tensorflow - wafrat

tensorflow

A collection of 6 posts

Using TensorFlow addons on Mac with M1

These days I am building an image classifier using an unbalanced dataset. So instead of Accuracy, I am more interested in viewing Precision and Recall, or the F1 score. The F1 score metric is implemented by https://www.tensorflow.org/addons/api_docs/python/tfa/metrics/F1Score. So I went

Using the Keras Tuner

Recently I read about the Keras Tuner. A hyperparameter tuner in machine learning is a piece of software that will train random combinations of models in order to find the best architecture. See https://www.tensorflow.org/tutorials/keras/keras_tuner. The idea is simple: 1. make a function that

Plotting discrimination thresholds in TensorFlow

scikit-learn can render precision, recall and F1 score depending on thresholds in a chart. I wanted the same to evaluate my binary classifier made with TensorFlow. See how it looks like on scikit-learn. O(data × number of thresholds) Generating thresholds In order to implement it, I had to figure out

Installing TensorFlow 2.5 and Jupyter Lab on Mac with M1

Last month, I finally painstakingly installed TensorFlow 2.4 and Jupyter Lab on my Mac with M1 (see the blog post). It worked nicely: 10 times faster than Colab, but also had a few issues like working only with Python 3.8, having to manually downgrade some packages such as

TensorFlow reference docs

In a previous post, I collected reference documentation for Python at http://blog.wafrat.com/finding-the-python-reference-docs/. Let's do the same for TensorFlow: * https://www.tensorflow.org/api_docs/python/tf/dtypes/DType * https://keras.io/api/preprocessing/image/ * https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit * https:

Use TensorBoard in Colab to track the training of a text classification model

I just read Chapter 7 of Chollet's Deep learning with Python, and it introduces TensorBoard as a way to visualize metrics in real-time during training and embeddings. The goal In order to try it out, I decided to make a text classification model that predicts the category of a news