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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
README.md Tensorflow Object Detection API Creating accurate machine learning models capable of localizing and identifying multiple objects in a single image remains a core challenge in computer vision. The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. At Google we’ve certainly found
MobileNet_v1 MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices with TensorFlow Mobile . MobileNets trade off bet
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