Category: TensorFlow

  • Installing TensorFlow on Pop!_OS using Tensorman

    Installing TensorFlow on Pop!_OS using Tensorman

    Pop!_OS allows easy installation and management of Tensorflow using ‘tensorman’. Installing TensorFlow on Pop!_OS using Tensorman First, make sure you have all the updates installed: sudo apt update sudo apt full-upgrade Then, install the tensorman package: sudo apt install tensorman  In order to get Nvidia CUDA support, install the nvidia docker package: sudo apt install…

  • Using model.fit() instead of fit_generator() with Data Generators – TF.Keras

    If you have been using data generators in Keras, such as ImageDataGenerator for augment and load the input data, then you would be familiar with the using the *_generator() methods (fit_generator(), evaluate_generator(), etc.) to pass the generators when trainning the model.  But recently, if you have switched to TensorFlow 2.1 or later (and tf.keras), you might…

  • Fixing the KeyError: ‘acc’ and KeyError: ‘val_acc’ Errors in Keras 2.3.x

    Have you been using the ‘History’ object returned by the fit() functions of Keras to graph or visualize the training history of your models? And have you been getting a ‘KeyError’ type error such as the following since recent Keras upgrade and wondering why? Traceback (most recent call last): File “lenet_mnist_keras.py”, line 163, in <module>…

  • How to Install TensorFlow 2.0 on Anaconda (with CUDA support)

    TensorFlow 2.0 has been released for a few months now. This latest version comes with many new features and improvements, such as eager execution, multi-GPU support, tighter Keras integration, and new deployment options such as TensorFlow Serving. So, it’s time we all switched to TensorFlow 2.0. The Anaconda-native TensorFlow 2.0 packages are now available in…

  • TensorFlow 2.0 Released!

    After months in the Alpha state, Google has now released the final stable version of TensorFlow 2.0. TensorFlow 2.0 aims at providing a easy to use yet flexible and powerful machine learning platform. The new version also hopes to simplify deployment of TF models to any platform by standardizing the model formats. You will be…

  • Installing the New Anaconda Native TensorFlow Package

    For a while now, the most reliable two ways to get TensorFlow installed is to either use the pip package, or compile from source. Compiling TensorFlow from source takes hours, and still prone to errors (see “Failed Attempts at Building TensorFlow GPU from Source“). While the pip package is relatively easier, getting the GPU version…

  • Failed Attempts at Building TensorFlow GPU from Source

    For the last 3 weeks, I’ve been trying to build TensorFlow from source. I wanted to get TensorFlow GPU version working on Windows with CUDA 9.2 and cuDNN 7.1. Since the pre-built wheels only work with CUDA 9.0, the only way we can get it working with 9.2 is to build it ourselves from source.…

  • TensorFlow Lite Developer Preview Announced

    TensorFlow yesterday (14th Nov) announced the developer preview of TensorFlow Lite, a lightweight solution of TensorFlow for mobile and embedded devices, targeted for low-latency inference of on-device machine learning models. TensorFlow Lite Logo TensorFlow Lite is an evolution of TensorFlow Mobile, and designed to be lightweight, cross-platform (Android and iOS for a start), and fast.…

  • What is the image_data_format parameter in Keras, and why is it important

    We’ve talked about the image_dim_ordering parameter in Keras and why is it important. But since from Keras v2 changed the name of the parameter, I thought of bringing this up again. As you know, Keras  is a higher-level neural networks library for Python, which is capable of running on top of TensorFlow, CNTK (Microsoft Cognitive…