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Installing TensorFlow on Windows This guide explains how to install TensorFlow on Windows. Although these instructions might also work on other Windows variants, we have only tested (and we only support) these instructions on machines meeting the following requirements: 64-bit, x86 desktops or laptops Windows 7 or later Determine which TensorFlow to install You must choose one of the following typ
I am trying to convert my Keras graph to a TF graph. I managed to run the provided tensorflow_serving examples, but I'm having issues to run my custom model. Here is my code: ` import tensorflow as tf from keras import backend as K from tensorflow.contrib.session_bundle import exporter def export_model_to_tf(model): K.set_learning_phase(0) # all new operations will be in test mode from now on # se
Opening this with reference to #7500. Installed TensorFlow 1.0 with reference to https://www.tensorflow.org/install/install_windows on Windows 10 and hit the same issue discussed in #7500. With applying the solution suggested in that thread, the original issue disappeared but got the new warnings: C:\Users\geldqb>python Python 3.5.3 (v3.5.3:1880cb95a742, Jan 16 2017, 16:02:32) [MSC v.1900 64 bit (
README.md StreetView Tensorflow Recurrent End-to-End Transcription (STREET) Model. A TensorFlow implementation of the STREET model described in the paper: "End-to-End Interpretation of the French Street Name Signs Dataset" Raymond Smith, Chunhui Gu, Dar-Shyang Lee, Huiyi Hu, Ranjith Unnikrishnan, Julian Ibarz, Sacha Arnoud, Sophia Lin. International Workshop on Robust Reading, Amsterdam, 9 October
It looks like from the latest documentation that rnn performs early stopping for dynamic length sequences whereas dynamic_rnn does not? This would seem to be the reverse of the intuition. So it looks like in commit 855d3b5, the definition of dynamic_rnn was changed from: The parameter sequence_length is required and dynamic calculation is automatically performed. to: The parameter sequence_length
README.md tfprof: TensorFlow Profiler and Beyond Features Profile model architectures parameters, tensor shapes, float operations, device placement, etc. Profile model performance execution time, memory consumption Profile multiple steps. Auto detect and advise. (Experimental) Interfaces Python API Command Line Visualization C++ API (Not public, contact us if needed.) Views and Options tfprof prov
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