Ipyvolume currently can Do (multi) volume rendering. Create scatter plots (up to ~1 million glyphs). Create quiver plots (like scatter, but with an arrow pointing in a particular direction). Render isosurfaces. Do lasso mouse selections. Render in the Jupyter notebook, or create a standalone h
Please let us know which model this issue is about (specify the top-level directory) models/tutorials/image/cifar10/cifar10_input.py:87 File "/mnt/st12tb/models/tutorials/image/cifar10/cifar10_input.py", line 87, in read_cifar10 tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32) models/tutorials/image/cifar10/cifar10_input.py:93 File "/mnt/st12tb/models/tutorials/image/cifar10/cifar10_i
Major Features and Improvements XLA (experimental): initial release of XLA, a domain-specific compiler for TensorFlow graphs, that targets CPUs and GPUs. TensorFlow Debugger (tfdbg): command-line interface and API. New python 3 docker images added. Made pip packages pypi compliant. TensorFlow can now be installed by pip install tensorflow command. Several python API calls have been changed to rese
package main import ( "archive/zip" "bufio" "flag" "fmt" "io" "log" "net/http" "os" "path/filepath" tf "github.com/tensorflow/tensorflow/tensorflow/go" "github.com/tensorflow/tensorflow/tensorflow/go/op" ) func main() { // An example for using the TensorFlow Go API for image recognition // using a pre-trained inception model (http://arxiv.org/abs/1512.00567). // // Sample usage: <program> -dir=/tm
Here is the traceback: W tensorflow/core/framework/op_kernel.cc:975] Not found: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for model_epoch0_128_0.100_[200].ckpt-577569 W tensorflow/core/framework/op_kernel.cc:975] Not found: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for model_epoch0_128_0.100_[200].ckpt-577569 W tensorflow/core
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